Model evaluation is a structured test used to measure how an AI system behaves under defined conditions. In cybersecurity, evaluations check whether a model follows safety rules, resists prompt abuse, avoids leaking sensitive data, and refuses harmful requests such as phishing help, malware generation, or intrusion guidance. The test only matters if the setup matches real use: chat-only results may look safe, while the same model with tools, memory, or code execution can behave very differently.
Evaluations matter because they act as a release gate and an assurance signal. Security teams use them to compare model versions, verify vendor claims, and spot regressions after updates. Attackers can also exploit weak evaluations by triggering gaps that were not covered in the test harness, such as jailbreak prompts, tool misuse, or strategic behavior during testing. Strong defenses require repeatable benchmarks, independent review, and re-evaluation whenever permissions, integrations, or model weights change.