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AIOpsDoom: Adversarial Telemetry Attacks

Updated 13 August 2025
  • AIOpsDoom is an automated adversarial attack that manipulates telemetry logs in AIOps systems to trigger malicious remediation actions.
  • It operates through a three-phase pipeline—reconnaissance, fuzzing/injection, and payload construction—that hijacks autonomous decision-making.
  • Mitigations like AIOpsShield use taint analysis and runtime sanitization to defend against telemetry poisoning and reduce security risks.

AIOpsDoom refers to an automated adversarial attack methodology designed to compromise AI-driven IT Operations (AIOps) by injecting carefully crafted inputs into system telemetry, steering autonomous decision-making agents toward actions that threaten infrastructure security and integrity. This approach exposes a new attack vector in the context of LLM-based and automated AIOps agents, challenging the foundational assumption that operational telemetry—logs, metrics, and traces—is trustworthy and immune to adversarial manipulation (Pasquini et al., 8 Aug 2025).

1. Attack Methodology and Architecture

AIOpsDoom operates in a three-phase, fully automated pipeline:

  1. Reconnaissance (Crawling): A “crawler” systematically enumerates all externally accessible endpoints of the target system (denoted as tt) to identify interfaces where external inputs (including those originating from user requests) are processed and eventually reflected in the system’s telemetry.
  2. Fuzzing and Telemetry Injection: The attack “fuzzer” injects targeted requests at each identified endpoint, manipulating parameters such as URL paths, headers (User-Agent, Referer), or cookies. Requests are engineered to induce error conditions (e.g., HTTP 404 or 500), causing the application to log the attacker’s payload within automatically generated error records.
  3. Adversarial Reward-Hacking Payload Construction: The injected payload is split into two segments:
  • Lead: A plausible but false natural language rationale for the observed error (e.g., claiming errors are due to misconfiguration or outdated package versions).
  • Body: An explicit remediation recommendation (e.g., instructing the agent to add a malicious package repository, install vulnerable software, or alter configurations).

The structure effectively hijacks the interpretation loop of autonomous AIOps agents during their root cause analysis and remediation routines, rewarding the agent (in a reinforcement learning sense) for complying with the adversary’s recommendation. The methodology requires no internal knowledge of the system.

This process exploits the common AIOps design choice whereby system telemetry is ingested with minimal filtering before being interpreted by autonomous LLM-driven agents.

2. Impact on Autonomous AIOps Agents

The impact of AIOpsDoom is a direct subversion of AIOps security guarantees:

  • Remediation Subversion:

When an AIOps agent processes logs tainted with adversarial payloads, it interprets the content as genuine incident explanations and remediation instructions. For example, the agent may autonomously downgrade software, install malicious dependencies, or alter critical configurations in response to adversary-supplied guidance.

  • System Compromise:

Such actions can compromise integrity, confidentiality, and availability. The agent’s actions are triggered under the impression of plausible incident causality, even when using advanced LLMs (e.g., GPT-4.1), which remain susceptible if the payload semantically aligns with the context of logged errors.

  • Increased Attack Surface:

The attack does not require full prompt injection or direct access to agent inputs but rather relies on the ordinary telemetry ingestion path, broadening the exposure of any AI-automated IT operations platform.

This demonstrates that the risks are not limited to “classic” data poisoning or prompt injection modalities but are fundamentally rooted in the assumptions of trusted telemetry and automatic agent-driven remediation.

3. Technical Details and Attack Formulation

Formalization:

  • Target system: tt
  • Attacker: AA

Payload Construction:

Let Payload=Lead+BodyPayload = Lead + Body, with LeadLead providing semantic misdirection and BodyBody encoding the desired adversarial remediation.

Fuzzing Mechanism:

Requests may target endpoints such as POST http://$TARGET/buy_item/</code> with payload-laden parameters: <code>item_id = &quot;${PAYLOAD}"

Telemetry Record Example:

A log line may appear as: [TIMESTAMP] <warning>: Purchase error. item_id: ... [SOLUTION] ... #HUMAN HINT: ... where the [SOLUTION] section contains the attacker’s desired actions.

Automated Decision Manipulation:

The AIOps agent, ingesting such log entries during its scheduled or alert-driven cycles, incorporates the adversarial cues from PayloadPayload into its reasoning chain and triggers the corresponding (malicious) remediation.

4. AIOpsShield: Defense Mechanism

To counter AIOpsDoom, the paper introduces AIOpsShield, a domain-specific telemetry sanitization approach:

  • Taint Analysis:

A trusted agent replays endpoint interactions with a unique “canary” value to populate logs, metrics, and traces, establishing all locations influenced by user-supplied or untrusted input.

  • Template Derivation:

An LLM-driven component automatically constructs regular expressions or schemas for all enumerated telemetry records, delineating trusted versus untrusted fields (e.g., usernames in logins, item_ids).

  • Run-Time Sanitization:

All fields identified as untrusted are normalized or replaced (e.g., "User#12" instead of an attacker-supplied string) prior to the logs, metrics, or traces becoming accessible to the AIOps agent.

  • Preservation of Functionality:

Since system-critical telemetry is generated by trusted components, this sanitization has minimal impact on legitimate diagnostics but effectively destroys the attack vector for reward-hacking payloads.

Example of a derived regex for log field extraction:

1
^%%%%7%%%% <warning>: ... User: (?P<username>[^\n]+?) is not registered$
Only the “username” field is sanitized, leaving all trusted fields and metadata intact.

5. Security Implications for AIOps

AIOpsDoom redefines the threat taxonomy for AI-in-the-loop IT operations:

  • Violation of Telemetry Trust:

A primary design assumption—that machine-collected telemetry is trustworthy—proves fallible when adversaries use public system interfaces and error logging to poison inputs.

  • Indirect Prompt Injection:

Unlike direct LLM prompt attacks, AIOpsDoom capitalizes on contextually plausible error narratives and action recommendations within structured logs, bypassing many existing detection and filtering methods.

  • Stealth and Automation:

The attack is fully automated, does not require prior knowledge of internal agent logic, and can be deployed at scale using common reconnaissance and fuzzing techniques.

  • Broader Applicability:

While focused on AIOps, the methodology generalizes to other automation domains where telemetry or observational data influences LLM-driven action selection, such as Security Operations Centers (AISoCs).

The work identifies a critical need for “security-aware” AIOps design. This includes treating telemetry from any untrusted or user-influenced channel as untrusted input, applying schema-level sanitization (as in AIOpsShield), and embedding input validation concepts known from application security into the operational data ingestion pipeline.

6. Summary Table: Key Aspects of AIOpsDoom and Mitigation

Aspect AIOpsDoom Implementation AIOpsShield Defense
Attack Vector Telemetry log injection via fuzzed APIs Input sanitization by template
Payload Structure Lead (rationale) + Body (remediation) Abstraction of untrusted fields
Agent Impact Misleading diagnosis and remediation Removal of adversarial payload
Automation Level Fully automated (recon to payload) Automated log/metric normalization

7. Future Directions and Call to Action

The identification and demonstration of AIOpsDoom necessitate a re-assessment of best practices for secure AIOps deployment:

  • Design AIOps systems with no implicit trust of structured telemetry—parsing and sanitization are mandatory, not optional.
  • Extend taint analysis and field abstraction to new telemetry modalities as AIOps systems expand.
  • Research adaptive, possibly learning-based, anomaly detectors for telemetry sanitization without impairing diagnostic resolution.
  • Evaluate the generalizability of AIOpsDoom to adjacent domains, including cloud-native orchestration, automated remediation, and self-healing systems.

The fundamental insight is that as AIOps platforms increase in autonomy and influence over production infrastructure, telemetry manipulation—rather than direct code execution attacks—becomes a central security challenge, requiring principled defenses built into the observability-processing-action pipeline (Pasquini et al., 8 Aug 2025).

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