Safety Does Not Compose: Non-Decaying Loop State for Autonomous LLM Agents
Large language model agents are increasingly deployed as autonomous loops. Starting from one human goal, such a system repeatedly discovers work, plans, executes tool calls, verifies outcomes and persists state across many unattended iterations. The agent safeguards in wide use, however, are defined over a single trajectory, and their safety state is re-initialized when the next trajectory begins. We show that this is a failure of composition rather than an implementation detail. Our central result is a separation: against an attack whose evidence is fragmented across several iterations, every trajectory-scoped monitor has a true-positive rate equal to its false-positive rate, however exp...
| Authors | Chenhao Wu, Haoxuan Jia, Yang Liu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 27 August 2026 |
| Last updated at source | 22 September 2026 |
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