GHSA-xfhx-r7ww-5995
Duplicate Advisory: Google Keras Allocates Resources Without Limits or Throttling in the HDF5 weight loading component
High
Withdrawn May 6, 2026
PyPI
- Severity
- 0.0 / 10
- High · CVSS 4.0
- Exploit chance (EPSS)
- —
- No score published
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-770 Allocation of Resources Without Limits or Throttling
- Fix
3.12.1- Upgrade keras to this version or later
Description
Duplicate Advisory
This advisory has been withdrawn because it is a duplicate of GHSA-mgx6-5cf9-rr43. This link is maintained to preserve external references.
Original Description
Allocation of Resources Without Limits or Throttling in the HDF5 weight loading component in Google Keras 3.0.0 through 3.12.0 and 3.13.0 on all platforms allows a remote attacker to cause a Denial of Service (DoS) through memory exhaustion and a crash of the Python interpreter via a crafted .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape.
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 → 3.12.1
- pip
pip install --upgrade "keras>=3.12.1"- poetry
poetry add "keras>=3.12.1"- uv
uv add "keras>=3.12.1"
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/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)
- None
- Integrity (vulnerable system)
- None
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- None
- Integrity (subsequent systems)
- None
- Availability (subsequent systems)
- None
Weakness
CWE-770: Allocation of Resources Without Limits or Throttling
The product allocates a reusable resource or group of resources on behalf of an actor without imposing any intended restrictions on the size or number of resources that can be allocated.
How to prevent it
- Requirements: Clearly specify the minimum and maximum expectations for capabilities, and dictate which behaviors are acceptable when resource allocation reaches limits.
- Architecture and Design: Limit the amount of resources that are accessible to unprivileged users. Set per-user limits for resources. Allow the system administrator to define these limits. Be careful to avoid CWE-410.
- Architecture and Design: Design throttling mechanisms into the system architecture. The best protection is to limit the amount of resources that an unauthorized user can cause to be expended. A strong authentication and access control model will help prevent such attacks from occurring in the first place, and it will help the administrator to identify who is committing the abuse. The login application should be protected against DoS attacks as much as possible. Limiting the database access, perhaps by caching result sets, can help minimize the resources expended. To further limit the potential for a DoS attack, consider tracking the rate of requests received from users and blocking requests that exceed a defined rate threshold.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
- Architecture and Design: Mitigation of resource exhaustion attacks requires that the target system either: recognizes the attack and denies that user further access for a given amount of time, typically by using increasing time delays uniformly throttles all requests in order to make it more difficult to consume resources more quickly than they can again be freed. The first of these solutions is an issue in itself though, since it may allow attackers to prevent the use of the system by a particular valid user. If the attacker impersonates the valid user, they may be able to prevent the user from accessing the server in question. The second solution can be difficult to effectively institute -- and even when properly done, it does not provide a full solution. It simply requires more resources on the part of the attacker.
- Architecture and Design: Ensure that protocols have specific limits of scale placed on them.
- Architecture and Design, Implementation: If the program must fail, ensure that it fails gracefully (fails closed). There may be a temptation to simply let the program fail poorly in cases such as low memory conditions, but an attacker may be able to assert control before the software has fully exited. Alternately, an uncontrolled failure could cause cascading problems with other downstream components; for example, the program could send a signal to a downstream process so the process immediately knows that a problem has occurred and has a better chance of recovery. Ensure that all failures in resource allocation place the system into a safe posture.
- Operation, Architecture and Design: Use quotas or other resource-limiting settings provided by the operating system or environment. For example, when managing system resources in POSIX, setrlimit() can be used to set limits for certain types of resources, and getrlimit() can determine how many resources are available. However, these functions are not available on all operating systems. When the current levels get close to the maximum that is defined for the application (see CWE-770), then limit the allocation of further resources to privileged users; alternately, begin releasing resources for less-privileged users. While this mitigation may protect the system from attack, it will not necessarily stop attackers from adversely impacting other users. Ensure that the application performs the appropriate error checks and error handling in case resources become unavailable (CWE-703).
References
Advisories
Packages
Other links
Timeline
- Published
- Withdrawn
- Last updated by the source
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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