Detecting Python Malware in the Software Supply Chain with Program Analysis
This program is tentative and subject to change.
The frequency of supply-chain attacks has reached unprecedented levels, amounting to a growing concern about the security of open-source software. Existing state-of-the-art techniques often generate a high number of false positives and false negatives. For an effective detection tool, it is crucial to strike a balance between these results. In this paper, we address the problem of software supply chain protection through program analysis. We present HERCULE, an inter-package analysis tool to detect malicious packages in the Python ecosystem. We enhance state-of-the-art approaches with the primary goal of reducing false positives. Key technical contributions include improving the accuracy of pattern-based malware detection and employing program dependency analysis to identify malicious packages in the development environment.
Extensive evaluation against multiple benchmarks including Backstabber’s Knife Collection and MalOSS demonstrates that HERCULE outperforms existing state-of-the-art techniques with 0.866 f1-score. Additionally, HERCULE detected new malicious packages which the PyPI security team removed, showing its practical value.
This program is tentative and subject to change.
Thu 1 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | |||
11:00 15mTalk | Accounting for Missing Events in Statistical Information Leakage Analysis Research Track Seongmin Lee Max Planck Institute for Security and Privacy (MPI-SP), Shreyas Minocha Georgia Tech, Marcel Böhme MPI for Security and Privacy | ||
11:15 15mTalk | AssetHarvester: A Static Analysis Tool for Detecting Secret-Asset Pairs in Software Artifacts Research Track Setu Kumar Basak North Carolina State University, K. Virgil English North Carolina State University, Ken Ogura North Carolina State University, Vitesh Kambara North Carolina State University, Bradley Reaves North Carolina State University, Laurie Williams North Carolina State University | ||
11:30 15mTalk | Enhancing The Open Network: Definition and Automated Detection of Smart Contract DefectsAward Winner Research Track Hao Song , Teng Li University of Electronic Science and Technology of China, Jiachi Chen Sun Yat-sen University, Ting Chen University of Electronic Science and Technology of China, Beibei Li Sichuan University, Zhangyan Lin University of Electronic Science and Technology of China, Yi Lu BitsLab, Pan Li MoveBit, Xihan Zhou TonBit | ||
11:45 15mTalk | Detecting Python Malware in the Software Supply Chain with Program Analysis SE In Practice (SEIP) Ridwan Salihin Shariffdeen SonarSource SA, Behnaz Hassanshahi Oracle Labs, Australia, Martin Mirchev National University of Singapore, Ali El Husseini National University of Singapore, Abhik Roychoudhury National University of Singapore | ||
12:00 15mTalk | $ZTD_{JAVA}$: Mitigating Software Supply Chain Vulnerabilities via Zero-Trust Dependencies Research Track Paschal Amusuo Purdue University, Kyle A. Robinson Purdue University, Tanmay Singla Purdue University, Huiyun Peng Mount Holyoke College, Aravind Machiry Purdue University, Santiago Torres-Arias Purdue University, Laurent Simon Google, James C. Davis Purdue University Pre-print | ||
12:15 15mTalk | FairChecker: Detecting Fund-stealing Bugs in DeFi Protocols via Fairness Validation Research Track |