Summary
In this module, you learned the foundations of AI security testing through the lens of AI red teaming:
- What AI red teaming is: A practice that extends traditional security testing to cover AI-specific attack surfaces, addressing both security vulnerabilities and responsible AI concerns. Unlike traditional testing, AI red teaming must account for probabilistic outputs, include both adversarial and benign personas, and be repeated as models and metaprompts evolve.
- The three categories: Full stack red teaming assesses the entire technology stack. Adversarial machine learning targets the model itself through techniques like evasion and data poisoning. Prompt injection exploits the natural language interface through direct injection, indirect injection, and jailbreaking.
- Planning a red teaming exercise: Effective AI red teaming requires recruiting diverse teams and designing adversarial tests at both the model and application layers. Teams perform iterative testing with and without mitigations, use automated tools to complement manual testing, and report results to stakeholders.
AI security testing is an ongoing practice, not a one-time activity. As models are updated, metaprompts change, and new attack techniques emerge, organizations need to continuously test and validate their AI systems' security posture.
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