RL-Based Red Teaming Framework Exposes Persistent Prompt Injection Vulnerabilities in LLM Defenses
A new arXiv preprint introduces PISmith, a reinforcement learning-based framework designed to systematically test prompt injection defenses in large language models (LLMs). By training an attack model to optimize injected prompts in a black-box setting, the method reveals that state-of-the-art defenses remain vulnerable to adaptive attacks, achieving high success rates across 13 benchmarks and in agentic scenarios against both open-source and closed-source models. The study highlights the ongoing challenge of securing LLMs against evolving prompt injection strategies.
Why it matters: The work underscores a significant security gap in current LLM defenses, suggesting that widely used protections may not be sufficient against adaptive adversaries.
Full story at: arXiv Cryptography and Security ↗