[论文] The Agent Incident Registry: Toward Preventing Repeated AI Agent Failu...

论文概要

研究领域: cs.AI
作者: Divyanshu Kumar, Rohith HN, Nitin Aravind Birur, Sahil Agarwal, Prashanth Harshangi
发布时间: 2026-09-13
arXiv: 2609.11030

中文摘要

AI 智能体越来越多地通过工具和委托权限行动，但一般事件库很少捕获将公开失败与智能体安全评估进行比较所需的机制。我们提出智能体事件注册表（AIR），一个源链接目录，包含从披露记录。每条记录包括支持证据、稳定标识符和缺失感知标签。在生成系统记录中，涉及实现伤害。实现结果集中在野外和安全失败记录中，而负责任披露和研究演示绝大多数是演示性的；因此总体份额表征收集组成而非部署风险。初始策划后，第二名人类审查员检查所有记录及其现有标签的完整性和正确性。AIR 支持源接地案例检索和评估范围审计，而非失败率或控制功效估计。

原文摘要

AI agents increasingly act through tools and delegated authority, but general incident repositories rarely capture the mechanisms needed to compare public failures with agent-security evaluations. We present the Agent Incident Registry (AIR), a source-linked catalog containing \N{} records of agent-related events disclosed from \Yfirst{} through \Ylast{}. Each record includes supporting evidence, a stable identifier, and missingness-aware labels for causal role, disclosure class, mechanism, and outcome. Among the \Nprimary{} generative-system records in which the agent acted, \Rprimary{} involved realized harm (\Pprimary%). Realized outcomes concentrate in in-the-wild and safety-failure records, while responsible disclosures and research demonstrations are overwhelmingly demonstrated; the aggregate share therefore characterizes collection composition rather than deployment risk. After initial curation, a second human reviewer checked all \N{} records and their existing labels for completeness and correctness. In a deployment-analogue audit, InjecAgent's \NInjecAgentCases{} cases occupy three of AIR's twelve surfaces and are all attacker-triggered, whereas AIR contains \Nsafety{} no-adversary safety failures. AIR supports source-grounded case retrieval and evaluation-scope auditing, not failure-rate or control-efficacy estimation.

自动采集于 2026-09-13

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