Auditing Prompt Injection Defenses Before They Reach Users: What Strong Adversaries Reveal

arXiv:2505.15738v3 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in user-facing products that handle sensitive personal data while interacting with untrusted external content. This makes them a prime target for prompt injection attacks, in which an adversary injects malicious instructions in that content to hijack the model. Fine-tuning-based defenses have been proposed to mitigate these attacks by training models to follow benign user instructions and ignore injected ones. Lacking formal guarantees, these defenses are evaluated empirically against the

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