Red Teams & Security Researchers

AI Red-Teaming: Test AI Systems Before Attackers Do

Hands-on AI red team training: prompt injection, jailbreaking, model evasion, and adversarial ML, mapped to the OWASP LLM Top 10 and MITRE ATLAS.

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AI red team training teaches security practitioners to attack AI systems the way adversaries do: prompt injection, jailbreaking, model evasion, and data extraction, run as hands-on labs against real targets. GTK Cyber teaches it two ways: a dedicated 2-day AI Red-Teaming course delivered on-site for teams, and adversarial AI labs inside the AI Cyber Bootcamp at Black Hat USA 2026 (August 1-4, Mandalay Bay, Las Vegas).

Every AI System Is an Attack Surface

Organizations are deploying AI rapidly: chatbots with access to internal data, AI agents that take actions, LLM-powered analysis tools embedded in security workflows. Few of them have been tested adversarially.

The attack surfaces are real and exploitable now: prompt injection, jailbreaking, indirect instruction injection, model evasion, data extraction. These aren’t theoretical vulnerabilities. The OWASP Top 10 for LLM Applications catalogs them (LLM01 prompt injection leads the list), and MITRE ATLAS tracks the observed adversary techniques (AML.T0051 prompt injection, AML.T0015 evade ML model). They are being exploited in production systems today.

The security profession is just beginning to develop the methodology to test for them systematically.

What AI Red-Teaming Covers

GTK Cyber’s AI red-teaming training teaches practitioners to assess AI systems across the full threat surface:

LLM and Generative AI

  • Prompt injection, direct and indirect
  • Jailbreaking and safety control bypass
  • System prompt extraction
  • Data leakage from retrieval-augmented systems
  • Multi-turn attack chains

Classical ML and AI Models

  • Adversarial input crafting
  • Model evasion techniques
  • Feature manipulation attacks
  • Robustness evaluation frameworks
  • Data poisoning concepts

Assessment Methodology

  • Threat modeling for AI systems
  • Structured red team frameworks for LLMs, mapped to the OWASP LLM Top 10 and MITRE ATLAS
  • Reporting and communicating AI risk
  • Remediation approaches and their limitations

Taught by Practitioners

GTK Cyber instructors don’t teach these techniques from academic papers. They apply them in real assessments and bring that operational experience into the training environment.

Every lab is hands-on. You test real AI systems, craft real attacks, and build the judgment needed to adapt these techniques to the specific systems you’ll encounter in your work.

Prerequisites

Security practitioners with red team, penetration testing, or adversarial research backgrounds. Basic Python familiarity is helpful. No ML background required.

Looking for the broader picture first? Start with AI cybersecurity training for the full course map, or go straight to the AI Red-Teaming course page for the syllabus.

Relevant Courses

intermediate4 days

AI Cyber Bootcamp

Intensive 4-day bootcamp covering AI, machine learning, and data science for cybersecurity: LLMs, AI red-teaming, threat hunting, and SOC automation.

advanced2 days

AI Red-Teaming

Adversarial testing of AI systems: prompt injection, jailbreaks, robustness and bias evaluation, data exfiltration, and building repeatable red-team frameworks.

Frequently Asked Questions

What is AI red-teaming?
AI red-teaming is the systematic adversarial testing of AI systems to identify vulnerabilities, failure modes, and unexpected behaviors. It applies the red team mindset (find the weaknesses before attackers do) to AI-specific attack surfaces like prompt injection, jailbreaking, model evasion, and data extraction.
Who should take AI red-teaming training?
Security professionals on red teams or penetration testing teams, researchers evaluating AI systems for clients, security engineers responsible for AI applications that handle sensitive data or take consequential actions, and anyone tasked with assessing the security posture of AI systems in their organization.
Do I need a machine learning background?
No. GTK Cyber's AI red-teaming course is designed for security practitioners who understand adversarial thinking but need to apply it to AI systems. We teach the AI fundamentals needed to understand failure modes without requiring prior ML expertise.
What AI systems does the training cover?
The training covers large language models (LLMs) and their applications (chatbots, AI agents, RAG systems), as well as classical ML models used in security tools (anomaly detectors, classifiers, scoring systems). The techniques apply to AI systems built on any major platform.
Is there an AI red-teaming certification?
There is no widely recognized standards body for AI red-teaming certification yet; treat any vendor claiming an industry-standard credential with skepticism. GTK Cyber students receive a certificate of completion, and more importantly leave with working attack notebooks and assessment reports they can show a hiring manager or client. In a field this new, demonstrable lab work carries more weight than a certificate name.
When and where can I take AI red-teaming training?
Two paths. At Black Hat USA 2026 (August 1-4, Mandalay Bay, Las Vegas), the adversarial AI material, including prompt injection, model evasion, and RAG poisoning labs, runs inside the 4-day AI Cyber Bootcamp. The dedicated 2-day AI Red-Teaming course is delivered as a custom on-site or virtual engagement for security teams year-round.

Learn About AI Red-Teaming

Contact us about custom training for your team or upcoming public courses.

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