Least Authority reviewed three layers of the White Noise stack:
- the November 2025 Marmot protocol review;
- the March 2026 MDK audit;
- the April 2026 White Noise backend audit.
Protocol review
The protocol review accepted MLS’s underlying forward-secrecy and post-compromise-security properties when correctly integrated. It was explicitly not a complete analysis of every underlying subsystem. The reviewers recommended a full-stack review, a detailed threat model, clear authentication-service assumptions, key compartmentalization, and stronger analysis of public Nostr metadata.
The review specifically noted that public group tags, network observations, and relay-visible IP addresses could reveal affiliations, timing, activity, or links between ephemeral identities.
Implementation audits
The MDK audit initially found critical failures in group authorization, sender binding, Nostr-to-MLS identity binding, and local MLS-state protection. The March verification marked the critical findings resolved. It left one high-severity finding unresolved: default reusable last-resort KeyPackages can enable reuse, unwanted enrollments, resource consumption, and correlation across groups.
The White Noise backend audit covered group lifecycle, Nostr event handling, encrypted media, account switching, and relay-state convergence. Most findings were verified as resolved. Dependency updating remained partially resolved, including a denial-of-service advisory in a transitive transport dependency; the reported unfixed RSA path was not used by the SQLite deployment.
Interpretation
The audits increase confidence in the reviewed revisions, but they do not convert the young stack into a mature equivalent of Signal. They show both that serious review occurred and that composing sound MLS cryptography with Nostr created consequential application-layer failure modes.
The findings inform White Noise, Marmot Protocol, and Secure and decentralized communication stacks.
Built on 3 sources (3 external).
Working out connections…
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Model contributions
Measured by git-blame lines per AI model (72 total).
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