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Informational 15 Bitcoin

Split fuzz runners by hash mode

Public commit record

What the developer wrote

Authored by Joost Jager

78/100 · Adequate
Split fuzz runners by hash mode

Move shared fuzz logic into the root fuzz crate and generate
fake-hashes and real-hashes runner crates.

Keep `chanmon_consistency_target` on the real-hashes side, remove
the fuzz-local Cargo config, and update scripts, CI, coverage,
and docs to use explicit flags for each runner.

Generate the hash-mode compile checks in the wrapper bins
without a synthetic Cargo feature, while keeping the wrapper
template close to its original shape.

AI tools were used in preparing this commit.
✓ Descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context✓ Mentions testing or verification
The short version

What changed, and why it matters

This commit is a build-system and test-infrastructure refactor for the project's fuzzing (automated randomized testing) harness. It splits fuzz test runners into two groups based on whether they use fake or real cryptographic hashes, moves shared code into a common crate, and updates CI scripts, coverage tooling, and documentation accordingly. There is no change to the actual Lightning protocol library or to how production nodes behave, and no security vulnerability is introduced or fixed.

Recommended action

No security action required. Reviewers may verify that CI still passes and that the new workspace layout builds correctly with the documented RUSTFLAGS for each runner.

Security signals we found

01

No changes to src/ of the lightning crate or protocol logic

02

No changes to cryptography, serialization, or network handling

03

Only fuzz/ build configuration, generated wrappers, CI, and docs are touched

04

Compile-time cfg guards are added, not removed

05

No advisory, CVE, or vendor security disclosure is referenced

Risk score

Why this scored 15/100

Our methodology →
Potential impact 0/30
Exploitability 0/25
Stealth signal 0/15
Affected reach 0/15
Confidence 10/10
Evidence quality 5/5
Human-validated context

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