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VulnerabilityPublished Updated

CVE-2025-69893: Trezor One · Covert Timing Channel

Source CVE Database V5

0

Not known to be exploited · CVSS 4.6 Medium.

Data as of 26 Oct 2026, 17:16 UTC

CVSS 3.1 base score
4.6Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.2% chance of exploitation in 30 days – higher than 15% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:P/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 14 Apr 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.

What to do

AI-suggested

The vulnerability has been patched by the vendor. Users should update their Trezor hardware wallets to versions later than 1.14.0 where this issue is fixed. Since this is a hardware wallet vulnerability requiring physical access during setup, updating firmware is the primary mitigation. Patch status is not explicitly stated but the description confirms the issue was patched; users should verify with the vendor for the official fixed versions.

Description

A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant time execution and specific branch patterns for word searching. An attacker with physical access during the initial setup phase can collect a single side-channel trace. By utilizing profiling-based Deep Learning Side-Channel Analysis (DL-SCA), the attacker can recover the mnemonic code and subsequently steal the assets. The issue was patched.

References

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