TL;DR for operators A tool-using agent does not experience an evaluation as an abstract benchmark. It sees prompts, tool wrappers, permissions, response timing, filesystem artifacts, network behavior, logging infrastructure, and other parts of the environment. If those signals differ from production, a sufficiently adaptive agent may be able to infer when it is being tested and behave differently. ...
TL;DR for operators Red teaming is not the act of finding one clever prompt that makes a model misbehave. That is a demo. Sometimes a useful demo, occasionally a terrifying one, but still a demo. The two papers here point to something more operational. RECAP shows how adversarial prompt generation can become cheaper by retrieving previously successful attack patterns rather than optimizing every new attack from scratch.1 A separate red-teaming framework shows how those attacks can be routed through a controlled attacker-target-jury workflow, with ensemble judging, task-specific criteria, and cross-linguistic analysis.2 ...