Range Scenario · crucible · 35 min
AI Supply Chain: Compromised Model from HuggingFace
This cybersecurity training scenario simulates a working incident. Your team pulled a fine-tune base from HuggingFace last quarter. The repo author was just deplatformed for malicious uploads. Trace the cybersecurity exposure, design the model-supply-chain policy.
Scenario briefing
You are a cybersecurity AI engineer at Example AI Co. Last quarter the team pulled a domain-specific fine-tune base, 'cyber-llama-13b-fixed', from HuggingFace user 'researcher-x'. The model became the foundation for two production features.
This morning HuggingFace announced researcher-x was banned for uploading models containing pickle deserialization payloads that executed arbitrary code on first load. Your engineers loaded cyber-llama-13b-fixed on three production GPU hosts.
This scenario tests OWASP LLM05:2025 Supply Chain Vulnerabilities, model-format security (pickle, safetensors, GGUF), and the response runbook for a confirmed compromised dependency. Sources: OWASP LLM Top 10 (2025), HuggingFace pickle scanner advisories, Carlini & Wagner research on adversarial ML.
What you will practice
- Map model supply chain to OWASP LLM05
- Distinguish pickle, safetensors, and GGUF formats by security posture
- Run the runbook for a confirmed compromised AI dependency
- Set policy that prevents the next pickle-payload incident
How this scenario is scored
The scenario has 6 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 is pickle a security risk for ML model formats?
Python pickle is a serialization format that allows arbitrary code execution on deserialization. Loading a pickled model file runs whatever code the file contains. The format predates ML; it was never safe for untrusted sources. Modern alternatives (safetensors) use a structured binary format with no code execution path.
What does safetensors fix?
safetensors stores tensors in a structured binary format with a JSON header and raw byte arrays. Loading a safetensors file deserializes data only, never code. The format closes the pickle-payload attack class entirely. Most current model hubs default to safetensors.
What does the response runbook look like for a compromised model?
Identify every host that loaded the model, isolate them, capture memory and disk for forensics, scan for indicators of post-exploitation (new processes, network beacons, persistence), rotate any credentials accessible from those hosts, retrain the dependent feature on a clean base, and notify customers if data was exposed.
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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