Beyond organized communities on Slack or Discord, GitHub serves as the primary armory and laboratory for the AI security community. It’s where theoretical attacks are codified into functional tools, where defensive frameworks are stress-tested, and where the most current research often surfaces first in the form of public repositories. For an AI red teamer, proficiency in navigating and leveraging GitHub is not optional; it is fundamental to maintaining a state-of-the-art toolkit and methodology.
This section catalogs key organizations and standalone projects that provide the building blocks for modern AI red team operations. Think of these not just as code repositories, but as active ecosystems of research, discussion (via Issues and Pull Requests), and practical application.
Foundational Frameworks & Toolkits
These are comprehensive projects designed to support a wide range of adversarial machine learning tasks. They provide libraries, abstractions, and command-line interfaces to streamline both attack execution and defensive evaluation.
| Project | Description | Red Team Application |
|---|---|---|
| Counterfit | A command-line tool and automation framework from Microsoft for assessing the security of AI systems. It provides a generic interface for attacking various models across different data types. | Excellent for standardizing and automating initial vulnerability assessments against a target API. Its modular design allows you to quickly test a battery of known attacks against a new model endpoint. |
| Adversarial Robustness Toolbox (ART) | An extensive Python library for machine learning security, supporting attacks (evasion, poisoning, extraction, inference) and defenses for multiple frameworks like TensorFlow and PyTorch. | Use ART when you need to craft specific, low-level attacks or experiment with novel adversarial techniques. It’s a powerful workshop for building custom exploits against white-box or gray-box models. |
| Garak | An LLM vulnerability scanner from Hugging Face. It uses a plugin-based architecture to probe for a wide range of failure modes, including prompt injection, PII leakage, and jailbreaking. | Garak is your go-to tool for automated, broad-spectrum reconnaissance of an LLM. Run it early in an engagement to identify the most promising avenues for deeper, manual exploitation. |
Specialized Attack & Auditing Tools
These projects are more narrowly focused, often implementing a specific research paper or targeting a particular vulnerability class like prompt injection or model redlining.
| Project | Description | Red Team Application |
|---|---|---|
| llm-attacks | Implementation of the attack from the paper “Universal and Transferable Adversarial Attacks on Aligned Language Models.” It generates suffixes that can jailbreak a wide variety of models. | This is a prime resource for developing potent and reusable jailbreak payloads. Instead of relying on community lists, you can generate novel attack strings tailored to your target’s potential weaknesses. |
| promptmap | A tool from the AI Village for mapping the attack surface of systems that use LLMs. It helps visualize and test for prompt injection vulnerabilities in complex prompt chains. | Essential for engagements involving LLM-powered agents or applications with Retrieval-Augmented Generation (RAG). Use it to discover and exploit hidden prompts and system instructions. |
| Rebuff | A self-hardening prompt injection detection framework. It uses a “canary word” technique to detect and mitigate prompt injection attacks. | While a defensive tool, you should study Rebuff’s mechanisms to learn how to bypass them. Understanding the defense is the first step to crafting an attack that circumvents it. |
Knowledge & Payload Repositories
This category includes curated lists, collections of research papers, and databases of prompts or payloads. They are invaluable for research, learning, and sourcing attack vectors.
| Project | Description | Red Team Application |
|---|---|---|
| Awesome AI Security | A comprehensive, curated list of resources related to AI security, including tools, articles, papers, and courses. | Your first stop for discovery. When tasked with assessing a new type of model or attack surface, check this list to find the latest community tools and research on the topic. |
| Jailbreak Chat | A community-driven collection of jailbreak prompts for various LLMs, categorized and ranked by effectiveness. | A rich source of payloads for initial testing. Analyze the patterns in successful prompts to understand the underlying logic of bypasses, then create your own sophisticated variants. |
| BastionLab | A framework for data science with privacy guarantees, using techniques like differential privacy and secure enclaves. | Study this repository to understand the state-of-the-art in privacy-preserving ML. As a red teamer, your goal might be to find flaws in these implementations or test the limits of their privacy claims. |
Engaging with Projects
Effective use of these resources goes beyond simple consumption. To stay at the cutting edge, you should actively engage with the projects that are most relevant to your work. This means cloning, experimenting, and understanding their inner workings.
Most projects follow a standard installation pattern. You’ll typically clone the repository and then install its dependencies using a package manager like pip.
# 1. Clone the repository from GitHub git clone https://github.com/huggingface/garak.git # 2. Navigate into the newly created directory cd garak # 3. Install the required Python packages # Always check for a requirements.txt, setup.py, or pyproject.toml file pip install .
Beyond installation, make it a habit to:
- Watch key repositories to get notified of new releases and updates.
- Read the Issues tab to understand the tool’s limitations and see what problems other users are facing. This is often a source of ideas for new attack vectors.
- Examine Pull Requests to see how the tool is evolving and what new features or attacks are being added.
By treating GitHub as an active intelligence source rather than a static library, you ensure your red teaming toolkit and knowledge base remain current and effective.