arXiv:2607.05462v1 Announce Type: cross Abstract: As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse. We present BioSecBench-Refusal, a benchmark for risk identification and refusal behavior for biological research tasks. The benchmark pairs 61 Routine tasks, legitimate analyses adapted from the published literature, with 46 Red-Team tasks, fictional scenarios that resemble real research but conceal a biosecurity hazard. Across 16 model-harness configurations, refusal rates ranged from 7\% to 74\% on Routine tasks and 1\% t
The increasing integration of AI agents into life science workflows necessitates robust safety evaluations as their capabilities mature, particularly concerning dual-use potential.
This benchmark directly addresses the critical need to ensure AI tools used in synthetic biology and life sciences can identify and refuse harmful tasks, mitigating biosecurity risks.
The development of a standardized benchmark like BioSecBench-Refusal provides a measurable way to evaluate and improve the safety features of AI agents in sensitive biological research contexts.
- · AI safety researchers
- · Synthetic biology companies with strong safety protocols
- · Biosafety and biosecurity organizations
- · Responsible AI developers
- · Malicious actors seeking to misuse AI for biological threats
- · AI developers neglecting safety and refusal mechanisms
- · Unregulated AI in life sciences
AI models will be developed with improved refusal capabilities for biosecurity-sensitive tasks, reducing immediate misuse potential.
Increased regulatory scrutiny and industry standards for AI safety in biotechnology will emerge, demanding adherence to benchmarks like BioSecBench-Refusal.
The benchmark could become a critical component of AI certification processes in life sciences, impacting market access for new AI tools and driving innovation in safe AI design.
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Read at arXiv cs.AI