GHSA-mq84-hjqx-cwf2 CVE-2025-12058 GHSA-qg93-c7p6-gg7f PYSEC-2026-1487
Keras is vulnerable to arbitrary local file loading and Server-Side Request Forgery
- Severity
- 0.0 / 10
- Medium · CVSS 4.0
- Exploit chance (EPSS)
- 0.25%
- Chance of exploitation in the next 30 days. Higher than 16.14% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Fix
3.12.0- Upgrade keras to this version or later
Description
The Keras.Model.load_model method, including when executed with the intended security mitigation safe_mode=True, is vulnerable to arbitrary local file loading and Server-Side Request Forgery (SSRF).
This vulnerability stems from the way the StringLookup layer is handled during model loading from a specially crafted .keras archive. The constructor for the StringLookup layer accepts a vocabulary argument that can specify a local file path or a remote file path.
-
Arbitrary Local File Read: An attacker can create a malicious .keras file that embeds a local path in the StringLookup layer's configuration. When the model is loaded, Keras will attempt to read the content of the specified local file and incorporate it into the model state (e.g., retrievable via get_vocabulary()), allowing an attacker to read arbitrary local files on the hosting system.
-
Server-Side Request Forgery (SSRF): Keras utilizes tf.io.gfile for file operations. Since tf.io.gfile supports remote filesystem handlers (such as GCS and HDFS) and HTTP/HTTPS protocols, the same mechanism can be leveraged to fetch content from arbitrary network endpoints on the server's behalf, resulting in an SSRF condition.
The security issue is that the feature allowing external path loading was not properly restricted by the safe_mode=True flag, which was intended to prevent such unintended data access.
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| keras | < 3.12.0 |
3.12.0 |
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 → 3.12.0
- pip
pip install --upgrade "keras>=3.12.0"- poetry
poetry add "keras>=3.12.0"- uv
uv add "keras>=3.12.0"
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:A/AC:H/AT:P/PR:L/UI:P/VC:H/VI:L/VA:L/SC:H/SI:L/SA:L/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
- Attack vector
- Adjacent
- Attack complexity
- High
- Attack requirements
- Present
- Privileges required
- Low
- User interaction
- Passive
- Confidentiality (vulnerable system)
- High
- Integrity (vulnerable system)
- Low
- Availability (vulnerable system)
- Low
- Confidentiality (subsequent systems)
- High
- Integrity (subsequent systems)
- Low
- Availability (subsequent systems)
- Low
- Exploit maturity
- Not defined
- Scope
- X
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].
CWE-918: Server-Side Request Forgery (SSRF)
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
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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