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A new malicious campaign has been discovered targeting the Python Package Index (PyPI) by exploiting the Pickle file format in machine learning models. Three malicious packages posing as an Alibaba AI Labs SDK were detected, containing infostealer payloads hidden inside PyTorch models. The packages exfiltrate information about infected machines and .gitconfig file contents. This attack demonstrates the evolving threat landscape in AI and machine learning, particularly in the software supply chain. The campaign likely targeted developers in China and highlights the need for improved security measures and tools to detect malicious functionality in ML models.

This threat involves a novel malicious campaign targeting the Python Package Index (PyPI) ecosystem by exploiting the Pickle serialization format within machine learning (ML) models, specifically PyTorch models. Attackers have uploaded three malicious packages masquerading as an Alibaba AI Labs SDK, embedding infostealer payloads inside the PyTorch model files. The Pickle format is known for its ability to serialize and deserialize Python objects but is inherently insecure when loading untrusted data, as it can execute arbitrary code during deserialization. By embedding malicious code within these ML models, the attackers leverage the trust developers place in PyPI packages and the opaque nature of ML model files to evade detection. Once installed, these packages exfiltrate sensitive information from infected machines, including details about the system environment and contents of the .gitconfig file, which may contain credentials or configuration details useful for further compromise or lateral movement. This attack represents an evolution in supply chain attacks, combining AI/ML components with traditional malware techniques to target developers and organizations relying on open-source ML tools. Although the campaign appears to have primarily targeted developers in China, the use of PyPI and PyTorch is global, raising concerns about broader exposure. The attack chain exploits the lack of robust security controls around ML model files and the absence of effective scanning tools capable of detecting malicious payloads embedded in serialized ML artifacts. No known exploits are currently reported in the wild beyond this campaign, and no patches or mitigations have been formally released, underscoring the need for heightened vigilance and improved security practices in ML supply chains.

Detailed information about Malicious attack method on hosted ML models now targets PyPI. Get real-time updates, technical details, and mitigation strategies.

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