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Moderate 51 Bitcoin

Clear the output buffer on failures in merkle-tree related functions

Public commit record

What the developer wrote

Authored by Salvatore Ingala

73/100 · Adequate
Clear the output buffer on failures in merkle-tree related functions

As a defense-in-depth measure, we make sure that the output buffers are zeroed on
failures, preventing the attacker from leaving host-chosen content on uninitialized
buffers.
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context
The short version

What changed, and why it matters

This commit hardens Ledger's Bitcoin app so that when it asks the host computer for Merkle-tree data and the request fails or the data is rejected, the memory buffer that was supposed to receive the data is wiped clean with zeros. Before the change, an attacker-controlled host could leave chosen bytes in that buffer even after the app decided the data was invalid. The patch is a defense-in-depth measure; it does not by itself fix a known exploitable bug, but it removes a class of subtle mistakes where later code might accidentally trust leftover hostile data.

Recommended action

Treat as a worthwhile hardening patch. Review all callers of the affected functions to confirm they check return codes before using output buffers, since zeroed buffers are not a substitute for status checking. Include the new unit tests in CI. No urgent incident response is indicated absent evidence of a reachable exploit chain.

Security signals we found

01

explicit_bzero added to clear output buffers on failure paths

02

Host-supplied data is streamed into buffers before hash/Merkle verification

03

New unit tests verify buffer clearing on failure, including MAP_VALUE_ABSENT

04

Commit message frames change as defense-in-depth against uninitialized/host-chosen buffer content

05

No CVE, advisory, or independent researcher attribution present in commit

Risk score

Why this scored 51/100

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

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