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

arXiv:2505.14368v2 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 trustworthy LLM era. This paper comprehensively studies effective prompt injection attacks against 14 widely used open-source and three closed-source LLMs on five attack benchmarks. Moreover, existing evalua

Source: arXiv cs.CL — read the full report at the original publisher.

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