Range Scenario · gauntlet · 50 min
AI Infrastructure Secret Protection: Diagnose Pipeline Leakage
This cybersecurity training scenario simulates a working incident. An AI training pipeline is leaking secrets into logs, manifests, and cybersecurity-relevant artifacts. You have 45 minutes to find them, contain the exposure, and rewrite the IaC so it never happens again.
Scenario briefing
You are the AI cybersecurity engineer for a 200-person ML platform team. A security researcher emailed disclosures with a screenshot showing an OPENAI_API_KEY in a public training log. You have 45 minutes to find every secret leak in the pipeline, rotate exposed keys, and produce a remediation plan.
Inputs: a Terraform module for the training cluster, a Helm values file for the inference service, the training pipeline orchestration YAML, and a sample training log fragment. Each contains realistic synthetic credentials. Find them, contain, fix.
This scenario tests reading IaC for credential exposure, recognizing the difference between a key in code and a key in deployed state, and writing an IaC pattern that survives developer mistakes. Real production AI pipelines leak secrets the same way.
What you will practice
- Spot embedded credentials in Terraform, Helm, and pipeline YAML
- Distinguish 'secret in repo' from 'secret in deployed state' from 'secret in log'
- Rotate exposed credentials in the right order
- Rewrite IaC to use external secret stores instead of inline values
How this scenario is scored
The scenario has 8 ordered steps. Most steps are exact-match (a MITRE ATT&CK technique ID, a tool name, or a yes/no decision) or multiple choice. Free-text steps queue for manual review and do not affect the auto-final-score in the MVP.
Each step has a max score of 100 points. Hints deduct points up front, listed before you reveal them. Your final score is the sum across steps. Range Elo updates on completion based on scenario difficulty (Advanced) and your final score percentage.
Frequently asked questions
Why do AI pipelines leak secrets more than other systems?
AI pipelines often glue together training scripts, data pipelines, model registries, inference services, and observability tools. Each component reads its own configuration. Developers paste keys into the closest config to make a thing work, and the keys land in a Helm values file, a training log line, or an environment variable that gets dumped. The complexity makes leak surfaces wider than a typical web app.
What is the right secret rotation order after a leak?
Rotate the most-impactful key first (production inference API key, model registry access key). Then training-cluster credentials (object storage access keys, container registry credentials). Then observability and CI/CD tokens. Document who rotated what, when, and the new key fingerprint. Audit logs in the relevant providers verify the old key is dead.
How do you prevent the leak from recurring?
External secret stores referenced by IaC, never inline values. CI/CD scanning that blocks PRs containing detected secret patterns. Runtime injection from secret store at pod start, not bake-in to images. Log scrubbing for known patterns. Periodic credential rotation. Each layer catches a different failure mode.
Course content is for educational purposes only and does not constitute professional advice. All claims are supported by cited peer-reviewed academic research. DecipherU does not teach or reproduce any proprietary sales methodology. Verify all referenced sources independently.
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