GHSA-4f3f-g24h-fr8m CVE-2026-1462 PYSEC-2026-2547
Keras has an untrusted deserialization vulnerability
- Severity
- 8.8 / 10
- High · CVSS 3.0
- Exploit chance (EPSS)
- 0.40%
- Chance of exploitation in the next 30 days. Higher than 34.75% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-502 Deserialization of Untrusted Data
- Fix
3.13.2- Upgrade keras to this version or later
Description
A vulnerability in the TFSMLayer class of the keras package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of .keras models, even when safe_mode=True. This bypasses the security guarantees of safe_mode and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the from_config() method.
Affected versions
PyPI
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
keras PyPI → 3.13.2
- pip
pip install --upgrade "keras>=3.13.2"- poetry
poetry add "keras>=3.13.2"- uv
uv add "keras>=3.13.2"
Severity breakdown
CVSS 3.0 8.8
CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- Required
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
Weakness
CWE-502: Deserialization of Untrusted Data
The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
How to prevent it
- Architecture and Design, Implementation: If available, use the signing/sealing features of the programming language to assure that deserialized data has not been tainted. For example, a hash-based message authentication code (HMAC) could be used to ensure that data has not been modified.
- Implementation: When deserializing data, populate a new object rather than just deserializing. The result is that the data flows through safe input validation and that the functions are safe.
- Implementation: Explicitly define a final object() to prevent deserialization.
- Architecture and Design, Implementation: Make fields transient to protect them from deserialization. An attempt to serialize and then deserialize a class containing transient fields will result in NULLs where the transient data should be. This is an excellent way to prevent time, environment-based, or sensitive variables from being carried over and used improperly.
- Implementation: Avoid having unnecessary types or gadgets (a sequence of instances and method invocations that can self-execute during the deserialization process, often found in libraries) available that can be leveraged for malicious ends. This limits the potential for unintended or unauthorized types and gadgets to be leveraged by the attacker. Add only acceptable classes to an allowlist. Note: new gadgets are constantly being discovered, so this alone is not a sufficient mitigation.
- Architecture and Design, Implementation: Employ cryptography of the data or code for protection. However, it's important to note that it would still be client-side security. This is risky because if the client is compromised then the security implemented on the client (the cryptography) can be bypassed.
- Operation: Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481].
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-74m6-m3xx-3vmj Keras model loading is vulnerable to denial of service through HDF5 shape bombs | Medium | Also affects keras |
| GHSA-m8wh-29wm-52mv Keras: HDF5 links can disclose local file contents | Medium | Also affects keras |
| GHSA-v2w2-w228-c444 Keras: TorchModuleWrapper can deserialize unsafe PyTorch pickle data | High | Also affects keras |
| GHSA-58hv-7753-xmfq Keras: tar extraction permits symlink-based path traversal | Medium | Also affects keras |
| GHSA-5gwj-m78q-7pq3 Keras: Lambda deserialization can bypass safe mode and execute code | Critical | Also affects keras |
| GHSA-26c4-7vv6-867j Keras: HDF5 virtual datasets can disclose local files | Medium | Also affects keras |
| GHSA-gh82-f9x8-5frx Keras: DiskIOStore permits path traversal through crafted layer names | Medium | Also affects keras |
| GHSA-hqp4-2352-xf5r Keras archive extraction utilities allow path traversal and arbitrary file writes | High | Also affects keras |
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