implementing-mlops
Pass
Audited by Gen Agent Trust Hub on Sep 17, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill provides legitimate industry guidance and best-practice implementation patterns for machine learning operations. No malicious patterns, data exfiltration, or unauthorized command execution were detected.
- [PROMPT_INJECTION]: Static analysis identified potential prompt injection patterns in
references/llmops-patterns.md. However, these are part of aSafetyCheckerclass designed specifically to detect and prevent prompt injection in AI applications, representing a defensive security feature rather than an attack. - [DYNAMIC_EXECUTION]: Static analysis flagged a
compile()call inexamples/kubeflow_pipeline.py. This is a false positive; the call is part of the Kubeflow Pipelines SDK (kfp.compiler.Compiler().compile) used to transform Python-defined pipelines into YAML manifests, not for executing arbitrary dynamic code. - [INDIRECT_PROMPT_INJECTION]: The skill identifies the surface area for indirect prompt injection in LLM-based applications. It correctly mitigates this risk by demonstrating the use of guardrails (NeMo Guardrails) and custom regex-based filters to sanitize inputs before they reach the model.
Audit Metadata