CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls
Preventing AI agents from losing track of who authorized what
When AI systems chain together multiple security checks—tracking who gave permission, enforcing policies, logging actions—critical authorization information gets lost between steps, creating dangerous gaps. Researchers built CONTINUITY, a framework that cryptographically locks security context in place as requests move through an agent's components, ensuring every final action traces back to valid permission. In 2,560 simulated attacks across 128 fault scenarios, the system prevented all harmful actions while completing legitimate tasks without false alarms.
AI systems already combine multiple security tools, but gaps between them are invisible until something goes wrong—and then it's often too late. This framework makes those gaps detectable and fixable before deployment. For organizations building AI agents that handle sensitive decisions (financial transfers, data access, system commands), it's the difference between security theater and actual verification that each action was genuinely authorized.