The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures
2609.11030

Authors

Divyanshu Kumar,Rohith HN,Nitin Aravind Birur,Sahil Agarwal,Prashanth Harshangi

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.

Resources

Ray graphicRay graphicRay graphicRay graphic

Stay in the loop

Every AI paper that matters, free in your inbox daily.

Details

  • takara.ai
  • Custom AI and machine learning from the Frontier Research Team.
  • © 2026 takara.ai Ltd
  • Content is sourced from third-party publications.
Ray graphicRay graphicRay graphic