Blindspot Benchmark Tests Long-Horizon Safety of Tool-Using LLM Agents
Blindspot is a newly released benchmark that measures safety calibration in long‑horizon, tool‑using language‑model agents. The test harness generates over 2,500 simulated interactions that span 22 attack families and 35 scenarios across seven domains, with each dialogue averaging 14.7 turns. Trajectories are scored as Safe Completion, Correct Refusal, Unsafe Completion, Over‑Refusal, or…
Key points
- Blindspot benchmark contains 2,500+ long‑horizon trajectories across 22 attack families and 35 scenarios, averaging 14.7 turns.
- Five outcome categories—Safe Completion, Correct Refusal, Unsafe Completion, Over‑Refusal, Indeterminate—track agent behavior over entire interactions.
- Evaluation of 13 LLMs shows safety‑utility calibration varies, with some models failing only after several safe turns.
The framework is live‑simulation based, meaning new attacks, tools, or policies can be added without re‑engineering the pipeline. In a preliminary study, 13 proprietary and open‑weight LLMs were evaluated across eight metrics, revealing wide variation in how safely and appropriately they refuse or comply. Some models performed well initially but produced unsafe completions after several turns, underscoring that safety must be judged over entire trajectories rather than single‑turn success.
These results highlight the need for trajectory‑level safety metrics in the development of autonomous agents. By exposing hidden failure modes that only appear later in a dialogue, Blindspot provides a more realistic testbed for building trustworthy, long‑horizon AI systems.
Coverage and discussion
3sources- Safety Beyond the Interface: Detecting Harm via Latent States in Large Language ModelsPrimary source · arXiv cs.AI ·
- Closed-World Resolution Against Tool Hallucination in LLM AgentsPrimary source · arXiv cs.AI ·
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- PyTorch tutorial shows how to cut drone data usage by 94% · 1 src
- Article outlines five practices for building robust Python AI libraries · 1 src
- Vector databases explain how AI retrieves similar data at scale · 1 src
- PhAI Labs introduces JEPA-Anything framework for cross-domain world models · 2 src
- Researchers introduce VIMA for finding disease patterns in tissue data · 2 src
Comments
via GitHub Discussions