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CVE-2025-30404VulnerabilityPublished Updated

CVE-2025-30404: 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 25 Oct 2026, 19:02 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 2026-10-25.

What to do

Affected software

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

AI-suggested

1. Immediate mitigation involves monitoring for updates from Meta Platforms and applying patches or commits that address the integer overflow vulnerability as soon as they become available. 2. Until patches are released, organizations should implement strict network segmentation and firewall rules to limit exposure of ExecuTorch services to untrusted networks. 3. Employ runtime application self-protection (RASP) and memory protection mechanisms such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) to reduce exploitation success. 4. Conduct thorough code audits and static analysis on AI model loading components to detect anomalous memory allocation patterns. 5. Implement anomaly detection systems to monitor for unusual behavior in AI model execution environments, including unexpected crashes or memory corruption indicators. 6. Restrict the use of untrusted or third-party AI models within ExecuTorch to minimize attack surface. 7. Maintain comprehensive logging and incident response plans tailored to AI infrastructure to enable rapid detection and containment of potential exploitation attempts.

Description

An integer overflow vulnerability in the loading of ExecuTorch models can cause overlapping allocations, potentially resulting in code execution or other undesirable effects. This issue affects ExecuTorch prior to commit d158236b1dc84539c1b16843bc74054c9dcba006.

References

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