HINTBench: Horizon-agent Intrinsic Non-attack Trajectory Benchmark

HINTBench addresses a critical but often overlooked gap in AI safety: intrinsic failure. While most safety benchmarks focus on 'red-teaming' or adversarial attacks (jailbreaking), HINTBench evaluates why agents fail naturally during complex, multi-step tasks. With 0 stars but 6 forks within 2 days of release, it is likely a brand-new research artifact associated with a pre-print or conference submission. Its defensibility is currently low (4) because benchmarks rely entirely on community adoption and 'prestige' to build a moat; without being integrated into standard leaderboards (like Open LLM Leaderboard) or adopted by major labs, it remains a reproducible reference implementation. Frontier labs (OpenAI, Anthropic) have a high interest in 'agentic reliability' and are likely building similar internal telemetry; however, an open-source standard for 'non-attack' risk is valuable for the broader ecosystem. The displacement horizon is 1-2 years as the field of agentic evaluation is moving rapidly, and newer, more comprehensive environments (like OSWorld or WebVoyager) often absorb the specific metrics introduced by niche benchmarks.