Spectral Guardrails for Agents in the Wild: Detecting Tool Use Hallucinations via Attention Topology

• URL: http://arxiv.org/abs/2602.08082v1
• Date: Sun, 08 Feb 2026 18:56:16 GMT
• Title: Spectral Guardrails for Agents in the Wild: Detecting Tool Use Hallucinations via Attention Topology
• Authors: Valentin Noël,
• Abstract summary: We propose a training free guardrail based on spectral analysis of attention topology that complements supervised approaches.<n>On Llama 3.1 8B, our method achieves 97.7% recall with multi-feature detection and 86.1% recall with 81.0% precision for balanced deployment.<n>We reveal the Loud Liar'' phenomenon: Llama 3.1 8B's failures are spectrally catastrophic and dramatically easier to detect, while Mistral 7B achieves the best discrimination.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying autonomous agents in the wild requires reliable safeguards against tool use failures. We propose a training free guardrail based on spectral analysis of attention topology that complements supervised approaches. On Llama 3.1 8B, our method achieves 97.7\% recall with multi-feature detection and 86.1\% recall with 81.0\% precision for balanced deployment, without requiring any labeled training data. Most remarkably, we discover that single layer spectral features act as near-perfect hallucination detectors: Llama L26 Smoothness achieves 98.2\% recall (213/217 hallucinations caught) with a single threshold, and Mistral L3 Entropy achieves 94.7\% recall. This suggests hallucination is not merely a wrong token but a thermodynamic state change: the model's attention becomes noise when it errs. Through controlled cross-model evaluation on matched domains ($N=1000$, $T=0.3$, same General domain, hallucination rates 20--22\%), we reveal the ``Loud Liar'' phenomenon: Llama 3.1 8B's failures are spectrally catastrophic and dramatically easier to detect, while Mistral 7B achieves the best discrimination (AUC 0.900). These findings establish spectral analysis as a principled, efficient framework for agent safety.

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