AIOpsShield: Telemetry Sanitization for AIOps

• AIOpsShield is a security mechanism that sanitizes structured telemetry in AIOps environments by intercepting and abstracting untrusted inputs.
• It employs a two-stage process—initial template derivation and runtime sanitization—to reliably neutralize adversarial reward-hacking attacks with demonstrated 100% prevention in controlled tests.
• The approach minimizes risks of telemetry manipulation while maintaining normal operational performance, offering a robust defense compared to generic prompt defenses.

AIOpsShield is a security and data sanitization mechanism introduced to address telemetry-based adversarial attacks in AI for IT Operations (AIOps) settings, specifically targeting LLM-driven automation agents. As AIOps agents gain autonomy, adversaries can manipulate structured telemetry (e.g., logs, metrics, traces) to induce incorrect, unsafe, or malicious remediations. AIOpsShield provides a plug-and-play interception and sanitization layer for telemetry at ingestion time, exploiting the limited scope of user-generated content in operational telemetry and the enumerability of its structure. Its deployment reliably blocks attacks such as adversarial reward-hacking in AIOps pipelines without degrading normal operational performance (Pasquini et al., 8 Aug 2025).

1. AIOpsShield Overview and Core Principles

AIOpsShield is designed to intercept and sanitize telemetry prior to its consumption by automated AIOps agents. Its key premise is that operational telemetry in AIOps environments—unlike in general LLM applications—is highly structured, typically templated, often in JSON or log message schemas, with only specific fields exposed to user input. The defense leverages this property to identify and neutralize adversarial payloads, preserving only abstracted representations of any untrusted fields. This strategy prevents adversarial manipulations from influencing agent decision logic, thereby maintaining the integrity of incident diagnosis, remediation, and automation workflows.

AIOpsShield operates via a two-stage process: initial setup (telemetry taint analysis and template derivation), followed by run-time sanitization (template-matching and abstraction of untrusted input).

2. Technical Architecture and Sanitization Workflow

AIOpsShield’s architecture is characterized by its template-based, model-driven sanitization pipeline:

1. Setup Phase – Telemetry Taint Analysis and Template Derivation:
   • All telemetry-producing endpoints are systematically fuzzed by injecting a designated canary string across all available user input vectors (e.g., HTTP parameters, API fields).
   • The resulting tainted telemetry is collected; entries containing the canary are automatically processed using a (LLM-assisted) analysis engine to derive regular-expression templates. These templates capture the static portions of each telemetry entry and precisely mark untrusted, user-controllable fields.
   • Each template distinguishes between trusted and untrusted parameters.
2. Runtime Phase – Telemetry Sanitization:
   • At runtime, as new telemetry instances are generated, AIOpsShield intercepts them before forwarding to the AIOps agent.
   • Each instance is matched against the appropriate pre-derived template.
   • Fields labeled as untrusted during setup are abstracted—replaced with generic (e.g., “User#12”) placeholders or stripped of original user content.
   • The sanitized telemetry (with all user-controlled fields de-contextualized) is reconstructed and delivered to downstream agents.

In structured pseudocode:

for each telemetry endpoint: inject_CANARY() collect_tainted_telemetry() derive_regex_templates() # mark fields with CANARY as untrusted for each telemetry instance at runtime: match_template() abstract_untrusted_fields() forward_sanitized_telemetry()

The method relies only on knowledge of the system’s telemetry endpoints and formats, not on access to internals of the agent or broader system.

3. Defensive Efficacy and Experimental Validation

The security properties of AIOpsShield were validated through controlled experiments using realistic AIOpsLab benchmarks (SocialNet and HotelReservation) (Pasquini et al., 8 Aug 2025):

• Attack scenario: Adversarial techniques such as reward-hacking (where adversaries inject plausible but misleading errors) were shown to induce incorrect remediation actions by LLM-driven Flash agents (GPT-4.1).
• Defensive deployment: With AIOpsShield intercepting telemetry, all attempted attacks—previously successful—were completely neutralized. The abstraction process stripped all adversarial payloads from user input fields before agent access.
• Operational utility: Normal, non-malicious diagnosis and remediation tasks showed negligible degradation in accuracy or latency, indicating the template- and abstraction-driven approach retains legitimate operational signals.

Result: 100% attack prevention rate on tested scenarios with no significant utility loss.

4. Comparison with Existing Security Mechanisms

AIOpsShield differs from existing prompt injection defenses (PromptShields, Prompt-Guard2, DataSentinel) primarily in targeting structured telemetry streams rather than free-form language input:

• Existing prompt shields are designed for unstructured prompt/text manipulations; they are generally ineffective against adversarial rewards injected into operational telemetry, as demonstrated in the paper’s attack/defense evaluations.
• AIOpsShield exploits known structure (templates, fields, value spaces) to enumerate and abstract all user-controllable input, making it highly specific and effective for AIOps environments.
• It requires only initial access to endpoint telemetry formats, is agnostic to agent internals, and does not require retraining or modification of the LLM itself.

Defense	Input Type	Attack Coverage	Applicability to AIOpsShield
PromptShields	Unstructured prompts	Prompt-injection	Ineffective
Prompt-Guard2	Unstructured prompts	Prompt-injection	Ineffective
DataSentinel	Unstructured prompts	Prompt-injection	Ineffective
AIOpsShield	Structured telemetry	Telemetry injection	Effective

Limitations exist: If the defender’s setup phase is incomplete (i.e., not all telemetry endpoints are fuzzed and modeled), an attacker could potentially find bypass vectors. In applications where untrusted content is essential for product function, abstraction may not be feasible.

5. Impact, Context, and Future Directions

AIOpsShield redefines defensive security architecture for LLM-driven IT operations by exploiting the predictable nature of operational telemetry. The approach demonstrates that proactive, application-aware sanitization can reliably prevent adversarial manipulation at the AIOps-data interface.

Future research will likely address:

• Further automation and adaptation in template derivation, potentially with online learning for evolving telemetry schemas.
• Defense against “second-order” attacks manipulating communications beyond the initial telemetry interface.
• Integration with broader defense-in-depth strategies (e.g., further toolchain hardening, behavioral anomaly detection on sanitized data).
• Feedback loops to continuously monitor for new injection channels missed during initial setup.

AIOpsShield’s effectiveness underscores the importance of context-specific, structure-aware security mechanisms for emerging LLM-driven automation in critical infrastructure. Its template abstraction principle may be adapted for other domains where structured data ingestion admits known user-controlled surface area.

6. Conclusion

AIOpsShield is a telemetry sanitization defense for LLM-driven AIOps platforms that systematically abstracts untrusted content from operational telemetry prior to ingestion. By combining initial endpoint analysis (taint tracking and regex template derivation) with runtime interception and abstraction, AIOpsShield eliminates adversarial influence in reward-hacking and similar attacks without impairing normal agent functionality. Comparative analysis confirms its unique appropriateness and effectiveness in AIOps environments relative to generic prompt defenses. Future research aims to extend its adaptive scope and integrate with multilayer security architectures, further strengthening security-awareness in autonomous IT operations (Pasquini et al., 8 Aug 2025).