Large Language Models (LLMs) are vulnerable to prompt injection attacks, and several defenses have recently been proposed, often claiming to mitigate these attacks successfully. However, we argue that existing studies lack a principled approach to evaluating these defenses. In this paper, we argue the need to assess defenses across two critical dimensions: (1) effectiveness, measured against both existing and adaptive prompt injection attacks involving diverse target and injected prompts, and (2) general-purpose utility, ensuring that the defense does not compromise the foundational capabilities of the LLM. Our critical evaluation reveals that prior studies have not followed such a comprehensive evaluation methodology. When assessed using this principled approach, we show that existing defenses are not as successful as previously reported. This work provides a foundation for evaluating future defenses and guiding their development. Our code and data are available at: https://github.com/PIEval123/PIEval.
@article{arxiv.2505.18333,
title = {A Critical Evaluation of Defenses against Prompt Injection Attacks},
author = {Yuqi Jia and Zedian Shao and Yupei Liu and Jinyuan Jia and Dawn Song and Neil Zhenqiang Gong},
journal= {arXiv preprint arXiv:2505.18333},
year = {2025}
}