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arXiv AI By Tiantong Wu, Wei Yang Bryan Lim

Decision Hijacking: Prompt Injection Attacks on Jev's Typed Probabilistic Decisions

Read the original on arXiv AI →

The paper investigates prompt injection attacks on Jev, a non‑generative decision model, using 510 reconstructed cases. It finds that malicious prompts can shift Jev’s action probabilities, though rarely cause it to choose the attacker’s target. Techniques such as override markers mitigate influence, while adaptive attacks that use score feedback roughly double the highest attacker‑target probability and increase success rates on new validation calls from 1.8% to 3.5%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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