Papers for

ai system operators

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Agent Incident registry helps track AI agent failures and harms

The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures

Abstract: 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.

Thu 10 SeptArtificial Intelligence
The gist
AI agents sometimes fail, causing harm or security problems. The authors created the Agent Incident Registry, a detailed catalog of past AI agent failures with evidence and labels about how and why they happened. This registry helps people find real-world examples and compare incidents to improve safety. It does not estimate how often failures happen or how well controls work.
Open 2609.11030v1