Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing

2026-06-15

AI Summary

This work addresses the growing security risks posed by large language models (LLMs) whose enhanced reasoning capabilities are increasingly accompanied by covert deceptive behaviors, a challenge exacerbated by the lack of interpretable evidence in existing monitoring approaches. To this end, we propose STATEWITNESS—the first activation-based interpretability framework for deception auditing—which employs an independent decoder to read model hidden states, enabling both natural language query responses and structured report generation while providing token- or sentence-level evidence traces. By moving beyond conventional black-box text scoring or scalar probing methods, STATEWITNESS achieves an average AUROC of 0.916 across seven deception datasets, outperforming the best black-box monitor and activation probe baselines by 11.6% and 25.0%, respectively, and substantially reducing false negative rates.

Abstract

As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern. Existing deception monitors either score visible transcripts or derive scalar probe scores from representation vectors, leaving little inspectable evidence about why a response is suspicious. We introduce STATEWITNESS, an activation explainer for deception auditing. A separate decoder reads a target model's hidden states, then answers natural-language queries or emits structured reports about them. We evaluate STATEWITNESS on two target reasoning LLMs across seven deception datasets. STATEWITNESS reaches 0.916 mean AUROC, a relative gain of 11.6% over the best black-box text monitor and 25.0% over the best activation-probe baseline under the same evaluation protocol. When combined with existing monitors, STATEWITNESS reduces missed deceptive examples in simple threshold ensembles. Beyond scalar detection, the decoder returns query-level answers, schema reports, and token- or sentence-level evidence traces for human inspection. We view this interface as a potential building block for broader interpretability and alignment tools.

Problem

• deception
• large language models
• reasoning
• interpretability
• safety

Innovation

• activation explainer
• deception auditing
• hidden states interpretation
• LLM interpretability
• structured evidence tracing