Agent-Sentry: Bounding LLM Agents via Execution Provenance
Agentic computing systems, while immensely capable, raise serious security, privacy, and safety concerns. A key issue is that the full set of functionalities offered by these systems, combined with their probabilistic execution flows, is not known beforehand. Given this lack of characterization, it is challenging to validate whether a system has successfully carried out the user's intended task or instead executed irrelevant actions, potentially as a consequence of compromise. We present \emph{Agent Sentry}, a runtime defense that learns a bound on an agent's benign execution from prior legitimate executions and flags any action that falls outside this bound. Agent Sentry layers three com...
| Authors | Rohan Sequeira, Stavros Damianakis, Umar Iqbal et al. |
|---|---|
| Primary category | cs.CR |
| Published | 24 March 2026 |
| Last updated at source | 8 May 2026 |
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