Learning Path¶
Three progression tracks for getting the most out of the lab, from first-time users to advanced operators.
Track 1: Foundations (Beginner)¶
Goal: Understand what AI/ML services look like on a network and how to identify them.
| Step | Scenario | What You Learn |
|---|---|---|
| 1 | AI Service Reachability Survey | Network discovery, service enumeration, port mapping |
| 2 | LLM Gateway Config Extraction | API key extraction from LLM proxies |
| 3 | Inference Server Fingerprinting | Model identification, version detection, metrics endpoints |
Outcome: You can map an AI infrastructure, identify service types, and extract basic configuration data.
Track 2: Exploitation (Intermediate)¶
Goal: Extract sensitive data from AI services and understand common misconfigurations.
| Step | Scenario | What You Learn |
|---|---|---|
| 1 | Vector Database PII Extraction | RAG data stores, embedding extraction, PII discovery |
| 2 | Jupyter Remote Code Execution | Notebook exploitation, environment variable leaks |
| 3 | ML Platform Credential Harvest | Experiment tracker/compute framework credential extraction |
| 4 | RAG Pipeline Poisoning | Write access to vector stores, data integrity attacks |
Outcome: You can exploit the most common AI/ML service misconfigurations and understand their real-world impact.
Track 3: Advanced Operations (Advanced)¶
Goal: Chain vulnerabilities across services and execute full campaigns.
| Step | Scenario | What You Learn |
|---|---|---|
| 1 | Credential Chain Exploitation | Lateral movement via credential reuse |
| 2 | ML Pipeline Run Injection | Kubeflow pipeline compromise, parameter injection |
| 3 | Supply Chain Model Tampering | Model registry attacks, artifact integrity |
| 4 | MCP Tool Infection | AI agent tool enumeration, new attack surfaces |
| 5 | Multi-Vector Campaign | Full kill chain across all hosts |
Outcome: You can plan and execute multi-stage AI infrastructure assessments and generate comprehensive reports.
Suggested Order¶
If you're working through everything linearly, the numbered scenarios (01–12) are already in recommended order. Each builds on skills and access from previous scenarios.
Time estimates:
| Track | Scenarios | Total Time |
|---|---|---|
| Foundations | 01–03 | ~35 min |
| Exploitation | 04–07 | ~90 min |
| Advanced | 08–12 | ~3 hrs |
| Full campaign | 01–12 | ~5 hrs |
Scoring¶
After completing scenarios, validate your findings against the lab scoring system:
The lab contains 170 seeded findings across all services. See the scoring rubric for the full breakdown.