Two Supply Chains, One Artifact: Why Format Safety, Signing and Backdoor Detection Do Not Compose Into an Integrity Claim About a Model

@misc{mahendrakar2026two,
  author       = {Mahendrakar, Pranay},
  title        = {{Two Supply Chains, One Artifact: Why Format Safety, Signing and Backdoor Detection Do Not Compose Into an Integrity Claim About a Model}},
  year         = {2026},
  month        = sep,
  date         = {2026-09-08},
  publisher    = {Life of Research},
  doi          = {10.5281/zenodo.22651383},
  url          = {https://lifeofresearch.com/articles/27},
  abstract     = {(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A model downloaded from a public hub is the target of two distinct defensive programmes that use the same vocabulary and secure different things. One treats the artifact as an executable: it scans serialized files for dangerous deserialization opcodes, migrates the ecosystem to code-free tensor formats, signs releases, and attaches provenance metadata. The other treats the artifact as a learned function: it searches for triggers that flip behaviour, for poisoned training data that planted them, and for weights that were edited to install them. Both programmes call their object a supply chain, both call their output integrity, and a checkpoint that passes every check in the first is compatible with an arbitrary backdoor under the second. This paper argues that the two defence stacks do not compose into a joint claim, and that the reason is structural rather than a coverage gap further engineering will close. Four independent obstacles are separated: the stacks quantify over different objects, so their negatives cannot be conjoined into a statement about the model; the code-versus-weights partition leaks in both directions, since executable payloads have been embedded in parameters and semantic backdoors placed in architecture code; a signature binds bytes to an identity and cannot reach how those bytes were trained, and the mechanism proposed to close that gap has been spoofed twice, the second time by an author group overlapping with its inventors; and the checked artifact is frequently quantized, merged or adapted before it is run, which invalidates the signature and, on published evidence, the backdoor verdict too. The paper states what each stack establishes at its own declared scope, sets out the resulting threat-model matrix cell by cell, and specifies the four predicates an integrity claim about a model would have to assert separately. No experiments are reported, and what is not known is stated flatly, including that the joint failure has not been measured end to end. The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every citation was machine-verified against the arXiv API and Crossref before inclusion, and every quantitative claim was read back against the cited source's own abstract or, where a claim is drawn from a paper's body, against the located passage. The author is responsible for the final text and for all claims made in it.},
  keywords     = {machine learning supply chain, model hubs, backdoor detection, deserialization, provenance, code signing, threat modelling, proof-of-learning},
  language     = {en},
  copyright    = {Creative Commons Attribution 4.0}
}