The content explores the challenges of safety generalization in large language models when presented with code inputs. It introduces CodeAttack, a framework that transforms text completion tasks into code completion tasks to test safety generalization. Experimental results reveal vulnerabilities in state-of-the-art models and emphasize the importance of robust safety alignment algorithms.
The study uncovers new risks associated with large language models in novel domains, particularly in the code domain. CodeAttack consistently bypasses safety guardrails, showcasing common vulnerabilities across different models. The findings suggest a gap in current safety mechanisms and call for more comprehensive red teaming evaluations.
Key points include:
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by Qibing Ren,C... om arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07865.pdfDiepere vragen