Observability & Evaluation · Software component
Adversarial Robustness Evaluator
Software componentObservability & EvaluationObservability & Evaluationarc:AdversarialRobustnessEvaluator
An evaluation component that systematically attacks a model or agent with jailbreaks, prompt injection, role-play, encoded requests, information-extraction and multi-turn manipulation, measuring attack success.
Responsibility. Measures resistance to attempts to circumvent principles.
Also known as: Adversarial testing, Jailbreak testing
Relationships
reads dependency
evaluates assurance
Design guidance
- MUST run regularly after deployment, since attack vectors accumulate as attackers iterate.
- SHOULD expect the agent to reject attacks and explain why, and to answer extraction probes without leaking internal details.
- SHOULD search for inputs on which the reward model gives incorrect scores before they are exploited during optimization.
Quantitative guidance
As stated by the sources; verify before use.
- Adversarial success rate target is 0%; any success requires immediate fixes (Ref9.01).
Classification
- Patterns
- Social-engineering attacksRole-play attacksObfuscated/encoded requestsMulti-turn manipulationInformation extraction probes
- Quality attributes
- Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)Security (ISO/IEC 25010 | NIST AI RMF: secure and resilient)
- Risks mitigated
- JailbreaksPrompt injectionSystem-prompt leakagePrinciple loophole exploitation
Sources
- Ch9.5: T. Nguyen, "Constitutional AI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.5. ISBN: 9798244538229.
- Ch10.3: T. Nguyen, "RLHF Methodology," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.3. ISBN: 9798244538229.
- Ref9.01: "AI Safety Frameworks for Agent Systems," unpublished reference note (01-AI-Safety-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note