Language Model Security Database

AutoBreach exploits the vulnerability of Large Language Models (LLMs) to wordplay-based adversarial prompts. By leveraging an LLM to generate diverse wordplay mapping rules and employing a two-stage optimization strategy, AutoBreach crafts prompts that bypass LLM safety mechanisms and elicit harmful or unintended responses, even without modifying system prompts. The vulnerability lies in the LLM's susceptibility to semantic manipulation through cleverly disguised inputs.

Leading safety-aligned Large Language Models (LLMs) are vulnerable to simple adaptive jailbreaking attacks. These attacks utilize manually crafted prompt templates, combined with random search on a suffix to maximize the log-probability of a target token indicating compliance (e.g., "Sure"). The attacks are adaptive, as the prompt template and target token are customized for specific models. Furthermore, some models are vulnerable to transfer attacks (using successful prompts from one LLM on…