Security
Sign Here: Attributing a Source for a More Trustworthy Software Supply Chain
arXiv cs.CR0 views
arXiv:2610.28794v1 Announce Type: new Abstract: Software supply chain attacks continue to be on the rise, exploiting the trust that helps open source ecosystems thrive. Recent incidents like the Shai-Hulud worm and the Axios compromise demonstrate that a single malicious package can have wide reaching and devastating impacts overnight. These high profile attacks can be traced back to the same culprit: package registries. Although they are a useful convenience to pull prebuilt packages from, is such a commonly exploited component of the software supply chain worth the risk? In this paper, we investigate whether removing the registry from the developer workflow by building packages directly from source can serve as a malware prevention strategy - effectively removing an entire attack surface. Our hypothesis is that malicious packages avoid attributing a source to minimize visibility, while benign packages seek exposure and therefore link to their repositories. We evaluate this through three research questions: how effective is building from source at avoiding malware, how feasible is it to build from source in the current state of the supply chain, and how the building from source strategy compares to state-of-the-art security scanners. To do so, we (1) construct an aggregate dataset of 47,066 malicious packages from four academic and one industry malicious datasets source across npm and PyPI ecosystems, (2) build a snapshot index of all 3.9 million npm and 764 thousand PyPI packages, and (3) analyze all malicious samples alongside a statistically significant sample of 16,569 packages from each registry, totaling 33,138 packages. We find that simply checking for a source has a success rate between 87.7 and 99.9 percent of preventing malware, depending on the level of source vetting. Consequently, there is a significant lack of transparency within benign packages, with only 47.4 percent attributing a source an end user could build from.
Source:https://arxiv.org/abs/2610.28794PreviousIntegrating Flow Matching and Model Predictive Control for Safe Robot NavigationNextHow Much Does the Harness Matter, and for Whom? A Multi-Model Study of Context in LLM-Driven Vulnerability Discovery
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