ENDOPROMPT: Victim-Side Pseudo-References for Utility Degradation

Abstract:Prompt injection can degrade benign task performance without eliciting harmful content. Yet many attack objectives depend on task labels or predefined target responses. We present ENDOPROMPT, a white-box method that learns utility-degrading prefixes from unlabeled instructions. Its generator takes the request text as input. Clean victim continuations serve as pseudo-references: local search identifies prefixes that reduce continuation likelihood, and preference fitting on comparisons within the same instruction, followed by reward refinement, distills this signal into a generator. At deployment, the generator produces one prefix per request without further victim-side search. Across four instruction-tuned models and the complete splits of seven benign benchmarks, ENDOPROMPT yields a mean utility change of -26.8 percentage points; 27 of 28 cells are negative. Failure analysis reveals output expansion and prefix reuse; the controls do not establish a degradation advantage from request matching. Victim-derived supervision can reveal utility weaknesses without benchmark feedback or prescribed failure responses. The code will be released upon acceptance.

Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)

arXiv:2609.29948 [cs.AI]

(or arXiv:2609.29948v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.29948 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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