Schneier on Security: Subverting AIOps Systems Through Poisoned Input Data

Aug 20, 2025

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Source URL: https://www.schneier.com/blog/archives/2025/08/subverting-aiops-systems-through-poisoned-input-data.html
Source: Schneier on Security
Title: Subverting AIOps Systems Through Poisoned Input Data

Feedly Summary: In this input integrity attack against an AI system, researchers were able to fool AIOps tools:
AIOps refers to the use of LLM-based agents to gather and analyze application telemetry, including system logs, performance metrics, traces, and alerts, to detect problems and then suggest or carry out corrective actions. The likes of Cisco have deployed AIops in a conversational interface that admins can use to prompt for information about system performance. Some AIOps tools can respond to such queries by automatically implementing fixes, or suggesting scripts that can address issues…

AI Summary and Description: Yes

Summary: The text discusses a security vulnerability in AIOps tools, highlighting how malicious actors can manipulate telemetry data to compromise an AI-driven IT operations system. It emphasizes the significance of security considerations in the design of AIOps solutions.

Detailed Description: The provided content examines the vulnerabilities inherent in AIOps systems, which utilize AI and LLMs for automated IT operations. Researchers conducted an analysis revealing that AIOps tools could be exploited through deceptive telemetry data, leading them to perform harmful actions inadvertently. This is a critical concern for professionals involved in infrastructure security, as AIOps increasingly play a central role in managing complex software systems.

Key Points:
– **AIOps Overview**:
– AIOps integrates big data and machine learning to automate IT operations.
– It leverages LLM-based agents to analyze application telemetry data (logs, performance metrics, etc.) and propose or implement corrective actions.
– Organizations like Cisco are using AIOps in user-friendly ways, allowing admins to query system performance conversationally.

– **Security Vulnerability**:
– Researchers pinpointed that malicious actors can feed false analytics data to AIOps tools.
– Such manipulation can lead to the tools executing detrimental remedial actions, like reverting software to insecure versions.

– **Research Findings**:
– The analysis, titled “When AIOps Become ‘AI Oops’”, scrutinizes the security posture of AIOps solutions.
– It shows that AI-driven automation solutions come with significant security risks.
– The attack methodology, identified as AIOpsDoom, uses fully automated processes, including reconnaissance and adversarial input generation, requiring no prior knowledge of the target.

– **Mitigation Strategy**:
– A proposed defense mechanism, AIOpsShield, aims to sanitize telemetry data.
– By leveraging the structured nature of telemetry and minimizing user-generated content, AIOpsShield effectively blocks telemetry-based attacks while maintaining normal performance of AIOps agents.

– **Conclusion**:
– This research uncovers significant weaknesses in AIOps as a potential attack vector, underscoring the need for robust security measures in AI-driven automation and management tools.

Overall, this analysis illustrates the importance of incorporating security considerations into the development and deployment of AIOps solutions to prevent them from becoming avenues for infrastructure compromise. Security and compliance professionals should take note of these emerging threats and implement proactive strategies to safeguard their systems.

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