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New SkillLifeBench benchmark reveals AI agent security gaps across skill lifecycles

By PulseAugur Editorial · [1 sources] ·

A new benchmark, SkillLifeBench, has been developed to evaluate the security of AI agent skills across their entire lifecycle, including implementation, interface, runtime, composition, and version changes. The benchmark consists of 388 paired scenarios and was used to test 14 models. Notably, GPT-5.6-Sol and Claude Sonnet 4.6 performed similarly on runtime tasks, achieving 82.0%, but diverged significantly on lifecycle tasks, with GPT-5.6-Sol scoring 96.7% and Sonnet 4.6 scoring 56.7%. The study also found that providing execution provenance improved recovery rates, highlighting the importance of detailed evidence for agent skill management. AI

IMPACT Highlights critical security vulnerabilities in AI agent skill management, potentially impacting the reliability and safety of autonomous systems.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for AI agent security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SkillLifeBench benchmark reveals AI agent security gaps across skill lifecycles

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The cluster describes a new academic paper introducing a benchmark for AI agent security. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chenxu Zhang, Zhou Liu, Wu Xin, Wentao Zhang ·

    SkillLifeBench: Benchmarking Security Across the Lifecycle of Agent Skills

    arXiv:2610.35215v1 Announce Type: cross Abstract: Web agents reuse skills whose stored code, selected version, and loaded implementation can diverge across tasks and updates. SkillLifeBench links these states to task outcomes in 388 paired scenarios spanning implementation, inter…

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