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Deserialization of Untrusted DataWeakness · CWE-502

CVE-2025-49655

CRITICAL · 9.8 CVSS v3.1 Published 2025-10-17
Patch available
A vendor patch is available. No clean upgrade release — apply the published patch.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click Patch available

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
Deserialization of untrusted data can occur in versions of the Keras framework running versions 3.11.0 up to but not including 3.11.3, enabling a maliciously uploaded Keras file containing a TorchModuleWrapper class to run arbitrary code on an end user’s system when loaded despite safe mode being enabled. The vulnerability can be triggered through both local and remote files.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

Deserialization vulnerability in Keras 3.11.0-3.11.2 allows arbitrary code execution via maliciously crafted Keras model files containing a TorchModuleWrapper class, bypassing the framework's safe mode protection. The vulnerability exists in the deserialization logic that handles the TorchModuleWrapper during model loading.

MitigationUpgrade to Keras 3.11.3 or later to patch the vulnerability. Avoid loading Keras model files from untrusted sources and implement file validation before deserialization.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify installed Keras version
    Run 'python -c "import keras; print(keras.__version__)"' or 'pip show keras' to see the installed version
    Affected if The version is 3.11.0, 3.11.1, or 3.11.2
  2. Check for PyTorch integration usage
    Inspect code for imports of keras.integration.torch or usage of TorchModuleWrapper class in model loading pipelines
    Affected if The codebase uses TorchModuleWrapper or loads models that may contain serialized TorchModuleWrapper objects
  3. Verify source of Keras model files
    Review all model loading calls such as keras.models.load_model() and keras.saving.load_model() to determine if any load from untrusted or external sources
    Affected if Models are loaded from untrusted sources, user input, or files of unknown origin
  4. Inspect safe deserialization configuration
    Check if safe_mode=True is enforced in all model loading operations. Search for load_model() calls without explicit safe_mode parameter
    Affected if safe_mode is disabled or not explicitly set to True in model loading code, since the vulnerability bypasses this protection

You are affected if Keras version is 3.11.0-3.11.2 AND you load Keras model files (especially those involving PyTorch integration or TorchModuleWrapper) from untrusted or external sources with safe_mode disabled or not enforced.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Patch available Apply the vendor patch
Vendor patch github.com →
Interim mitigation

Upgrade to Keras 3.11.3 or later to patch the vulnerability. Avoid loading Keras model files from untrusted sources and implement file validation before deserialization.

Recommended fix High confidence

keras>=3.11.3

  1. Identify the current Keras version by running 'pip show keras' or 'import keras; print(keras.__version__)
  2. Upgrade Keras to version 3.11.3 or later by running: pip install --upgrade keras
  3. Verify the upgrade was successful by running: pip show keras and confirming the version is 3.11.3 or higher
  4. If using a requirements file, update the keras version constraint to 'keras>=3.11.3'
  5. If using TensorFlow (which bundles Keras), ensure TensorFlow is also updated to a version that includes the fixed Keras, or install the standalone Keras package with: pip install --upgrade tf-keras
Caveat No breaking changes expected for this patch upgrade; this is a security fix

Generated from the published advisory — verify against the referenced sources before acting.

Have this fixed Scoped from the published advisory
  • Consultation3.0 h
  • Implementation2.0 h
  • Testing4.0 h
  • Review / QA2.0 h
11.0 hours of engineering $1,920
Get the patch applied

An estimate, not a bill — we confirm scope with you before any work starts. Need it this week? Rush from $3,072.

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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2025-49655 in production — separate from our analysis above.

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What this is

A place for practitioners to share what actually worked: a mitigation you’ve tested, a configuration change, a version- or environment-specific caveat, or a link to a verified patch. The most useful notes rise to the top as peers upvote them, so the signal stays high.

What belongs here
  • Verified mitigations, workarounds, and config changes
  • Version or environment caveats, and links to real fixes
  • No weaponised exploit code, or anything meant to cause harm
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