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Informational 21 Bitcoin

autoclean: don't increment num_cleaned when record wasn't even a candidate.

Public commit record

What the developer wrote

Authored by Rusty Russell

81/100 · Strong
autoclean: don't increment num_cleaned when record wasn't even a candidate.

For example, `autoclean-once failedforwards` would count every non-failed forwards
as "uncleaned".

This is both technically correct and completely useless.

Changelog-Fixed: JSON-RPC: `autoclean-once` returns "uncleaned" number reflecting number of candidates which were too new to be cleaned, not all records we didn't delete.
Fixes: https://github.com/ElementsProject/lightning/issues/8632
Reported-by: @grubles and several other sharp-eyed users.
Signed-off-by: Rusty Russell <rusty@rustcorp.com.au>
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context✓ Links an issue, advisory, or supporting reference
The short version

What changed, and why it matters

This commit fixes a bookkeeping bug in the autoclean-once feature. Previously, when you asked to clean up failed payment forwards, the tool counted every forward that wasn't deleted—including successful forwards that were never supposed to be deleted—as 'uncleaned'. Now it only counts records that were actually candidates for cleanup but were too new to be removed. This is a user-interface/reporting fix, not a security vulnerability.

Recommended action

No security action required. This is a normal bug fix improving API accuracy. Operators relying on autoclean-once reporting may update to v25.12 for clearer metrics.

Security signals we found

01

No security-relevant code path altered

02

Fix is purely informational/statistical in RPC response

03

No memory safety, authentication, authorization, or cryptographic changes

04

No CVE or security advisory referenced in commit

Risk score

Why this scored 21/100

Our methodology →
Potential impact 2/30
Exploitability 0/25
Stealth signal 0/15
Affected reach 5/15
Confidence 9/10
Evidence quality 5/5
Human-validated context

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