If You've Seen One, You've Seen Them All: Leveraging AST Clustering Using MCL to Mimic Expertise to Detect Software Supply Chain Attacks

Trojanized software packages used in software supply chain attacks constitute an merging threat. Unfortunately, there is still a lack of scalable approaches that allow automated and timely detection of malicious software packages. However, it has been observed that most attack campaigns comprise multiple packages that share the same or similar malicious code. We leverage that fact to automatically reproduce manually identified clusters of known malicious packages that have been used in real world attacks, thus, reducing the need for expert knowledge and manual inspection. Our approach, AST Clustering using MCL to mimic Expertise (ACME), yields promising results with a $F_1$ score of 0.99. Signatures are automatically generated based on representative code fragments from clusters and are subsequently used to scan the whole npm registry for unreported malicious packages. We are able to identify and report six malicious packages that have been removed from npm consequentially. Therefore, our approach is able to reproduce clustering based on expert knowledge and hence may be employed by maintainers of package repositories like npm to timely detect possible maliciousness of newly uploaded or updated packages.