NEXUS: Structured Runtime Safety for Tool-Using LLM Agents

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[Submitted on 25 May 2026]

Title:NEXUS: Structured Runtime Safety for Tool-Using LLM Agents

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Abstract:Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential. We present NEXUS (Neural EXecution Utility and Safety), a structured-plan safety monitor that applies a formal intervention policy to select among four actions: allow, block, request confirmation, or request revision. NEXUS combines deterministic safety rules, argument-level inspection, and a calibrated logistic-regression risk score for graded escalation. On a 128-instance synthetic benchmark, NEXUS achieves an F1 score of 0.949 and a 4-class intervention accuracy of 0.6406, outperforming rule-only intervention selection by 27.3 percentage points. It also improves over rule-only on R-Judge (F1 = 0.861 vs. 0.849), matches rule-only on AgentHarm due to threat-model limits, and achieves 0% ASR at 99% control allow on IPI. On the rule-blind NEXUS-Stress benchmark, NEXUS reaches an F1 score of 0.881, highlighting the difficulty of fine-grained intervention routing. With 0.205 ms median latency, NEXUS adds under 0.1% overhead to typical agent loops. Code, benchmarks, and the calibrated risk scorer are publicly released.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.19356 [cs.AI]

(or arXiv:2607.19356v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.19356

arXiv-issued DOI via DataCite

Submission history

From: Md Elias Hossain [view email] [v1] Mon, 25 May 2026 22:24:09 UTC (1,707 KB)

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