CVE-2026-12570

Published: Ago 10, 2026 Last Modified: Ago 10, 2026
ExploitDB:
Other exploit source:
Google Dorks:
MEDIUM 5,5
Attack Vector: local
Attack Complexity: low
Privileges Required: none
User Interaction: required
Scope: unchanged
Confidentiality: none
Integrity: none
Availability: high

Description

AI Translation Available

A vulnerability in keras-team/keras versions <= 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.

EPSS (Exploit Prediction Scoring System)

Trend Analysis

EPSS (Exploit Prediction Scoring System)

Prevede la probabilità di sfruttamento basata su intelligence sulle minacce e sulle caratteristiche della vulnerabilità.

EPSS Score
0,0013
Percentile
0,0th
Updated

Single Data Point

Only one EPSS measurement is available for this CVE. Trend analysis requires multiple data points over time.

770

Allocation of Resources Without Limits or Throttling

Incomplete
Common Consequences
Security Scopes Affected:
Availability
Potential Impacts:
Dos: Resource Consumption (Cpu) Dos: Resource Consumption (Memory) Dos: Resource Consumption (Other)
Applicable Platforms
All platforms may be affected
View CWE Details
https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8
https://github.com/keras-team/keras/commit/4933ea4a5b3fcc24ceacdc276f5bb5dfbd06…
https://huntr.com/bounties/a064f475-780a-409a-82f7-678512f27ad8