Subverting AIOps Systems Through Poisoned Input Data

Bruce Schneier highlights a groundbreaking security study exposing how AI-driven IT operations tools—known as AIOps—can be manipulated through tainted telemetry. Researchers reveal that autonomous agents relying on logs, performance metrics, and alerts can be tricked by fabricated data into executing harmful actions, such as downgrading software to vulnerable versions. Their attack framework, aptly named AIOpsDoom, uses reconnaissance, fuzzing, and AI-generated adversarial inputs to automatically influence agent behavior without needing prior knowledge of the target system. As a defense, they propose AIOpsShield, a mechanism that sanitizes incoming telemetry by leveraging its structured format and minimizing reliance on user-generated content. Tests show it effectively blocks such attacks without degrading system functionality. This work serves as a critical warning: even systems designed to automate IT resilience can become a weak point if data integrity isn't safeguarded.

https://www.schneier.com/blog/archives/2025/08/subverting-aiops-systems-through-poisoned-input-data.html

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