No Snake Oil: Verifying Python Package Builds

yruzin2 pts0 comments

[2607.21888] No Snake Oil: Verifying Python Package Builds

Skip to main content

Search arXiv

Press Enter to search · Advanced search

-->

Computer Science > Software Engineering

arXiv:2607.21888 (cs)

[Submitted on 24 Jul 2026]

Title:No Snake Oil: Verifying Python Package Builds

Authors:Jens Dietrich, Spencer Sun, Tim W. White, Behnaz Hassanshahi<br>View a PDF of the paper titled No Snake Oil: Verifying Python Package Builds, by Jens Dietrich and 3 other authors

View PDF<br>HTML (experimental)

Abstract:Python has become the default language for interacting with AI, with packages being distributed through registries like the Python Package Index (PyPI). This creates a need to analyse supply chains comprising such packages. One such analysis is to rebuild packages in order to identify compromised builds injecting malware. Independent rebuilds in hardened environments have the added advantage that they can generate and record provenance in order to increase the trustworthiness of packages. Two tools that are designed to automate such rebuilds and run them at scale are macaron and oss-rebuild. We study 12,180 popular releases from PyPI and find that the byte-for-byte equivalence rate is generally low. We analyse the reasons why they produce different wheels, and find that equivalence between the original and rebuilt wheels can often still be established, preserving most of the guarantees users expect from rebuildable releases. We present and evaluate daleq4py, a tool to establish the equivalence of Python wheels through the kernel of a normalisation function that is based on provenance-preserving datalog rules. Experimental results show that daleq4py substantially expands the set of rebuilds that can be accepted as equivalent. Although only 15.4% of macaron rebuilds and 19.1% of oss-rebuild rebuilds are byte-for-byte identical to the published PyPI wheels, daleq4py establishes wheel equivalence for 60.2% and 78.9% of source-equivalent rebuilds, respectively.

Comments:<br>11 pages, 2 figures

Subjects:

Software Engineering (cs.SE); Cryptography and Security (cs.CR)

ACM classes:<br>D.4.6; D.2.9; D.2.7

Cite as:<br>arXiv:2607.21888 [cs.SE]

(or<br>arXiv:2607.21888v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2607.21888

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Jens Dietrich [view email]<br>[v1]<br>Fri, 24 Jul 2026 01:31:24 UTC (68 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled No Snake Oil: Verifying Python Package Builds, by Jens Dietrich and 3 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.SE

next >

new<br>recent<br>| 2026-07

Change to browse by:

cs<br>cs.CR

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)

Major funding support from

toggle arxiv python package builds view

Related Articles