Top 5 AI Red-Teaming Training Providers for Security Teams

AI red-teaming is the newest specialty in offensive security. As organizations deploy LLMs in customer-facing products, embed AI agents in internal workflows, and integrate ML into security operations, the attack surface has expanded faster than the training market has caught up.

This ranking covers five providers we recommend security teams consider for AI red-teaming training in 2026. They serve different needs and different skill levels.

1. GTK Cyber

Best for: Security professionals who want structured, hands-on AI red-teaming training from practitioners who actively do this work.

GTK Cyber’s AI Red-Teaming course covers the full progression from LLM attack surface (prompt injection, jailbreaking, indirect injection, RAG poisoning) through adversarial ML (model evasion, data poisoning, robustness evaluation). Every lab is hands-on and uses real tools: Ollama for local model testing, the Adversarial Robustness Toolbox for ML attacks, MITRE ATLAS for technique classification.

The AI Cyber Bootcamp includes AI red-teaming as part of a broader 4-day curriculum that also covers defensive applications. Both courses are available at Black Hat USA and through custom on-site engagements.

Format: Lab-based, practitioner-led, 2-4 days. Website: gtkcyber.com/courses/ai-red-teaming

2. OWASP LLM Top 10 Project (Free Resources)

Best for: Practitioners who want to learn the classification of LLM vulnerabilities and contribute to the open-source standard.

OWASP’s Top 10 for LLM Applications is the closest thing to a canonical vulnerability classification for LLM-powered systems. It is free, actively maintained, and includes worked examples for each category (prompt injection, sensitive information disclosure, insecure output handling, model denial of service, and so on).

The project runs working groups and accepts contributions from practitioners. It is an excellent reference but not a training program. Teams still need applied training to translate the taxonomy into operational testing capabilities.

Format: Documentation, working groups, community-driven. Website: owasp.org

3. Lakera (Gandalf and Training Games)

Best for: Building intuition about prompt injection through gamified challenges.

Gandalf is a free, browser-based game where players attempt to extract a password from an increasingly hardened LLM. It is one of the most effective ways to build intuition about how prompt injection actually works, and which techniques transfer across different defensive setups.

Lakera is primarily an AI security product company, not a training provider. Their educational tools are excellent for warming up before structured training or for onboarding non-security staff (engineering, product) to the problem space. They do not offer formal curriculum, certifications, or structured progression.

Format: Free browser games. Website: lakera.ai

4. DEF CON AI Village

Best for: Researchers and practitioners who want to engage with cutting-edge AI red-teaming research and live challenges.

The AI Village at DEF CON is the community hub for AI security research. It hosts talks, CTF-style challenges, and hands-on labs during the conference. Content ranges from practitioner-accessible (LLM jailbreaking demos) to deeply technical (adversarial ML research).

This is not a training program you can send a team to for reliable skill-building. It is a community and a conference track. For practitioners who are already reasonably skilled, the AI Village is one of the best places to accelerate learning and build relationships with the research community.

Format: Conference village, talks, CTFs, hands-on labs (annually at DEF CON, Las Vegas). Website: aivillage.org

5. MITRE ATLAS (Reference Framework)

Best for: Organizations that need a structured taxonomy of adversarial AI techniques for risk assessment and testing.

MITRE ATLAS (Adversarial Threat Landscape for AI Systems) maps adversarial techniques against AI systems the same way ATT&CK maps traditional attack techniques. It is free, actively maintained, and integrates with MITRE’s broader threat modeling ecosystem.

ATLAS is a reference, not a training program. Teams use it to structure their red-teaming methodology, map findings to recognized technique IDs, and communicate risk in a language that security leadership and vendors understand. Pairing ATLAS with applied training (such as GTK Cyber’s) produces the combination of methodology and technique needed for operational AI red-teaming.

Format: Free online framework, documentation, case studies. Website: atlas.mitre.org

How to Build an AI Red-Teaming Capability

The practical progression for a security team:

1. Build intuition: Lakera’s Gandalf and similar gamified tools. 2-4 hours.
2. Learn the taxonomy: Read OWASP LLM Top 10 and MITRE ATLAS. 1-2 days.
3. Formal training: GTK Cyber AI Red-Teaming course or equivalent. 2-4 days.
4. Engage the community: DEF CON AI Village, follow researchers on social media, read papers.
5. Build internal capability: Apply techniques to your own LLM deployments, document findings using ATLAS technique IDs.

Each stage reinforces the others. Teams that skip stages (such as jumping straight to advanced adversarial ML without building LLM attack fundamentals) typically struggle.