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CVE-2025-8747VulnerabilityPublished Updated

CVE-2025-8747: Google Keras · Deserialization of Untrusted Data

Affects Google KerasSource CVE Database V5

0

Not known to be exploited · CVSS 8.6 High.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 4.0 base score
8.6High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.1% chance of exploitation in 30 days – higher than 1% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:P/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/AU:Y/R:A

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 11 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
Google KerasAffected
  • =3.0.0
Fixed in–

AI-suggested

To mitigate CVE-2025-8747, organizations should implement strict controls on the sources of .keras model files, ensuring only trusted and verified models are loaded. Employing digital signatures or cryptographic hashes to validate model integrity before loading can prevent malicious models from being accepted. Users should be trained to recognize the risks of loading models from untrusted sources and to avoid executing unverified code embedded in model files. Until official patches are released, consider isolating environments where models are loaded, such as using containerization or sandboxing techniques, to limit the impact of potential exploitation. Monitoring and logging model loading activities can help detect suspicious behavior. Additionally, organizations should track updates from Google and apply security patches promptly once available. Reviewing and restricting permissions of users who load models can reduce the attack surface. Finally, integrating security reviews into the ML model lifecycle, including code and artifact audits, can help identify and mitigate risks early.

Description

A safe mode bypass vulnerability in the `Model.load_model` method in Keras versions 3.0.0 through 3.10.0 allows an attacker to achieve arbitrary code execution by convincing a user to load a specially crafted `.keras` model archive.

References

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