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Moderate 59 Cryptographic libraries

Merge pull request #11264

Public commit record

What the developer wrote

Authored by tobtoht

58/100 · Thin
Merge pull request #11264

3125e6d multisig: reject mismatched key image component count (jpk68)

ACKs: selsta, UkoeHB
✓ Descriptive subject✓ Provides an explanatory body✓ Links an issue, advisory, or supporting reference
The short version

What changed, and why it matters

This Monero update tightens validation around multisig key images and imported multisig data. Multisig wallets require several participants to combine pieces (partial key images and signing nonces) to spend funds. The patch makes the wallet reject incomplete or duplicate pieces before it accepts them, instead of silently building a broken key image. It also fixes an incorrect count check used when importing multisig data. The changes are defensive hardening; they do not by themselves show that money could be stolen, but they close paths where malformed or inconsistent data could cause incorrect key images, failed transactions, or potential misuse.

Recommended action

Treat as a security-hardening fix for multisig wallets. Users running multisig Monero wallets should upgrade to a release containing this commit. Wallet developers and integrators should review multisig import/export handling and ensure imported data is validated before use. No immediate emergency response is indicated by the diff alone, but the patch closes real error paths that could affect correctness.

Security signals we found

01

Defensive validation added to multisig key-image composition

02

Import path now rejects duplicate or identity partial key images and duplicate signing nonces

03

Import path validates candidate data before overwriting wallet state

04

Expected partial key image count corrected to use threshold and signer count

05

Unit and core tests added for wrong-component rejection and import validation

Risk score

Why this scored 59/100

Our methodology →
Potential impact 18/30
Exploitability 12/25
Stealth signal 8/15
Affected reach 10/15
Confidence 7/10
Evidence quality 4/5
Human-validated context

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