Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

2026-09-21

AI Summary

本文针对AI代理安全事件报告问题，通过专家意见识别必要信息，并提出高效记录事件和评估漏洞泛化等研究方向。

Abstract

AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting becomes increasingly important for legal compliance, governance, accountability, and security; current frameworks must be adapted to the unique characteristics of AI agents. In this paper, two editorial authors compare AI systems and AI agents and, drawing on input from 23 experts in academia and industry, identify the information required for reporting incidents where the security of AI agents is harmed. %involving AI agents. Potential reporting elements include, for example, agent memory and memory accesses, actual and potential levels of autonomy, and tool usage. Based on these findings, we identify several open research questions, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize. Expert feedback also highlighted potential reporting weaknesses, such as risks of data leakage and attacks targeting the reporting infrastructure itself, creating additional research needs. Lastly, we summarize privacy requirements and outline research directions for the secure and trustworthy deployment of AI agents.

Problem

• AI agents
• incident reporting
• security
• vulnerabilities
• data leakage

Innovation

• AI Security Incident Reporting
• Autonomy Levels
• Agent Memory