Benchmark methodology is the set of rules that defines how a system is tested: what tasks are included, how outputs are scored, what data is used, and what tools or limits are allowed. In cyber security, the methodology matters as much as the score, because changing the harness or scoring rules can make the same model look better or worse.
This term appears often in AI security claims, where vendors compare models on curated tests such as vulnerability discovery, triage, or exploit reasoning. A strong benchmark result can suggest real capability, but only if the methodology matches the target environment and is disclosed clearly. Defenders use benchmark methodology to judge whether a tool will hold up in production, while attackers may exploit poorly designed evaluations to overstate performance or hide weak spots. Good methodology makes results reproducible, comparable, and harder to game.