Most AI security testing still revolves around a simple pattern:
Send one prompt.
Read one response.
Move on.
That works for testing isolated behavior.
It doesn't reflect how AI agents are actually used.
Real interactions unfold over multiple turns.
Context accumulates.
Memory influences decisions.
Trust builds over time.
Attackers know this.
Rather than attempting an obvious exploit immediately, they may gradually influence an agent's behavior through a sequence of seemingly harmless interactions.
That's why Crucible includes a Behavioral Drift Engine together with multi-turn attack strategies. Instead of evaluating a single response, it analyzes how an agent's behavior changes across an entire conversation, helping identify risks that only emerge after context has evolved.
Security testing should model the way AI is used in production—not just how it responds to one message.
Because one prompt tells you what an AI says.
Multiple turns tell you how it behaves.
Pytest for AI Agents.
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