HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark

2026-02-17 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

HINTBench is a new benchmark designed to evaluate intrinsic risks in long-horizon AI agent execution under benign, non-adversarial conditions. It comprises 629 agent trajectories (523 risky, 106 safe), averaging 33 steps, significantly longer than previous benchmarks. The benchmark supports three auditing tasks: trajectory-level risk detection, coarse-grained risk-step localization, and fine-grained intrinsic failure-type identification, all organized under a unified five-constraint taxonomy (Goal, Factual, Capability, Procedural, and State Constraints). Experiments with large language models (LLMs) and specialized guard models reveal a substantial capability gap: while LLMs perform well on overall risk detection, their performance drops significantly (below 35 Strict-F1) for risk-step localization and fine-grained diagnosis. Existing guard models also transfer poorly to this intrinsic risk setting, often exhibiting prediction bias.

Key takeaway

For research scientists developing or deploying long-horizon AI agents, you should prioritize auditing for intrinsic failures under benign conditions, as HINTBench reveals a critical capability gap in current LLMs for risk-step localization and fine-grained diagnosis. Your safety evaluations must extend beyond simple trajectory-level risk detection to include precise identification of risky steps and failure types, as this is where current models struggle most. Consider integrating the five-constraint taxonomy to guide your agent design and auditing processes, aiming for more robust and transparent agent behavior.

Key insights

Intrinsic risks in long-horizon AI agents under benign conditions pose a significant, underexplored safety challenge.

Principles

• Intrinsic failures can propagate across long-horizon execution.
• Agent safety requires auditing beyond external attacks.
• A constraint-based taxonomy improves risk diagnosis.

Method

HINTBench uses a structured trajectory synthesis pipeline, starting with environment seed curation, then generating interaction skeletons, and finally expanding into complete trajectories, followed by human verification against a five-constraint taxonomy.

In practice

• Use HINTBench to evaluate agent intrinsic safety.
• Focus on step-level localization for robust auditing.
• Develop models for fine-grained failure diagnosis.

Topics

• HINTBench
• Intrinsic Risk Auditing
• Long-Horizon Agents
• Agent Safety Benchmarking
• LLM Evaluation

Best for: Research Scientist, AI Scientist, AI Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.