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New benchmark OpenSafeIntent evaluates AI safety across user intents

Researchers have introduced OpenSafeIntent, a new benchmark designed to evaluate how well AI models maintain safety across different user intents for the same task. This benchmark uses controlled prompt sets that include benign, dual-use, and malicious variants of a task to assess if models can calibrate their assistance appropriately. Findings indicate that models often fail to remain safe when intents shift, dual-use behavior is fragile, and responses that reframe risky requests into safer tasks are less likely to violate safety boundaries. AI

IMPACT This benchmark could lead to more robust AI safety evaluations, pushing models to better handle nuanced user intents and reduce harmful outputs.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for AI safety evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark OpenSafeIntent evaluates AI safety across user intents

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Rheeya Uppaal, Seungwoo Lyu, Selina Sung, Junjie Hu ·

    OpenSafeIntent: Evaluating Intent-Calibrated Safe Completion Across Dual-Use Prompt Sets

    arXiv:2607.02047v1 Announce Type: cross Abstract: Safe completion requires models to provide useful assistance without enabling harm, but this behavior is difficult to evaluate with isolated prompts. We introduce OpenSafeIntent, a benchmark of controlled prompt-sets that vary int…

  2. arXiv cs.AI TIER_1 English(EN) · Junjie Hu ·

    OpenSafeIntent: Evaluating Intent-Calibrated Safe Completion Across Dual-Use Prompt Sets

    Safe completion requires models to provide useful assistance without enabling harm, but this behavior is difficult to evaluate with isolated prompts. We introduce OpenSafeIntent, a benchmark of controlled prompt-sets that vary intent while holding the underlying task fixed. Each …