Centrala begrepp
Large language models exhibit vulnerabilities in safety alignment when faced with code inputs, highlighting the need for improved safety mechanisms to address novel domains.
Sammanfattning
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:
- Introduction of CodeAttack framework for testing safety generalization.
- Vulnerabilities identified in state-of-the-art large language models against code inputs.
- Importance of robust safety alignment algorithms for safer integration of models into real-world applications.
Statistik
CodeAttack consistently bypasses the safety guardrails of all models more than 80% of the time.
Using less popular programming languages like Go instead of Python increases the attack success rate on Claude-2 from 24% to 74%.
Citat
"CodeAttack consistently bypasses the safety guardrails of all models more than 80% of the time."
"A larger distribution gap between CodeAttack and natural language leads to weaker safety generalization."