AI Adversarial Testing
AI adversarial testing is the practice of deliberately probing AI systems with manipulated inputs, edge cases, and known attack techniques to evaluate their resilience against exploitation before and during production deployment.
What Adversarial Testing Covers
Adversarial testing is a security discipline specific to AI systems. It borrows methodology from traditional penetration testing but targets the AI attack surface rather than infrastructure or application code. Typical coverage includes prompt injection and indirect prompt injection, jailbreak attempts, data poisoning probes, model extraction attempts, and evaluation of guardrails and safety filters.
The MITRE ATLAS framework provides a shared taxonomy for the tactics and techniques adversarial testing exercises, making test coverage measurable and comparable across systems.
Why Enterprises Need Adversarial Testing
Enterprises deploying generative and agentic AI systems have discovered that traditional QA does not surface AI-specific failure modes. A model that passes functional testing can still fall to a prompt injection payload it has never seen. A model that behaves correctly on benchmark data can leak sensitive information when probed adversarially.
How organizations should prioritize AI security risks treats adversarial testing as a required control for any AI system with meaningful business impact.
Adversarial Testing vs. Red Teaming
Adversarial testing and AI red teaming overlap but are not identical. Adversarial testing is often systematic and repeatable, running defined attack payloads against known surfaces. Red teaming is more open-ended, using creative attackers to find the failure modes the systematic tests miss. Mature programs use both.
Related Terms
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