vulnerability / NVI-00018497 / Revision 1
Keras vulnerable to DoS via Malicious .keras Model (HDF5 Shape Bomb Causes Petabyte Allocation in KerasFileEditor)
GitHub lists this reviewed advisory for keras, keras. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.
This report has not inspected your inventory, configuration, deployed build, reachability or evidence. A catalog listing and a severity score do not establish exposure or compromise.
01 / Meaning
Why it matters
Published affected context helps an owner decide what evidence to gather. This report has not examined your application.
02 / Applicability
The affected context
Affected versions, configuration prerequisites and known exclusions are public. Confirm each one against the deployed service.
keras
source listed>= 3.0.0, <= 3.12.0
### Summary Keras’s model loader (KerasFileEditor) unsafely loads user-supplied .keras model files containing HDF5-based weight files without performing any validation on HDF5 dataset metadata. An attacker can craft a .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape (e.g. (50_000_000, 50_000_000)), but stores only a few bytes. The .keras file remains small (100–400 KB) because HDF5 with gzip compression stores minimal data. During model loading, Keras executes: `python result[key] = value[()] # loads entire dataset into memory` value[()] instructs h5py to allocate RAM proportional to the dataset’s declared shape – in this case 8.88 PiB of memory. This results in: Immediate memory exhaustion Python / TensorFlow crashes Jupyter kernel kill System instability Full Denial of Service on any workload that processes untrusted .keras models This allows an attacker to crash any environment or pipeline that loads .keras models, including MLOps backends, training services, model upload endpoints, or automated pipelines. ### Proof of Concept ``` // PoC.py import zipfile import io import h5py import numpy as np from keras.saving import K
keras
source listed>= 3.13.0, < 3.13.2
### Summary Keras’s model loader (KerasFileEditor) unsafely loads user-supplied .keras model files containing HDF5-based weight files without performing any validation on HDF5 dataset metadata. An attacker can craft a .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape (e.g. (50_000_000, 50_000_000)), but stores only a few bytes. The .keras file remains small (100–400 KB) because HDF5 with gzip compression stores minimal data. During model loading, Keras executes: `python result[key] = value[()] # loads entire dataset into memory` value[()] instructs h5py to allocate RAM proportional to the dataset’s declared shape – in this case 8.88 PiB of memory. This results in: Immediate memory exhaustion Python / TensorFlow crashes Jupyter kernel kill System instability Full Denial of Service on any workload that processes untrusted .keras models This allows an attacker to crash any environment or pipeline that loads .keras models, including MLOps backends, training services, model upload endpoints, or automated pipelines. ### Proof of Concept ``` // PoC.py import zipfile import io import h5py import numpy as np from keras.saving import K
03 / Response
What to do next
- investigate
Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.
Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system. GitHub Advisory Database
A fixed release is not deployment evidence.
Check current vendor support and compatibility. After making a change, verify the deployed build and relevant runtime conditions. Keep the evidence, owner and time. An updated lockfile alone does not prove what is running.
04 / Limits
What this report cannot establish
- Customer inventory, deployment, reachability and exploitation have not been inspected.
05 / Provenance
Sources you can inspect
Each assertion retains its source. Mirrored records may repeat one underlying publisher; additional links are not independent corroboration.
- GHSA-mgx6-5cf9-rr43 ↗
GitHub Advisory Database · vendor advisory
Observed . GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.
Public revision history
Only published revisions appear here. Routine fetches do not create a material-change event.
- Revision 1 ·
First indexed by Null; source disclosure date is shown separately.