AGENTIC-SYSTEMSJuly 23, 20261 min read

NEXUS: Structured Runtime Safety for Tool-Using LLM Agents

NEXUS is a novel structured-plan safety monitor designed for tool-using LLM agents. By combining deterministic rules and calibrated risk scoring, NEXUS enforces graded intervention policies with sub-millisecond latency.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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