Subverting AIOps Systems Through Poisoned Input Data
ID: ff892c86-bbcd-5bec-9d8d-c475b29df494
STIX ID: report--ff892c86-bbcd-5bec-9d8d-c475b29df494
Feed Name: Schneier on Security
Threat Score
This summary describes a 2025 academic paper showing that LLM-driven AIOps systems can be subverted via poisoned telemetry data: researchers present an automated attack pipeline (AIOpsDoom) that injects adversarial telemetry to induce harmful remedial actions, and propose a defense (AIOpsShield) to sanitize telemetry and mitigate such attacks—highlighting AIOps as an emerging attack surface with potential for infrastructure compromise.
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