What Is AI Red Teaming?
AI red teaming is the systematic practice of testing AI systems for security vulnerabilities by simulating adversary behavior. Just as traditional red teaming tests network defenses, AI red teaming tests how your LLM-powered applications respond to malicious inputs — prompt injection, data exfiltration attempts, jailbreaking, and denial-of-service attacks.
The goal is not to break the system for breaking's sake — it's to find vulnerabilities before attackers do, measure the effectiveness of your security controls, and build an evidence trail that proves to auditors and customers that your AI pipeline is secure.
Why Traditional Security Testing Isn't Enough
New attack surface
LLMs introduce prompt-based attack vectors that sit entirely outside traditional network and application security models. A WAF can't see inside a prompt.
Data at risk
Prompts contain sensitive data — PII, credentials, proprietary code — that flows directly to third-party providers. Traditional pentesting doesn't account for this data egress path.
Tool exploitation
When LLMs are connected to tools (MCP, plugins, APIs), prompt injection becomes remote code execution. Testing must cover the full tool chain, not just the model.
Compliance evidence
SOC 2, HIPAA, and ISO 27001 audits increasingly ask about AI-specific security testing. You need documented test cases, execution logs, and remediation evidence.
Industry Frameworks for AI Red Teaming
Two primary frameworks guide AI security testing today:
Attack Category Taxonomy
Defines the ten most critical vulnerability categories for LLM applications — from prompt injection (LLM01) to model theft (LLM10). Each category includes example attack scenarios, prevention guidance, and references to real-world incidents. This is the standard taxonomy for organizing AI security test cases.
Risk Management & Testing Methodology
The NIST AI Risk Management Framework (AI RMF 1.0, January 2023) provides a structured approach to AI risk — Govern, Map, Measure, Manage. The Measure function specifically covers red teaming, field testing, and independent assessment. NIST AI 600-1 extends this to generative AI applications with specific red teaming considerations for LLMs.
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