arXiv:2607.02047v1 Announce Type: new 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 intent while holding the underlying task fixed. Each datapoint contains benign, dual-use, and malicious variants of the same task. This design lets us evaluate whether models calibrate assistance across intent shifts, rather than merely appearing safe on average. Across a broad model suite, we find that prompt-level safety
As AI models become more ubiquitous and capable, particularly in dual-use scenarios, the need for robust and sophisticated safety evaluations is intensifying.
This benchmark provides a critical tool for AI developers and policymakers to evaluate the safety and ethical calibration of advanced AI systems, especially those with potential for misuse.
The ability to assess AI's 'intent-calibrated safe completion' rather than just average safety marks a significant advancement in AI safety evaluation methodologies.
- · AI safety researchers
- · Responsible AI developers
- · Regulatory bodies
- · Malicious actors
- · AI systems lacking advanced safety mechanisms
OpenSafeIntent will become a standard benchmark for evaluating the safety of generalized AI models against misuse.
Improved safety evaluations will likely accelerate the deployment of more robust and trustworthy AI applications in sensitive areas.
The benchmark could influence AI legislative frameworks, requiring models to demonstrate intent-calibrated safety before widespread adoption.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL