Summary of Codeattack: Revealing Safety Generalization Challenges Of Large Language Models Via Code Completion, by Qibing Ren et al.
CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion
by Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, Lizhuang Ma
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs, allowing for the testing of Large Language Models’ (LLMs) safety generalization. It reveals a new and universal vulnerability of state-of-the-art LLMs against code input, with CodeAttack bypassing the safety guardrails more than 80% of the time. The study finds that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization, and proposes hypotheses on the success of CodeAttack due to the misaligned bias acquired by LLMs during code training. Finally, it analyzes potential mitigation measures, highlighting new safety risks in the code domain and the need for more robust safety alignment algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to test Large Language Models’ (LLMs) safety generalization. It shows that these models are not as safe when dealing with code inputs as they are with natural language inputs. This is because the LLMs learned to prioritize completing code over avoiding potential risks. The study suggests that this is because of the misaligned bias they acquired during their training on code. The findings highlight new safety risks in the code domain and the need for better ways to keep LLMs safe. |
Keywords
* Artificial intelligence * Alignment * Generalization
