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Low 32 Bitcoin

feat(core): Use randomness from Tropic to generate secrets.

Public commit record

What the developer wrote

Authored by Andrew Kozlik

67/100 · Adequate
feat(core): Use randomness from Tropic to generate secrets.

[no changelog]
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Uses a recognizable type or scope✓ Names security-relevant behavior explicitly! No meaningful explanatory body
The short version

What changed, and why it matters

This commit changes how secret keys are generated during Trezor device production testing. Previously, randomness came from the device's own RNG and (on some models) an Infineon OPTIGA chip. The patch adds support for also mixing in randomness from a new 'Tropic' secure chip, and ensures a factory secure session with Tropic is started before secrets are generated. This is a feature addition, not a vulnerability fix. It improves entropy quality but introduces a new hardware dependency and a TODO-removal that should be reviewed for correctness.

Recommended action

Review the new Tropic integration for correctness: confirm `lt_random_value_get` and `prodtest_tropic_factory_session_start` error handling, verify the factory pairing key lifecycle matches the provisioning stage, and ensure the shared `buffer` cannot leak partial randomness if one branch fails before `memzero`. Consider whether this change warrants a changelog entry despite the [no changelog] tag, because it affects production secret generation.

Security signals we found

01

New hardware RNG source integrated into secret generation

02

Removal of a TODO placeholder for Tropic randomness

03

Addition of factory secure-session precondition before secret generation

04

Shared buffer reuse between OPTIGA and Tropic branches (non-overlapping macros, but same variable name)

05

Potential failure mode: if Tropic session start or randomness read fails, secret generation aborts

Risk score

Why this scored 32/100

Our methodology →
Potential impact 8/30
Exploitability 5/25
Stealth signal 4/15
Affected reach 6/15
Confidence 6/10
Evidence quality 3/5
Human-validated context

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