AI red teaming is not a solo pursuit. The field’s rapid evolution means that today’s novel attack vector is tomorrow’s standard operating procedure. Staying effective requires continuous learning, and the most potent resources for that are the communities and platforms where practitioners share knowledge, tools, and war stories.
While standards and regulations provide the “what,” these communities provide the “how.” They are the living repositories of tactics, techniques, and procedures (TTPs) that define the state of the art. Engaging with them is non-negotiable for any serious AI security professional. This section maps out the essential hubs you need to know.
Core Hubs for AI Security Practitioners
Certain communities have become central to the AI security landscape. They are where new research is debated, tools are released, and hands-on skills are honed. Think of them as your primary intelligence feeds.
AI Village (DEF CON)
If there is a ground zero for the public, hands-on AI hacking community, it is the AI Village. Born out of the DEF CON security conference, it is a vendor-neutral space focused on the practical exploitation of AI systems. It’s less about theoretical papers and more about live demos, capture-the-flag (CTF) competitions, and tool releases. For a red teamer, the AI Village is an essential annual pilgrimage, whether in person or virtually, to see what attackers are actually doing.
MLSecOps Community
Where the AI Village focuses heavily on the “breaking,” the MLSecOps community focuses on the entire secure lifecycle. This community brings together data scientists, security engineers, and operations professionals to address security in the context of Machine Learning Operations (MLOps). Their resources, including a very active Slack channel, are invaluable for understanding how to integrate security into the model development pipeline, from data ingestion to deployment and monitoring.
OWASP AI Projects
The Open Web Application Security Project (OWASP) is a cornerstone of traditional application security, and it has logically extended its work into AI. Their most significant contributions for red teamers are:
- OWASP Top 10 for Large Language Models: A critical document that categorizes the most significant vulnerabilities in LLMs, such as Prompt Injection and Insecure Output Handling. It provides a common language and framework for assessing LLM applications.
- AI Security and Privacy Guide: A broader project that aims to provide guidance on securing the entire AI/ML system, not just the model.
OWASP’s work is crucial for bridging the gap between traditional AppSec and the unique challenges of AI.
Frameworks and Knowledge Bases
Beyond discussion forums, structured knowledge bases are vital for systematic red teaming. They provide the scaffolding for planning engagements and reporting findings.
MITRE ATLAS™
MITRE’s Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS) is a knowledge base of adversarial tactics, techniques, and case studies. Modeled after the highly successful ATT&CK framework for cybersecurity, ATLAS is specifically tailored to the AI/ML lifecycle. It helps you answer questions like:
- What are the known ways an adversary can evade our facial recognition model? (Evasion Attack)
- How could an attacker poison the data we use to retrain our fraud detection system? (Data Poisoning)
- How can an adversary steal our proprietary model architecture? (Model Stealing)
Using ATLAS allows you to structure your red team engagements methodically, ensuring comprehensive coverage of potential threats against the AI system and its supporting infrastructure.
A Comparative Overview of Key Platforms
To help you navigate this ecosystem, the following table summarizes the primary focus and audience for each key resource.
| Platform / Community | Primary Focus | Key Resources | Target Audience |
|---|---|---|---|
| AI Village | Offensive security, hands-on hacking, and exploitation of AI models. | DEF CON talks, CTF competitions, tool releases, workshops. | Security researchers, penetration testers, red teamers. |
| MLSecOps Community | Securing the entire ML lifecycle, from development to operations. | Slack community, best practice guides, webinars. | ML engineers, security engineers, DevOps/MLOps professionals. |
| OWASP AI Projects | Categorizing AI vulnerabilities and providing standardized guidance. | Top 10 for LLMs, AI Security and Privacy Guide. | Application security professionals, developers, auditors. |
| MITRE ATLAS™ | Structured knowledge base of adversary tactics against AI systems. | Technique matrix, case studies, strategic intelligence. | Threat intelligence analysts, red team planners, security architects. |
The Broader Ecosystem: Research and Collaboration
The cutting edge of AI security is constantly being sharpened in academic and open-source communities. While the hubs above are for practitioners, these are where future threats and defenses are born.
The AI Red Teamer’s information ecosystem connects practitioner hubs, research, frameworks, and collaborative platforms.
Academic Conferences & Pre-print Servers
The foundational research on adversarial attacks—from the first gradient-based attacks to complex generative model exploits—originates in academia. Key conferences include security-focused venues (USENIX, IEEE S&P, ACM CCS) and machine learning-focused ones (NeurIPS, ICML, ICLR). For the absolute latest, you must monitor arXiv.org (specifically the cs.CR, cs.LG, and cs.AI categories). While not peer-reviewed, it’s where researchers post their findings first.
Collaborative Platforms (e.g., Hugging Face)
Platforms like Hugging Face are more than just model repositories; they are collaborative ecosystems. Discussions around model cards, security vulnerabilities discovered in popular models, and the development of new safety tools often happen in the open here. Monitoring these platforms provides insight into the practical security challenges developers face with real-world models.
Your role as a red teamer is to synthesize information from all these sources. An academic paper from arXiv might give you the theory for a new attack, a tool from the AI Village might provide the implementation, and a discussion on MLSecOps might tell you how it applies in a production environment. Active participation—not just passive observation—is the key to staying ahead.