Evaluation infrastructure is the set of tests, scoring methods, datasets, and workflows used to judge a model before it is deployed. In practice, it turns subjective impressions into repeatable measurements: accuracy on a golden set, latency, output length, refusal quality, and task-specific error rates. For AI systems, this matters because a model that looks strong in a demo may fail on real prompts, edge cases, or constrained environments.
In cyber security, evaluation infrastructure helps teams verify that a model does not leak secrets, follow malicious instructions, or break policy when exposed to prompt injection and other abuse. Defenders use it to compare candidates, track regressions, and prove compliance before release. Attackers can exploit weak evaluation by optimizing for benchmark scores while hiding unsafe behavior, so production decisions should rely on workload-specific tests, logging, and review rather than vendor claims alone.