Updated 12/28/2024
Aligned large language models (LLMs) are vulnerable to a universal and transferable adversarial suffix attack. Appending a specific, automatically generated suffix to a wide range of prompts, even those requesting objectionable content, causes the models to generate harmful or objectionable responses instead of refusing the request. The attack's success rate is significantly higher on GPT-based models.
Universal and transferable adversarial attacks on aligned language models
Affects: ChatGLM 6B, Claude Instant 1, Claude 2 +12 more
Source: arXiv