Create Model Ops and Supply Chain Security Cheat Sheet

templates/Model_Ops_and_Supply_Chain_Security_Cheat_Sheet.md

@@ -0,0 +1,49 @@
+# Model Ops and Supply Chain Security Cheat Sheet
+
+## Introduction
+
+Machine Learning (ML) models are frequently treated as static data, but in many common formats (like Python's Pickle), they are actually executable code. This "Model-as-Code" reality introduces significant supply chain risks, where malicious actors can embed "Pickle Bombs" or backdoors into pre-trained models.
+
+## Primary Risks
+
+### Unsafe Deserialization
+
+Loading a model using standard Python libraries (like `torch.load` or `pickle.load`) can execute arbitrary code hidden within the model file. A hacker can trigger a reverse shell or data exfiltration the moment a developer "loads" a downloaded model.
+
+### Model Poisoning and Backdoors
+
+Attackers can subtly alter model weights so that the model performs normally on most data but triggers a specific, malicious behavior when it sees a "trigger" input.
+
+## Mitigation Strategies
+
+### 1. Mandate Safe Serialization (Safetensors)
+
+Whenever possible, transition from `.pkl` or `.pth` (Pickle-based) formats to the **Safetensors** format.
+
+- **Why:** Safetensors is a "data-only" format. It contains no executable instructions, making it physically impossible to hide a script inside.
+
+### 2. Pre-Ingestion Scanning
+
+Treat every third-party model as "Untrusted Code."
+
+- **Tooling:** Use specialized scanners like `modelscan` or `fickling` to inspect the internal instruction stack (opcodes) of a model for malicious triggers.
+- **Environment:** Always perform scanning and initial testing in a network-isolated sandbox.
+
+### 3. Provenance and Integrity
+
+- **Hash Pinning:** Store and verify the SHA-256 hash of every model used in production.
+- **Signed Registries:** Only pull models from registries that support cryptographic signing and identity verification.
+
+## Code Examples
+
+### Unsafe vs. Safe Loading
+
+```python
+# UNSAFE: Risk of arbitrary code execution
+import torch
+model = torch.load('malicious_model.pkl')
+
+# SAFE: Only loads numeric tensors
+from safetensors.torch import load_file
+weights = load_file('safe_model.safetensors')
+```