Attack Type

Supply Chain

AI/ML systems sit on a long dependency chain: package managers (PyPI, npm, Cargo), model registries (HuggingFace Hub, Ollama Library), and dataset repositories. Each is a viable attack surface. Common patterns include typosquatting of popular AI packages, malicious post-install scripts in npm/PyPI uploads, and unsafe deserialization in shared model files — PyTorch and pickle-based formats can execute arbitrary code on load, which is why HuggingFace introduced the safer safetensors format. Model-registry attacks have included planting backdoored fine-tunes of popular base models that pass benchmark eval but misbehave on attacker-chosen triggers. Dataset poisoning is the slowest variant: an attacker who can influence a public training corpus inserts content that later teaches downstream models a backdoor. Defenses: pinned versions, signature verification, safetensors over pickle, provenance attestation (SLSA), and scanning model files before load.

965
Total CVEs
49
Pages
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Current
SeverityCVECVSS
HIGHCVE-2025-626097.5
CRITICALCVE-2023-11779.8
CRITICALCVE-2023-27809.8
CRITICALCVE-2023-376510.0
HIGHCVE-2023-40337.8
HIGHCVE-2023-60157.5
CRITICALCVE-2023-60189.8
CRITICALCVE-2023-60149.8
HIGHCVE-2023-67098.8
HIGHCVE-2023-67538.8
HIGHCVE-2023-68318.1
CRITICALCVE-2024-271329.6
CRITICALCVE-2024-271339.6
HIGHCVE-2024-15608.1
HIGHCVE-2024-370528.8
HIGHCVE-2024-370538.8
HIGHCVE-2024-370548.8
HIGHCVE-2024-370558.8
HIGHCVE-2024-370568.8
HIGHCVE-2024-370608.8

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