From ASR to ASP: Evaluating Prompt Attack Vulnerabilities Against Open-Source LLMs

Excerpt

arXiv:2505.14368v3 Announce Type: replace-cross Abstract: Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to attacks that generate harmful or sensitive outputs. As open-source LLMs are increasingly adopted in high-impact applications such as finance, law, and healthcare, systematically investigating their security risks is becoming increasingly important towards a trustworthy LLM era. This paper comprehensively studies effective prompt injection attacks against 14 wid