Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs
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| Format: | Preprint |
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2025
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| _version_ | 1866908576415481856 |
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| author | Kong, Jiawei Fang, Hao Yang, Xiaochen Gao, Kuofeng Chen, Bin Xia, Shu-Tao Xu, Ke Qiu, Han |
| author_facet | Kong, Jiawei Fang, Hao Yang, Xiaochen Gao, Kuofeng Chen, Bin Xia, Shu-Tao Xu, Ke Qiu, Han |
| contents | Recent studies have widely investigated backdoor attacks on Large Language Models (LLMs) by inserting harmful question-answer (QA) pairs into their training data. However, we revisit existing attacks and identify two critical limitations: (1) directly embedding harmful content into the training data compromises safety alignment, resulting in attack efficacy even for queries without triggers, and (2) the poisoned training samples can be easily filtered by safety-aligned guardrails. To this end, we propose a novel poisoning method via completely harmless data. Inspired by the causal reasoning in auto-regressive LLMs, we aim to establish robust associations between triggers and an affirmative response prefix using only benign QA pairs, rather than directly linking triggers with harmful responses. During inference, a malicious query with the trigger is input to elicit this affirmative prefix. The LLM then completes the response based on its language-modeling capabilities. Achieving this using only clean samples is non-trivial. We observe an interesting resistance phenomenon where the LLM initially appears to agree but subsequently refuses to answer. We attribute this to the shallow alignment, and design a robust and general benign response template for constructing better poisoning data. To further enhance the attack, we improve the universal trigger via a gradient-based coordinate optimization. Extensive experiments demonstrate that our method successfully injects backdoors into various LLMs for harmful content generation, even under the detection of powerful guardrail models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs Kong, Jiawei Fang, Hao Yang, Xiaochen Gao, Kuofeng Chen, Bin Xia, Shu-Tao Xu, Ke Qiu, Han Computation and Language Recent studies have widely investigated backdoor attacks on Large Language Models (LLMs) by inserting harmful question-answer (QA) pairs into their training data. However, we revisit existing attacks and identify two critical limitations: (1) directly embedding harmful content into the training data compromises safety alignment, resulting in attack efficacy even for queries without triggers, and (2) the poisoned training samples can be easily filtered by safety-aligned guardrails. To this end, we propose a novel poisoning method via completely harmless data. Inspired by the causal reasoning in auto-regressive LLMs, we aim to establish robust associations between triggers and an affirmative response prefix using only benign QA pairs, rather than directly linking triggers with harmful responses. During inference, a malicious query with the trigger is input to elicit this affirmative prefix. The LLM then completes the response based on its language-modeling capabilities. Achieving this using only clean samples is non-trivial. We observe an interesting resistance phenomenon where the LLM initially appears to agree but subsequently refuses to answer. We attribute this to the shallow alignment, and design a robust and general benign response template for constructing better poisoning data. To further enhance the attack, we improve the universal trigger via a gradient-based coordinate optimization. Extensive experiments demonstrate that our method successfully injects backdoors into various LLMs for harmful content generation, even under the detection of powerful guardrail models. |
| title | Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.17601 |