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arXiv AI

Causal Scan for LLM Misbehavior Detection

By Advanced AI EditorMay 20, 2025No Comments2 Mins Read
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[Submitted on 22 Oct 2024 (v1), last revised 18 May 2025 (this version, v3)]

View a PDF of the paper titled LLMScan: Causal Scan for LLM Misbehavior Detection, by Mengdi Zhang and 5 other authors

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Abstract:Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful, biased and harmful responses poses significant risks, particularly in critical applications. This highlights the urgent need for systematic methods to detect and prevent such misbehavior. While existing approaches target specific issues such as harmful responses, this work introduces LLMScan, an innovative LLM monitoring technique based on causality analysis, offering a comprehensive solution. LLMScan systematically monitors the inner workings of an LLM through the lens of causal inference, operating on the premise that the LLM’s `brain’ behaves differently when misbehaving. By analyzing the causal contributions of the LLM’s input tokens and transformer layers, LLMScan effectively detects misbehavior. Extensive experiments across various tasks and models reveal clear distinctions in the causal distributions between normal behavior and misbehavior, enabling the development of accurate, lightweight detectors for a variety of misbehavior detection tasks.

Submission history

From: Mengdi Zhang [view email]
[v1]
Tue, 22 Oct 2024 02:27:57 UTC (6,054 KB)
[v2]
Wed, 23 Oct 2024 03:41:49 UTC (6,054 KB)
[v3]
Sun, 18 May 2025 16:55:15 UTC (9,742 KB)



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