Skip to content

The Agent Incident Registry: Toward Preventing Repeated AI Agent Failures

Sep 2026 · 2 citations · 28 references
Computer Science

TL;DR

The Agent Incident Registry (AIR), a source-linked catalog containing records of agent-related events disclosed from \N{} through \Ylast{}.

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) (Project page: https://enkryptai.com/air), a source-linked catalog containing 487 records of agent-related events disclosed from 2022 through 2026. Each record includes supporting evidence, a stable identifier, and missingness-aware labels for causal role, disclosure class, mechanism, and outcome. Among the 336 generative-system records in which the agent acted, 81 involved realized harm (24\%). 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 487 records and their existing labels for completeness and correctness. In a deployment-analogue audit, InjecAgent's 1,054 cases occupy three of AIR's twelve surfaces and are all attacker-triggered, whereas AIR contains 92 no-adversary safety failures. AIR supports source-grounded case retrieval and evaluation-scope auditing, not failure-rate or control-efficacy estimation.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

Several open research questions are identified, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize, and privacy requirements are summarized and research directions for the secure and trustworthy deployment of AI agents are outlined.

Anastasia Pustozerova, E. Bagdasarian, Luca Beurer-Kellner et al. · 0 citations
Preprint Aug 2026

The Vulnerability With No CVE: Managing Persistent Gaps Between Mandate and Authority in AI Coding Agents

Existing guidance identifies excessive agency, excessive permission, weak task-bound authorization, and inadequate agent controls as important risks. Control frameworks also describe capabilities for constraining, authorizing, observing, validating, and responding to agent activity. Yet security programs still need a w...

S. Noga · 0 citations
#artificial intelligence Review Sep 2026

VeriWeave Govern: Evidence-Gated Deterministic Runtime Governance for Enterprise AI Agents

VeriWeave Govern is presented, a deterministic runtime governance layer that evaluates structured agent actions against versioned policies, validates typed evidence, applies fixed deny>review>allow precedence, routes consequential actions to accountable human review, and records replayable tamper-evident audit state.

Kabeh Mohsenzadegan, V. Tavakkoli, K. Kyamakya · 0 citations
Preprint Aug 2026

Towards Risk-free AI Agent Deployment

It is argued that risk-free deployment must be grounded in the agent's trajectory: the recorded sequence of reasoning steps, tool invocations, and environmental observations, and the absence of adequacy metrics.

Yin-Tong Huo, Rangeet Pan, Abhik Roychoudhury · 0 citations

Related blog posts

GPT-Lab Oct 14, 2026

Building trustworthy AI: The role of AI harnesses, intent contracts and evals

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post Building trustworthy AI: The role of AI harnesses, intent contracts and evals appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.