AI Security Testing: Red Teaming & Validation

You cannot secure what you cannot test. OWASP AI Security Testing defines how to validate the controls you've implemented.

Unlike traditional software testing (unit tests), AI testing is probabilistic. A test that passes once might fail the next time.

1. AI Red Teaming

The Concept: A manual or automated adversarial simulation where "attackers" try to break the model.

• Goal: Find jailbreaks, bias, and leakage that automated tools miss.
• Scope: Test the Model (LLM), the Application (RAG), and the System (Plugins/Tools).

Types of Red Teaming:

• Manual: Human experts crafting creative attacks.
• Automated (Agentic): Using an "Attacker LLM" to generate thousands of adversarial prompts (e.g., using Garak or PyRIT).

2. Automated Security Scanners

Just as you use SAST/DAST for code, you need tools for models.

• Vulnerability Scanners: Tools like Garak probe for hallucinations, prompt injection, and toxicity.
• Model Scanners: Tools like ModelScan check model files for malware (pickle attacks).
• Pipeline: Integrate these into your CI/CD. No model goes to production without a clean scan.

3. Continuous Validation

AI models drift. A secure model can become insecure after fine-tuning or even just over time (due to changing context).

• Regression Testing: Re-run your Red Team prompt sets after every model update.
• Production Monitoring: Use "Canary Prompts" in production to detect if the model's safety guardrails are failing in real-time.

CISO Takeaway

Penetration Testing is not enough. AI security requires Continuous Red Teaming. The attack surface (natural language) is infinite; your testing must be automated and ongoing.

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