AI RESEARCH
Intent Laundering: AI Safety Datasets Are Not What They Seem
arXiv CS.LG
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ArXi:2602.16729v3 Announce Type: replace-cross We systematically evaluate the quality of widely used adversarial safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world adversarial attacks based on three defining properties: being driven by ulterior intent, well-crafted, and out-of-distribution.