SRM: Detecting slow-burn risk in AI-agent sessions before execution
The paper introduces Session Risk Memory (SRM), a module designed to enhance safety in AI-agent sessions by providing trajectory-level authorization. SRM effectively evaluates agent actions while mitigating risks associated with distributed attacks. The results demonstrate that SRM significantly reduces false positives compared to existing systems while maintaining high detection rates.
- ▪Session Risk Memory (SRM) extends stateless execution gates with trajectory-level authorization.
- ▪SRM achieves an F1 score of 1.0000 with a 0% false positive rate in evaluations.
- ▪The framework distinguishes between spatial and temporal authorization consistency for improved session safety.
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Computer Science > Artificial Intelligence arXiv:2603.22350 (cs) [Submitted on 22 Mar 2026] Title:Session Risk Memory (SRM): Temporal Authorization for Deterministic Pre-Execution Safety Gates Authors:Florin Adrian Chitan View a PDF of the paper titled Session Risk Memory (SRM): Temporal Authorization for Deterministic Pre-Execution Safety Gates, by Florin Adrian Chitan View PDF Abstract:Deterministic pre-execution safety gates evaluate whether individual agent actions are compatible with their assigned roles. While effective at per-action authorization, these systems are structurally blind to distributed attacks that decompose harmful intent across multiple individually-compliant steps.
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