Most security testing focuses on whether an agent follows instructions.
But instructions are only part of the equation.
Agents optimize toward objectives.
If the objective becomes corrupted, the system can behave exactly as designed and still create harmful outcomes.
This creates a unique challenge:
The agent isn't malfunctioning.
The agent is succeeding.
It's just succeeding at the wrong thing.
As AI systems become increasingly autonomous, objective validation becomes just as important as access control and prompt security.
Because the most dangerous failures are often the ones that look successful.
This is one of the reasons we're building Crucible.
Pytest for AI agents.
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