LLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
arXiv:2506.09956v2 Announce Type: replace-cross Abstract: Indirect prompt injection attacks exploit a fundamental weakness of LLMs: executing unauthorized tasks from untrusted sources. Despite the practical significance of this attack vector, existing benchmarks rely on small sets of template-based attacks (100-1,000 examples) that fail to capture the diversity and adaptiveness of real-world adversaries. Consequently, benchmarks are saturated and overfit to static attacks. We introduce LLMail-I…