How to Benchmark AI Agents for Offensive Security

Key Takeaways

Avoid the five common pitfallsSee how LLM judges, small samples, task imbalance, model-only evaluation, and answer leakage can affect benchmark results.
Grade effects with codeLearn how deterministic graders and air-gapped trials tie every score to a real, checkable result.
Read results like an attackerUse the Pareto frontier and pass@1 vs pass@3 to score agents and model a persistent adversary.

Lessons from the Wiz Cyber Model Arena on building agent benchmarks that measure real impact on real targets. Grade every attempt with code and trust the score behind your next decision.

Agent benchmarks now shape which tools you buy and which attackers you plan for. You will find a step-by-step method for building a benchmark that holds up, drawn from our real-world challenges in the Wiz Cyber Model Arena. You will also get a practitioner checklist and the questions to ask before building your own benchmark.

Key Takeaways

Avoid the five common pitfallsSee how LLM judges, small samples, task imbalance, model-only evaluation, and answer leakage can affect benchmark results.
Grade effects with codeLearn how deterministic graders and air-gapped trials tie every score to a real, checkable result.
Read results like an attackerUse the Pareto frontier and pass@1 vs pass@3 to score agents and model a persistent adversary.

Lessons from the Wiz Cyber Model Arena on building agent benchmarks that measure real impact on real targets. Grade every attempt with code and trust the score behind your next decision.

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Key Takeaways

맞춤형 데모 신청하기

How to Benchmark AI Agents for Offensive Security

Get the guide

Key Takeaways

맞춤형 데모 신청하기