Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917598833147904 |
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| author | Kim, Heegyu Yuk, Sehyun Cho, Hyunsouk |
| author_facet | Kim, Heegyu Yuk, Sehyun Cho, Hyunsouk |
| contents | Caution: This paper includes offensive words that could potentially cause unpleasantness. Language models (LMs) are vulnerable to exploitation for adversarial misuse. Training LMs for safety alignment is extensive and makes it hard to respond to fast-developing attacks immediately, such as jailbreaks. We propose self-refine with formatting that achieves outstanding safety even in non-safety-aligned LMs and evaluate our method alongside several defense baselines, demonstrating that it is the safest training-free method against jailbreak attacks. Additionally, we proposed a formatting method that improves the efficiency of the self-refine process while reducing attack success rates in fewer iterations. We've also observed that non-safety-aligned LMs outperform safety-aligned LMs in safety tasks by giving more helpful and safe responses. In conclusion, our findings can achieve less safety risk with fewer computational costs, allowing non-safety LM to be easily utilized in real-world service. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15180 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement Kim, Heegyu Yuk, Sehyun Cho, Hyunsouk Machine Learning Computation and Language Cryptography and Security Caution: This paper includes offensive words that could potentially cause unpleasantness. Language models (LMs) are vulnerable to exploitation for adversarial misuse. Training LMs for safety alignment is extensive and makes it hard to respond to fast-developing attacks immediately, such as jailbreaks. We propose self-refine with formatting that achieves outstanding safety even in non-safety-aligned LMs and evaluate our method alongside several defense baselines, demonstrating that it is the safest training-free method against jailbreak attacks. Additionally, we proposed a formatting method that improves the efficiency of the self-refine process while reducing attack success rates in fewer iterations. We've also observed that non-safety-aligned LMs outperform safety-aligned LMs in safety tasks by giving more helpful and safe responses. In conclusion, our findings can achieve less safety risk with fewer computational costs, allowing non-safety LM to be easily utilized in real-world service. |
| title | Break the Breakout: Reinventing LM Defense Against Jailbreak Attacks with Self-Refinement |
| topic | Machine Learning Computation and Language Cryptography and Security |
| url | https://arxiv.org/abs/2402.15180 |