AnyDesk Hacked: Popular Remote Desktop Software Mandates Password Reset

Airgorah - A WiFi Auditing Software That Can Perform Deauth Attacks And Passwords Cracking

Airgorah is a WiFi auditing software that can discover the clients connected to an access point, perform deauthentication attacks against specific clients or all the clients connected to it, capture WPA handshakes, and crack the password of the access point.

It is written in Rust and uses GTK4 for the graphical part. The software is mainly based on aircrack-ng tools suite.

⭐ Don't forget to put a star if you like the project!

Legal

Requirements

This software only works on linux and requires root privileges to run.

You will also need a wireless network card that supports monitor mode and packet injection.

Installation

The installation instructions are available here.

Usage

The documentation about the usage of the application is available here.

License

This project is released under MIT license.

Contributing

If you have any question about the usage of the application, do not hesitate to open a discussion

If you want to report a bug or provide a feature, do not hesitate to open an issue or submit a pull request

WebSecProbe - Web Security Assessment Tool, Bypass 403

A cutting-edge utility designed exclusively for web security aficionados, penetration testers, and system administrators. WebSecProbe is your advanced toolkit for conducting intricate web security assessments with precision and depth. This robust tool streamlines the intricate process of scrutinizing web servers and applications, allowing you to delve into the technical nuances of web security and fortify your digital assets effectively.

WebSecProbe is designed to perform a series of HTTP requests to a target URL with various payloads in order to test for potential security vulnerabilities or misconfigurations. Here's a brief overview of what the code does:

• It takes user input for the target URL and the path.
• It defines a list of payloads that represent different HTTP request variations, such as URL-encoded characters, special headers, and different HTTP methods.
• It iterates through each payload and constructs a full URL by appending the payload to the target URL.
• For each constructed URL, it sends an HTTP GET request using the requests library, and it captures the response status code and content length.
• It prints the constructed URL, status code, and content length for each request, effectively showing the results of each variation's response from the target server.
• After testing all payloads, it queries the Wayback Machine (a web archive) to check if there are any archived snapshots of the target URL/path. If available, it prints the closest archived snapshot's information.

Does This Tool Bypass 403 ?

It doesn't directly attempt to bypass a 403 Forbidden status code. The code's purpose is more about testing the behavior of the server when different requests are made, including requests with various payloads, headers, and URL variations. While some of the payloads and headers in the code might be used in certain scenarios to test for potential security misconfigurations or weaknesses, it doesn't guarantee that it will bypass a 403 Forbidden status code.

In summary, this code is a tool for exploring and analyzing a web server's responses to different requests, but whether or not it can bypass a 403 Forbidden status code depends on the specific configuration and security measures implemented by the target server.

pip install WebSecProbe

WebSecProbe <URL> <Path>

Example:

WebSecProbe https://example.com admin-login

from WebSecProbe.main import WebSecProbe

if __name__ == "__main__":
    url = 'https://example.com'  # Replace with your target URL
    path = 'admin-login'  # Replace with your desired path

    probe = WebSecProbe(url, path)
    probe.run()

PentestPad: Platform for Pentest Teams

DC-Sonar - Analyzing AD Domains For Security Risks Related To User Accounts

DC Sonar Community

Repositories

The project consists of repositories:

• dc-sonar-frontend
• dc-sonar-user-layer
• dc-sonar-workers-layer
• ntlm-scrutinizer

Disclaimer

It's only for education purposes.

Avoid using it on the production Active Directory (AD) domain.

Neither contributor incur any responsibility for any using it.

Social media

Check out our Red Team community Telegram channel

Description

Architecture

For the visual descriptions, open the diagram files using the diagrams.net tool.

The app consists of:

• The dc-sonar-frontend is the fronted part of the user web interface bases on:
  • Angular
  • Angular Material
• The dc-sonar-user-layer is the backend part of the web app bases on:
  • Python 3.10
  • Django
  • Django ORM
  • Django REST framework
  • Celery
  • RabbitMQ
  • PostgreSQL
• The dc-sonar-workers-layer is the logic layer that performs and runs analyzing processes which base on:
  • Python 3.10
  • SQLAlchemy
  • Alembic
  • APScheduler
  • RabbitMQ
  • PostgreSQL
• The ntlm-scrutinizer is the NTLM hashes performer with REST API based on:
• Python 3.10
• FastAPI
• hashcat
• impacket

Functionallity

The DC Sonar Community provides functionality for analyzing AD domains for security risks related to accounts:

• Register analyzing AD domain in the app

  

• See the statuses of domain analyzing processes

  

• Dump and brute NTLM hashes from set AD domains to list accounts with weak and vulnerable passwords

  

• Analyze AD domain accounts to list ones with never expire passwords

  

• Analyze AD domain accounts by their NTLM password hashes to determine accounts and domains where passwords repeat

  

Installation

Docker

In progress ...

Manually using dpkg

It is assumed that you have a clean Ubuntu Server 22.04 and account with the username "user".

The app will install to /home/user/dc-sonar.

The next releases maybe will have a more flexible installation.

Download dc_sonar_NNNN.N.NN-N_amd64.tar.gz from the last distributive to the server.

Create a folder for extracting files:

mkdir dc_sonar_NNNN.N.NN-N_amd64

Extract the downloaded archive:

tar -xvf dc_sonar_NNNN.N.NN-N_amd64.tar.gz -C dc_sonar_NNNN.N.NN-N_amd64

Go to the folder with the extracted files:

cd dc_sonar_NNNN.N.NN-N_amd64/

Install PostgreSQL:

sudo bash install_postgresql.sh

Install RabbitMQ:

sudo bash install_rabbitmq.sh

Install dependencies:

sudo bash install_dependencies.sh

It will ask for confirmation of adding the ppa:deadsnakes/ppa repository. Press Enter.

Install dc-sonar itself:

sudo dpkg -i dc_sonar_NNNN.N.NN-N_amd64.deb

It will ask for information for creating a Django admin user. Provide username, mail and password.

It will ask for information for creating a self-signed SSL certificate twice. Provide required information.

Open: https://localhost

Enter Django admin user credentials set during the installation process before.

Style guide

See the information in STYLE_GUIDE.md

Deployment for development

Docker

In progress ...

Manually using Windows host and Ubuntu Server guest

In this case, we will set up the environment for editing code on the Windows host while running Python code on the Ubuntu guest.

Set up the virtual machine

Create a virtual machine with 2 CPU, 2048 MB RAM, 10GB SSD using Ubuntu Server 22.04 iso in VirtualBox.

If Ubuntu installer asks for updating ubuntu installer before VM's installation - agree.

Choose to install OpenSSH Server.

VirtualBox Port Forwarding Rules:

Config Window

Download and install Python 3.10.5.

Create a folder for the DC Sonar project.

Go to the project folder using Git for Windows:

cd '{PATH_TO_FOLDER}'

Make Windows installation steps for dc-sonar-user-layer.

Make Windows installation steps for dc-sonar-workers-layer.

Make Windows installation steps for ntlm-scrutinizer.

Make Windows installation steps for dc-sonar-frontend.

Set shared folders

Make steps from "Open VirtualBox" to "Reboot VM", but add shared folders to VM VirtualBox with "Auto-mount", like in the picture below:

After reboot, run command:

sudo adduser $USER vboxsf

Perform logout and login for the using user account.

In /home/user directory, you can use mounted folders:

ls -l

Output:
total 12
drwxrwx--- 1 root vboxsf 4096 Jul 19 13:53 dc-sonar-user-layer
drwxrwx--- 1 root vboxsf 4096 Jul 19 10:11 dc-sonar-workers-layer
drwxrwx--- 1 root vboxsf 4096 Jul 19 14:25 ntlm-scrutinizer

Config Ubuntu Server

Config PostgreSQL

Install PostgreSQL on Ubuntu 20.04:

sudo apt update
sudo apt install postgresql postgresql-contrib
sudo systemctl start postgresql.service

Create the admin database account:

sudo -u postgres createuser --interactive

Output:
Enter name of role to add: admin
Shall the new role be a superuser? (y/n) y

Create the dc_sonar_workers_layer database account:

sudo -u postgres createuser --interactive

Output:
Enter name of role to add: dc_sonar_workers_layer
Shall the new role be a superuser? (y/n) n
Shall the new role be allowed to create databases? (y/n) n
Shall the new role be allowed to create more new roles? (y/n) n

Create the dc_sonar_user_layer database account:

sudo -u postgres createuser --interactive

Output:
Enter name of role to add: dc_sonar_user_layer
Shall the new role be a superuser? (y/n) n
Shall the new role be allowed to create databases? (y/n) n
Shall the new role be allowed to create more new roles? (y/n) n

Create the back_workers_db database:

sudo -u postgres createdb back_workers_db

Create the web_app_db database:

sudo -u postgres createdb web_app_db

Run the psql:

sudo -u postgres psql

Set a password for the admin account:

ALTER USER admin WITH PASSWORD '{YOUR_PASSWORD}';

Set a password for the dc_sonar_workers_layer account:

ALTER USER dc_sonar_workers_layer WITH PASSWORD '{YOUR_PASSWORD}';

Set a password for the dc_sonar_user_layer account:

ALTER USER dc_sonar_user_layer WITH PASSWORD '{YOUR_PASSWORD}';

Grant CRUD permissions for the dc_sonar_workers_layer account on the back_workers_db database:

\c back_workers_db
GRANT CONNECT ON DATABASE back_workers_db to dc_sonar_workers_layer;
GRANT USAGE ON SCHEMA public to dc_sonar_workers_layer;
GRANT ALL ON ALL TABLES IN SCHEMA public TO dc_sonar_workers_layer;
GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO dc_sonar_workers_layer;
GRANT ALL ON ALL FUNCTIONS IN SCHEMA public TO dc_sonar_workers_layer;

Grant CRUD permissions for the dc_sonar_user_layer account on the web_app_db database:

\c web_app_db
GRANT CONNECT ON DATABASE web_app_db to dc_sonar_user_layer;
GRANT USAGE ON SCHEMA public to dc_sonar_user_layer;
GRANT ALL ON ALL TABLES IN SCHEMA public TO dc_sonar_user_layer;
GRANT ALL ON ALL SEQUENCES IN SCHEMA public TO dc_sonar_user_layer;
GRANT ALL ON ALL FUNCTIONS IN SCHEMA public TO dc_sonar_user_layer;

Exit of the psql:

\q

Open the pg_hba.conf file:

sudo nano /etc/postgresql/12/main/pg_hba.conf

Add the line for the connection to allow the connection from the host machine to PostgreSQL, save changes and close the file:

# IPv4 local connections:
host    all             all             127.0.0.1/32            md5
host    all             admin           0.0.0.0/0               md5

Open the postgresql.conf file:

sudo nano /etc/postgresql/12/main/postgresql.conf

Change specified below params, save changes and close the file:

listen_addresses = 'localhost,10.0.2.15'
shared_buffers = 512MB
work_mem = 5MB
maintenance_work_mem = 100MB
effective_cache_size = 1GB

Restart the PostgreSQL service:

sudo service postgresql restart

Check the PostgreSQL service status:

service postgresql status

Check the log file if it is needed:

tail -f /var/log/postgresql/postgresql-12-main.log

Now you can connect to created databases using admin account and client such as DBeaver from Windows.

Config RabbitMQ

Install RabbitMQ using the script.

Enable the management plugin:

sudo rabbitmq-plugins enable rabbitmq_management

Create the RabbitMQ admin account:

sudo rabbitmqctl add_user admin {YOUR_PASSWORD}

Tag the created user for full management UI and HTTP API access:

sudo rabbitmqctl set_user_tags admin administrator

Open management UI on http://localhost:15672/.

Install Python3.10

Ensure that your system is updated and the required packages installed:

sudo apt update && sudo apt upgrade -y

Install the required dependency for adding custom PPAs:

sudo apt install software-properties-common -y

Then proceed and add the deadsnakes PPA to the APT package manager sources list as below:

sudo add-apt-repository ppa:deadsnakes/ppa

Download Python 3.10:

sudo apt install python3.10=3.10.5-1+focal1

Install the dependencies:

sudo apt install python3.10-dev=3.10.5-1+focal1 libpq-dev=12.11-0ubuntu0.20.04.1 libsasl2-dev libldap2-dev libssl-dev

Install the venv module:

sudo apt-get install python3.10-venv

Check the version of installed python:

python3.10 --version

Output:
Python 3.10.5

Hosts

Add IP addresses of Domain Controllers to /etc/hosts

sudo nano /etc/hosts

Layers

Set venv

We have to create venv on a level above as VM VirtualBox doesn't allow us to make it in shared folders.

Go to the home directory where shared folders located:

cd /home/user

Make deploy steps for dc-sonar-user-layer on Ubuntu.

Make deploy steps for dc-sonar-workers-layer on Ubuntu.

Make deploy steps for ntlm-scrutinizer on Ubuntu.

Config modules

Make config steps for dc-sonar-user-layer on Ubuntu.

Make config steps for dc-sonar-workers-layer on Ubuntu.

Make config steps for ntlm-scrutinizer on Ubuntu.

Run

Make run steps for ntlm-scrutinizer on Ubuntu.

Make run steps for dc-sonar-user-layer on Ubuntu.

Make run steps for dc-sonar-workers-layer on Ubuntu.

Make run steps for dc-sonar-frontend on Windows.

Open https://localhost:8000/admin/ in a browser on the Windows host and agree with the self-signed certificate.

Open https://localhost:4200/ in the browser on the Windows host and login as created Django user.

Aura - Python Source Code Auditing And Static Analysis On A Large Scale

Source code auditing and static code analysis

Aura is a static analysis framework developed as a response to the ever-increasing threat of malicious packages and vulnerable code published on PyPI.

Project goals:

• provide an automated monitoring system over uploaded packages to PyPI, alert on anomalies that can either indicate an ongoing attack or vulnerabilities in the code
• enable an organization to conduct automated security audits of the source code and implement secure coding practices with a focus on auditing 3rd party code such as python package dependencies
• allow researches to scan code repositories on a large scale, create datasets and perform analysis to further advance research in the area of vulnerable and malicious code dependencies

Feature list:

• Suitable for analyzing malware with a guarantee of a zero-code execution
• Advanced deobfuscation mechanisms by rewriting the AST tree - constant propagations, code unrolling, and other dirty tricks
• Recursive scanning automatically unpacks archives such as zips, wheels, etc.. and scans the content
• Support scanning also non-python files - plugins can work in a “raw-file” mode such as the built-in Yara integration
• Scan for hardcoded secrets, passwords, and other sensitive information
• Custom diff engine - you can compare changes between different data sources such as typosquatting PyPI packages to what changes were made
• Works for both Python 2.x and Python 3.x source code
• High performance, designed to scan the whole PyPI repository
• Output in numerous formats such as pretty plain text, JSON, SQLite, SARIF, etc…
• Tested on over 4TB of compressed python source code
• Aura is able to report on code behavior such as network communication, file access, or system command execution
• Compute the “Aura score” telling you how trustworthy the source code/input data is
• and much much more…

Didn't find what you are looking for? Aura's architecture is based on a robust plugin system, where you can customize almost anything, ranging from a set of data analyzers, transport protocols to custom out formats.

Installation

# Via pip:
pip install aura-security[full]
# or build from source/git
poetry install --no-dev -E full

Or just use a prebuild docker image sourcecodeai/aura:dev

Running Aura

docker run -ti --rm sourcecodeai/aura:dev scan pypi://requests -v

Aura uses a so-called URIs to identify the protocol and location to scan, if no protocol is used, the scan argument is treated as a path to the file or directory on a local system.

Diff packages:

docker run -ti --rm sourcecodeai/aura:dev diff pypi://requests pypi://requests2

Find most popular typosquatted packages (you need to call aura update to download the dataset first):

aura find-typosquatting --max-distance 2 --limit 10

Why Aura?

While there are other tools with functionality that overlaps with Aura such as Bandit, dlint, semgrep etc. the focus of these alternatives is different which impacts the functionality and how they are being used. These alternatives are mainly intended to be used in a similar way to linters, integrated into IDEs, frequently run during the development which makes it important to minimize false positives and reporting with clear actionable explanations in ideal cases.

Aura on the other hand reports on ** behavior of the code**, anomalies, and vulnerabilities with as much information as possible at the cost of false positive. There are a lot of things reported by aura that are not necessarily actionable by a user but they tell you a lot about the behavior of the code such as doing network communication, accessing sensitive files, or using mechanisms associated with obfuscation indicating a possible malicious code. By collecting this kind of data and aggregating it together, Aura can be compared in functionality to other security systems such as antivirus, IDS, or firewalls that are essentially doing the same analysis but on a different kind of data (network communication, running processes, etc).

Here is a quick overview of differences between Aura and other similar linters and SAST tools:

• input data:

  • Other SAST tools - usually restricted to only python (target) source code and python version under which the tool is installed.
  • Aura can analyze both binary (or non-python code) and python source code as well. Able to analyze a mixture of python code compatible with different python versions (py2k & py3k) using the same Aura installation.

• reporting:

  • Other SAST tools - Aims at integrating well with other systems such as IDEs, CI systems with actionable results while trying to minimize false positives to prevent overwhelming users with too many non-significant alerts.
  • Aura - reports as much information as possible that is not immediately actionable such as behavioral and anomaly analysis. The output format is designed for easy machine processing and aggregation rather than human readable.

• configuration:

  • Other SAST tools - The tools are fine-tuned to the target project by customizing the signatures to target specific technologies used by the target project. The overriding configuration is often possible by inserting comments inside the source code such as # nosec that will suppress the alert at that position
  • Aura - it is expected that there is little to no knowledge in advance about the technologies used by code that is being scanned such as auditing a new python package for approval to be used as a dependency in a project. In most cases, it is not even possible to modify the scanned source code such as using comments to indicate to linter or aura to skip detection at that location because it is scanning a copy of that code that is hosted at some remote location.

Authors & Contributors

• Martin Carnogursky - Initial work and project lead - https://is.muni.cz/person/410345
• Mirza Zulfan - Logo Design - https://github.com/mirzazulfan

Donate

• GitHub Sponsors: https://github.com/sponsors/RootLUG
• Liberapay: https://liberapay.com/SourceCode.AI
• BuyMeACoffee: https://www.buymeacoffee.com/SourceCodeAI
• BTC: 3FVTaLsLwTDinmDjPh3BjS1qv3bYHbkcYc
• XMR: 46xvWZGCexo1NbvjLMMpLB1GhRd819AQr8eFPJT1q6kKMuuDy43JLiESh9XUM3asjk4SVUYqGakFVQZRY1adx8cS6ka4EXr
• ETH/ERC20: 0x708F1A08E3ee4922f037673E720c405518C0Ec85

LICENSE

Aura framework is licensed under the GPL-3.0. Datasets produced from global scans using Aura are released under the CC BY-NC 4.0 license. Use the following citation when using Aura or data produced by Aura in research:

@misc{Carnogursky2019thesis,
AUTHOR = "CARNOGURSKY, Martin",
TITLE = "Attacks on package managers [online]",
YEAR = "2019 [cit. 2020-11-02]",
TYPE = "Bachelor Thesis",
SCHOOL = "Masaryk University, Faculty of Informatics, Brno",
SUPERVISOR = "Vit Bukac",
URL = "Available at WWW <https://is.muni.cz/th/y41ft/>",
}

Packj - Large-Scale Security Analysis Platform To Detect Malicious/Risky Open-Source Packages

Packj (pronounced package) is a command line (CLI) tool to vet open-source software packages for "risky" attributes that make them vulnerable to supply chain attacks. This is the tool behind our large-scale security analysis platform Packj.dev that continuously vets packages and provides free reports.

How to use

Packj accepts two input args:

• name of the registry or package manager, pypi, npm, or rubygems.
• name of the package to be vetted

Packj supports vetting of PyPI, NPM, and RubyGems packages. It performs static code analysis and checks for several metadata attributes such as release timestamps, author email, downloads, dependencies. Packages with expired email domains, large release time gap, sensitive APIs, etc. are flagged as risky for security reasons.

Packj also analyzes public repo code as well as metadata (e.g., stars, forks). By comparing the repo description and package title, you can be sure if the package indeed has been created from the repo to mitigate any starjacking attacks.

Containerized

The best way to use Packj is to run it inside Docker (or Podman) container. You can pull our latest image from DockerHub to get started.

docker pull ossillate/packj:latest

$ docker run --mount type=bind,source=/tmp,target=/tmp ossillate/packj:latest npm browserify
[+] Fetching 'browserify' from npm...OK [ver 17.0.0]
[+] Checking version...ALERT [598 days old]
[+] Checking release history...OK [484 version(s)]
[+] Checking release time gap...OK [68 days since last release]
[+] Checking author...OK [mail@substack.net]
	[+] Checking email/domain validity...ALERT [expired author email domain]
[+] Checking readme...OK [26838 bytes]
[+] Checking homepage...OK [https://github.com/browserify/browserify#readme]
[+] Checking downloads...OK [2.2M weekly]
[+] Checking repo_url URL...OK [https://github.com/browserify/browserify]
	[+] Checking repo data...OK [stars: 14077, forks: 1236]
	[+] Checking repo activity...OK [commits: 2290, contributors: 207, tags: 413]
[+] Checking for CVEs...OK [none found]
[+] Checking dependencies...ALERT [48 found]
[+] Downloading package 'browserify' (ver 17.   0.0) from npm...OK [163.83 KB]
[+] Analyzing code...ALERT [needs 3 perms: process,file,codegen]
[+] Checking files/funcs...OK [429 files (383 .js), 744 funcs, LoC: 9.7K]
=============================================
[+] 5 risk(s) found, package is undesirable!
=> Complete report: /tmp/npm-browserify-17.0.0.json
{
    "undesirable": [
        "old package: 598 days old",
        "invalid or no author email: expired author email domain",
        "generates new code at runtime", 
        "reads files and dirs",
        "forks or exits OS processes",
    ]
}

Specific package versions to be vetted could be specified using ==. Please refer to the example below

$ docker run --mount type=bind,source=/tmp,target=/tmp ossillate/packj:latest pypi requests==2.18.4
[+] Fetching 'requests' from pypi...OK [ver 2.18.4]
[+] Checking version...ALERT [1750 days old]
[+] Checking release history...OK [142 version(s)]
[+] Checking release time gap...OK [14 days since last release]
[+] Checking author...OK [me@kennethreitz.org]
	[+] Checking email/domain validity...OK [me@kennethreitz.org]
[+] Checking readme...OK [49006 bytes]
[+] Checking homepage...OK [http://python-requests.org]
[+] Checking downloads...OK [50M weekly]
[+] Checking repo_url URL...OK [https://github.com/psf/requests]
	[+] Checking repo data...OK [stars: 47547, forks: 8758]
	[+] Checking repo activity...OK [commits: 6112, contributors: 725, tags: 144]
[+] Checking for CVEs...ALERT [2 found]
[+] Checking dependencies...OK [9 direct]
[+] Downloading package 'requests' (ver 2.18.4) from pypi...OK [123.27 KB]
[+   ] Analyzing code...ALERT [needs 4 perms: codegen,process,file,network]
[+] Checking files/funcs...OK [47 files (33 .py), 578 funcs, LoC: 13.9K]
=============================================
[+] 6 risk(s) found, package is undesirable, vulnerable!
{
    "undesirable": [
        "old package: 1744 days old",
        "invalid or no homepage: insecure webpage",
        "generates new code at runtime",
        "fetches data over the network", 
        "reads files and dirs",
    ], 
    "vulnerable": [
        "contains CVE-2018-18074,CVE-2018-18074"
    ]
}
=> Complete report: /tmp/pypi-requests-2.18.4.json
=> View pre-vetted package report at https://packj.dev/package/PyPi/requests/2.18.4

Non-containerized

Alternatively, you can install Python/Ruby dependencies locally and test it.

NOTE

• Packj has only been tested on Linux.
• Requires Python3 and Ruby. API analysis will fail if used with Python2.
• You will have to install Python and Ruby dependencies before using the tool:
  • pip install -r requirements.txt
  • gem install google-protobuf:3.21.2 rubocop:1.31.1

$ python3 main.py npm eslint
[+] Fetching 'eslint' from npm...OK [ver 8.16.0]
[+] Checking version...OK [10 days old]
[+] Checking release history...OK [305 version(s)]
[+] Checking release time gap...OK [15 days since last release]
[+] Checking author...OK [nicholas+npm@nczconsulting.com]
	[+] Checking email/domain validity...OK [nicholas+npm@nczconsulting.com]
[+] Checking readme...OK [18234 bytes]
[+] Checking homepage...OK [https://eslint.org]
[+] Checking downloads...OK [23.8M weekly]
[+] Checking repo_url URL...OK [https://github.com/eslint/eslint]
	[+] Checking repo data...OK [stars: 20669, forks: 3689]
	[+] Checking repo activity...OK [commits: 8447, contributors: 1013, tags: 302]
[+] Checking for CVEs...OK [none found]
[+] Checking dependencies...ALERT [35 found]
[+] Downloading package 'eslint' (ver 8.16.0) from npm...OK [490.14 KB]
[+] Analyzing code...ALERT [needs 2 perms: codegen,file]
[+   ] Checking files/funcs...OK [395 files (390 .js), 1022 funcs, LoC: 76.3K]
=============================================
[+] 2 risk(s) found, package is undesirable!
{
    "undesirable": [
        "generates new code at runtime", 
        "reads files and dirs: ['package/lib/cli-engine/load-rules.js:37', 'package/lib/cli-engine/file-enumerator.js:142']"
    ]
}
=> Complete report: /tmp/npm-eslint-8.16.0.json

How it works

• It first downloads the metadata from the registry using their APIs and analyze it for "risky" attributes.
• To perform API analysis, the package is downloaded from the registry using their APIs into a temp dir. Then, packj performs static code analysis to detect API usage. API analysis is based on MalOSS, a research project from our group at Georgia Tech.
• Vulnerabilities (CVEs) are checked by pulling info from OSV database at OSV
• Python PyPI and NPM package downloads are fetched from pypistats and npmjs
• All risks detected are aggregated and reported

Risky attributes

The design of Packj is guided by our study of 651 malware samples of documented open-source software supply chain attacks. Specifically, we have empirically identified a number of risky code and metadata attributes that make a package vulnerable to supply chain attacks.

For instance, we flag inactive or unmaintained packages that no longer receive security fixes. Inspired by Android app runtime permissions, Packj uses a permission-based security model to offer control and code transparency to developers. Packages that invoke sensitive operating system functionality such as file accesses and remote network communication are flagged as risky as this functionality could leak sensitive data.

Some of the attributes we vet for, include

Full list of the attributes we track can be viewed at threats.csv

These attributes have been identified as risky by several other researchers [1, 2, 3] as well.

How to customize

Packj has been developed with a goal to assist developers in identifying and reviewing potential supply chain risks in packages.

However, since the degree of perceived security risk from an untrusted package depends on the specific security requirements, Packj can be customized according to your threat model. For instance, a package with no 2FA may be perceived to pose greater security risks to some developers, compared to others who may be more willing to use such packages for the functionality offered. Given the volatile nature of the problem, providing customized and granular risk measurement is one of our goals.

Packj can be customized to minimize noise and reduce alert fatigue by simply commenting out unwanted attributes in threats.csv

Malware found

We found over 40 malicious packages on PyPI using this tool. A number of them been taken down. Refer to an example below:

$ python3 main.py pypi krisqian
[+] Fetching 'krisqian' from pypi...OK [ver 0.0.7]
[+] Checking version...OK [256 days old]
[+] Checking release history...OK [7 version(s)]
[+] Checking release time gap...OK [1 days since last release]
[+] Checking author...OK [KrisWuQian@baidu.com]
	[+] Checking email/domain validity...OK [KrisWuQian@baidu.com]
[+] Checking readme...ALERT [no readme]
[+] Checking homepage...OK [https://www.bilibili.com/bangumi/media/md140632]
[+] Checking downloads...OK [13 weekly]
[+] Checking repo_url URL...OK [None]
[+] Checking for CVEs...OK [none found]
[+] Checking dependencies...OK [none found]
[+] Downloading package 'KrisQian' (ver 0.0.7) from pypi...OK [1.94 KB]
[+] Analyzing code...ALERT [needs 3 perms: process,network,file]
[+] Checking files/funcs...OK [9 files (2 .py), 6 funcs, LoC: 184]
=============================================
[+] 6 risk(s) found, package is undes   irable!
{
    "undesirable": [
        "no readme",
        "only 45 weekly downloads",
        "no source repo found", 
        "generates new code at runtime", 
        "fetches data over the network: ['KrisQian-0.0.7/setup.py:40', 'KrisQian-0.0.7/setup.py:50']", 
        "reads files and dirs: ['KrisQian-0.0.7/setup.py:59', 'KrisQian-0.0.7/setup.py:70']"
    ]
}
=> Complete report: pypi-KrisQian-0.0.7.json
=> View pre-vetted package report at https://packj.dev/package/PyPi/KrisQian/0.0.7

Packj flagged KrisQian (v0.0.7) as suspicious due to absence of source repo and use of sensitive APIs (network, code generation) during package installation time (in setup.py). We decided to take a deeper look, and found the package malicious. Please find our detailed analysis at https://packj.dev/malware/krisqian.

More examples of malware we found are listed at https://packj.dev/malware Please reach out to us at oss@ossillate.com for full list.

Resources

To learn more about Packj tool or open-source software supply chain attacks, refer to our

• PyConUS'22 talk and slides.
• BlackHAT Asia'22 Arsenal presentation
• PackagingCon'21 talk and slides
• Academic dissertation on open-source software security and the paper from our group at Georgia Tech that started this research.

Upcoming talks

• BlackHat USA'22 Arsenal talk Detecting typo-squatting, backdoored, abandoned, and other "risky" open-source packages using Packj
• Open Source Summit, Europe'22 talk Scoring dependencies to detect “weak links” in your open-source software supply chain

Feature roadmap

• Add support for other language ecosystems. Rust is a work in progress, and will be available in July '22 (last week).
• Add functionality to detect several other "risky" code as well as metadata attributes.
• Packj currently only performs static code analysis, we are working on adding support for dynamic analysis (WIP, ETA: end of summer)

Team

Packj has been developed by Cybersecurity researchers at Ossillate Inc. and external collaborators to help developers mitigate risks of supply chain attacks when sourcing untrusted third-party open-source software dependencies. We thank our developers and collaborators.

We welcome code contributions. Join our discord community for discussion and feature requests.

FAQ

• What Package Managers (Registries) are supported?

Packj can currently vet NPM, PyPI, and RubyGems packages for "risky" attributes. We are adding support for Rust.

• Does it work on obfuscated calls? For example, a base 64 encrypted string that gets decrypted and then passed to a shell?

This is a very common malicious behavior. Packj detects code obfuscation as well as spawning of shell commands (exec system call). For example, Packj can flag use of getattr() and eval() API as they indicate "runtime code generation"; a developer can go and take a deeper look then. See main.py for details.

• Does this work at the system call level, where it would detect e.g. any attempt to open ~/.aws/credentials, or does it rely on heuristic analysis of the code itself, which will always be able to be "coded around" by the malware authors?

Packj currently uses static code analysis to derive permissions (e.g., file/network accesses). Therefore, it can detect open() calls if used by the malware directly (e.g., not obfuscated in a base64 encoded string). But, Packj can also point out such base64 decode calls. Fortunately, malware has to use these APIs (read, open, decode, eval, etc.) for their functionality -- there's no getting around. Having said that, a sophisticated malware can hide itself better, so dynamic analysis must be performed for completeness. We are incorporating strace-based dynamic analysis (containerized) to collect system calls. See roadmap for details.