GHSA-36fq-jgmw-4r9c CVE-2025-9906 PYSEC-2025-76

Keras is vulnerable to Deserialization of Untrusted Data

High PyPI

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): 0.20%; Chance of exploitation in the next 30 days. Higher than 10.26% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-502 Deserialization of Untrusted Data
Fix: 3.11.0; Upgrade keras to this version or later

Description

Arbitrary Code Execution in Keras

Keras versions prior to 3.11.0 allow for arbitrary code execution when loading a crafted .keras model archive, even when safe_mode=True.

The issue arises because the archive’s config.json is parsed before layer deserialization. This can invoke keras.config.enable_unsafe_deserialization(), effectively disabling safe mode from within the loading process itself. An attacker can place this call first in the archive and then include a Lambda layer whose function is deserialized from a pickle, leading to the execution of attacker-controlled Python code as soon as a victim loads the model file.

Exploitation requires a user to open an untrusted model; no additional privileges are needed. The fix in version 3.11.0 enforces safe-mode semantics before reading any user-controlled configuration and prevents the toggling of unsafe deserialization via the config file.

Affected versions: < 3.11.0 Patched version: 3.11.0

It is recommended to upgrade to version 3.11.0 or later and to avoid opening untrusted model files.

Affected versions

PyPI

Package	Vulnerable	Fixed in
keras	`< 3.11.0`	`3.11.0`
keras	See the source repository's commits	No fix yet

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 → `3.11.0`

pip

pip install --upgrade "keras>=3.11.0"

poetry

poetry add "keras>=3.11.0"

uv

uv add "keras>=3.11.0"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Attack vector: Network
Attack complexity: Low
Attack requirements: None
Privileges required: None
User interaction: Passive
Confidentiality (vulnerable system): High
Integrity (vulnerable system): High
Availability (vulnerable system): High
Confidentiality (subsequent systems): None
Integrity (subsequent systems): None
Availability (subsequent systems): None

CVSS 3.1 7.3

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

Attack vector: Local
Attack complexity: Low
Privileges required: Low
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

github.com/keras-team/keras

Timeline

Sep 19, 2025 Published
Oct 19, 2026 Last updated by the source
Nov 1, 2026 Latest EPSS score

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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