Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs (2026.acl-long)
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| Challenge: | Empirical evaluations on ML models show substantial reductions in unsafe generations and improved robustness against jailbreak attacks. |
| Approach: | They propose a resource-efficient pruning framework that directly identifies unsafe behaviors while preserving model utility. |
| Outcome: | The proposed framework reduces unsafe generations and improves robustness against jailbreak attacks with minimal utility loss. |
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| Challenge: | Existing methods to decouple safety enforcement from harmful feature acquisition rely on perturbation directions that conflict with harmful gradients . harmful fine-tuning attacks pose a significant challenge for service providers aiming to uphold rigorous safety standards. |
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Rui Zhang, Hongwei Li, Yun Shen, Xinyue Shen, Wenbo Jiang, Guowen Xu, Yang Liu, Michael Backes, Yang Zhang
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| Challenge: | Recent studies show that fine-tuning can erode safety alignment, causing LLMs to respond to harmful or unethical prompts. |
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| Challenge: | Existing approaches to jailbreak Large Language Models (LLMs) use computationally intensive verification or require adversarial fine-tuning, leaving models vulnerable to advanced attacks. |
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| Challenge: | Large language models (LLMs) depend on vast amounts of text data sourced from the Internet for their training. |
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| Challenge: | Existing decoding-time defense methods suffer from limited generalization, high computational overhead, or significant utility degradation. |
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| Challenge: | Jailbreak attacks pose persistent threats to large language models . current safety alignment methods have insufficient safety alignment depth and unrobust internal defense mechanisms. |
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| Challenge: | Existing approaches to decode large language models (LLMs) often over-reject benign information, limiting their generalizability in real-world scenarios where harmful and benign information coexist. |
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