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Open-source software (OSS) supply chain enlarges the attack surface, whichmakes package registries attractive targets for attacks. Recently, packageregistries NPM and PyPI have been flooded with malicious packages. Theeffectiveness of existing malicious NPM and PyPI package detection approachesis hindered by two challenges. The first challenge is how to leverage theknowledge of malicious packages from different ecosystems in a unified way suchthat multi-lingual malicious package detection can be feasible. The secondchallenge is how to model malicious behavior in a sequential way such thatmaliciousness can be precisely captured. To address the two challenges, wepropose and implement Cerebro to detect malicious packages in NPM and PyPI. Wecurate a feature set based on a high-level abstraction of malicious behavior toenable multi-lingual knowledge fusing. We organize extracted features into abehavior sequence to model sequential malicious behavior. We fine-tune the BERTmodel to understand the semantics of malicious behavior. Extensive evaluationhas demonstrated the effectiveness of Cerebro over the state-of-the-art as wellas the practically acceptable efficiency. Cerebro has successfully detected 306and 196 new malicious packages in PyPI and NPM, and received 385 thank lettersfrom the official PyPI and NPM teams.

Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence