keras CVE Vulnerabilities & Metrics

Focus on keras vulnerabilities and metrics.

Last updated: 01 Aug 2025, 22:25 UTC

About keras Security Exposure

This page consolidates all known Common Vulnerabilities and Exposures (CVEs) associated with keras. We track both calendar-based metrics (using fixed periods) and rolling metrics (using gliding windows) to give you a comprehensive view of security trends and risk evolution. Use these insights to assess risk and plan your patching strategy.

For a broader perspective on cybersecurity threats, explore the comprehensive list of CVEs by vendor and product. Stay updated on critical vulnerabilities affecting major software and hardware providers.

Global CVE Overview

Total keras CVEs: 1
Earliest CVE date: 11 Mar 2025, 09:15 UTC
Latest CVE date: 11 Mar 2025, 09:15 UTC

Latest CVE reference: CVE-2025-1550

Rolling Stats

30-day Count (Rolling): 0
365-day Count (Rolling): 1

Calendar-based Variation

Calendar-based Variation compares a fixed calendar period (e.g., this month versus the same month last year), while Rolling Growth Rate uses a continuous window (e.g., last 30 days versus the previous 30 days) to capture trends independent of calendar boundaries.

Variations & Growth

Month Variation (Calendar): 0%
Year Variation (Calendar): 0%

Month Growth Rate (30-day Rolling): 0.0%
Year Growth Rate (365-day Rolling): 0.0%

Monthly CVE Trends (current vs previous Year)

Annual CVE Trends (Last 20 Years)

Critical keras CVEs (CVSS ≥ 9) Over 20 Years

CVSS Stats

Average CVSS: 0.0

Max CVSS: 0

Critical CVEs (≥9): 0

CVSS Range vs. Count

Range Count
0.0-3.9 1
4.0-6.9 0
7.0-8.9 0
9.0-10.0 0

CVSS Distribution Chart

Top 5 Highest CVSS keras CVEs

These are the five CVEs with the highest CVSS scores for keras, sorted by severity first and recency.

All CVEs for keras

CVE-2025-1550 keras vulnerability CVSS: 0 11 Mar 2025, 09:15 UTC

The Keras Model.load_model function permits arbitrary code execution, even with safe_mode=True, through a manually constructed, malicious .keras archive. By altering the config.json file within the archive, an attacker can specify arbitrary Python modules and functions, along with their arguments, to be loaded and executed during model loading.