JevBreak: Your Model, Their Decision

Nitin Aravind Birur, Divyanshu Kumar, Sahil Agarwal, Prashanth Harshangi

Restricting what a model can say is often mistaken for restricting an attacker's influence over its decision. An attacker can add text to the evidence and steer the model toward a wrong answer that still belongs to the allowed set. We evaluate seven decision models spanning hosted APIs, open-weight models and an encoder on 16 tasks in seven domains from Decision Bench, targeting every wrong option across 240 text examples (990 targets per model). We present Slot Attack, a black-box attack designed for decision models that appends text assembled from named components to steer predictions toward an attacker-selected incorrect option. The method relies solely on returned label probabilities, achieves success rates of 32--99\% on initially correct decisions, and identifies the effective component combinations with a median of 17--72 queries per successful attack. Accuracy and confidence do not reliably reveal this vulnerability. Models with near-identical accuracy on the sampled tasks differ by about 20\%age points in attack success. We demonstrate that an attacker can force not just one wrong answer but every available one: on about 20\% of correct decisions for the JEV models, 77\%…