Interesting connection. I've been working on a similar problem but from a different angle: detecting anomalous binaries without reproducible builds or signatures, using frequency masks built from a corpus of known-good versions.
The idea is to measure how much of the invariant string or byte structure a new version still covers.
In the context of agents that churn out code overnight, the review fatigue impacting human agents. One way to prioritize review effort is to treat the codebase's own history as a "corpus" and flag commits that deviate structurally from past patterns—like an injection test for code. If an agent adds 20k lines that are mostly boilerplate, the coverage of established code patterns might drop, signalling something worth a closer look. It's not a substitute for tests or semantic verification, but a cheap way to surface outliers.
We tested this on Alpine apk-tools across nine releases: the 3.23 rewrite dropped from 71‑80% string coverage to 40%, and from 23‑28% byte n‑gram coverage to 13.6%—detected automatically just from size distribution. Applied to code, you could imagine a dashboard where every PR gets a "historical coverage" score; when it drops, the human knows to zoom in.
It complements existing verification: a signature tells you who signed, not whether the artifact is consistent with its own version history. Same for AI‑generated PRs: tests may pass, but if the code looks nothing like what came before, that’s a useful signal.
Details here if anyone's curious: https://lf3.gitlab.io/blog/binary-string-mask/