Skip to main content
CVE-2025-30405VulnerabilityPublished Updated

CVE-2025-30405: Meta Platforms ExecuTorch · Integer Overflow or Wraparound

Affects Meta Platforms ExecuTorchSource CVE Database V5

0

Not known to be exploited · CVSS 9.8 Critical.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.1 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.6% chance of exploitation in 30 days – higher than 49% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 7 Aug 2025

  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 2026-10-26.

What to do

Affected software

ProductAffectedFixed in
Meta Platforms, Inc ExecuTorchAffected
  • =0
Fixed in–

AI-suggested

1. Immediate application of the patch or update containing commit 0830af8207240df8d7f35b984cdf8bc35d74fa73 once it is officially released by Meta Platforms. 2. Until patches are available, restrict network access to ExecuTorch services to trusted internal networks only, employing network segmentation and firewall rules to minimize exposure. 3. Implement runtime application self-protection (RASP) or memory protection mechanisms such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) to mitigate exploitation impact. 4. Conduct thorough code audits and fuzz testing on ExecuTorch model loading routines to detect similar integer overflow issues proactively. 5. Monitor logs and network traffic for anomalous behavior indicative of exploitation attempts, including unexpected process executions or memory access violations. 6. Employ strict input validation and sanitization on model files before loading to prevent malformed or malicious models from triggering the overflow. 7. Develop incident response plans specific to AI/ML infrastructure compromise scenarios to enable rapid containment and recovery.

Description

An integer overflow vulnerability in the loading of ExecuTorch models can cause objects to be placed outside their allocated memory area, potentially resulting in code execution or other undesirable effects. This issue affects ExecuTorch prior to commit 0830af8207240df8d7f35b984cdf8bc35d74fa73.

References

Community reviews

Loading reviews…