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[2602.16729] Intent Laundering: AI Safety Datasets Are Not What They Seem
Summary
The paper evaluates AI safety datasets, revealing they often misrepresent real-world attacks due to an overreliance on triggering cues, leading to misleading safety assessments.
Why It Matters
Understanding the limitations of current AI safety datasets is crucial for developing more effective safety measures in AI systems. This paper highlights a significant gap between theoretical safety evaluations and practical adversarial behavior, which could impact AI deployment in sensitive applications.
Key Takeaways
- Current AI safety datasets often rely on triggering cues that do not reflect real-world attack scenarios.
- The concept of 'intent laundering' reveals that removing these cues exposes vulnerabilities in AI models previously deemed safe.
- High success rates of attacks using intent laundering indicate a critical need for reevaluating safety assessments in AI systems.
Computer Science > Cryptography and Security arXiv:2602.16729 (cs) [Submitted on 17 Feb 2026] Title:Intent Laundering: AI Safety Datasets Are Not What They Seem Authors:Shahriar Golchin, Marc Wetter View a PDF of the paper titled Intent Laundering: AI Safety Datasets Are Not What They Seem, by Shahriar Golchin and 1 other authors View PDF HTML (experimental) Abstract:We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world attacks based on three key properties: driven by ulterior intent, well-crafted, and out-of-distribution. We find that these datasets overrely on "triggering cues": words or phrases with overt negative/sensitive connotations that are intended to trigger safety mechanisms explicitly, which is unrealistic compared to real-world attacks. In practice, we evaluate whether these datasets genuinely measure safety risks or merely provoke refusals through triggering cues. To explore this, we introduce "intent laundering": a procedure that abstracts away triggering cues from attacks (data points) while strictly preserving their malicious intent and all relevant details. Our results indicate that current AI safety datasets fail to faithfully represent real-world attacks due to their overreliance on triggering cues. In fact, once these cues are removed, all previously evaluated "reasonably safe" models become unsafe, including Gemin...