Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models

• URL: http://arxiv.org/abs/2406.05948v1
• Date: Mon, 10 Jun 2024 00:53:25 GMT
• Title: Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models
• Authors: Xi Li, Yusen Zhang, Renze Lou, Chen Wu, Jiaqi Wang,
• Abstract summary: Backdoor attacks present significant threats to Large Language Models (LLMs) We propose a novel solution, Chain-of-Scrutiny (CoS) to address these challenges. CoS guides the LLMs to generate detailed reasoning steps for the input, then scrutinizes the reasoning process to ensure consistency with the final answer.
• Score: 35.77228114378362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Backdoor attacks present significant threats to Large Language Models (LLMs), particularly with the rise of third-party services that offer API integration and prompt engineering. Untrustworthy third parties can plant backdoors into LLMs and pose risks to users by embedding malicious instructions into user queries. The backdoor-compromised LLM will generate malicious output when and input is embedded with a specific trigger predetermined by an attacker. Traditional defense strategies, which primarily involve model parameter fine-tuning and gradient calculation, are inadequate for LLMs due to their extensive computational and clean data requirements. In this paper, we propose a novel solution, Chain-of-Scrutiny (CoS), to address these challenges. Backdoor attacks fundamentally create a shortcut from the trigger to the target output, thus lack reasoning support. Accordingly, CoS guides the LLMs to generate detailed reasoning steps for the input, then scrutinizes the reasoning process to ensure consistency with the final answer. Any inconsistency may indicate an attack. CoS only requires black-box access to LLM, offering a practical defense, particularly for API-accessible LLMs. It is user-friendly, enabling users to conduct the defense themselves. Driven by natural language, the entire defense process is transparent to users. We validate the effectiveness of CoS through extensive experiments across various tasks and LLMs. Additionally, experiments results shows CoS proves more beneficial for more powerful LLMs.

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