CVE-2025-1550: Google Keras · Code Injection
Affects Google KerasSource CVE Database V5
Not known to be exploited · public exploit available (Exploit-DB) · CVSS 7.3 High.
Data as of 26 Oct 2026, 06:26 UTC
- EPSS
- 2.6% chance of exploitation in 30 days – higher than 85% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:HExploitation evidence
t = 0 is the CVE’s publication on cve.org
Disclosed: evidence found.
CVE published 11 Mar 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: evidence found.
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
Timeline
CVE ID reserved
by Google
, 18 days before disclosure
CVE published
on cve.org
, t = 0
First Exploit-DB entry (opens in a new tab)
Keras 2.15 - Remote Code Execution (RCE)
, 127 days after disclosure
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
PyPIkeraspkg:pypi/kerasOther threats | Affected
| Fixed in– |
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
European organizations should implement several specific mitigations beyond generic advice: 1) Avoid loading Keras models from untrusted or unauthenticated sources. Establish strict provenance and integrity verification for all model files, including cryptographic signatures or checksums. 2) Implement sandboxing or containerization for environments where models are loaded, limiting the impact of potential code execution. 3) Monitor and restrict the use of Keras 3.0.0 in production environments until a patch is available. Consider downgrading to earlier, unaffected versions if feasible. 4) Enhance user training and awareness to prevent inadvertent loading of malicious models, especially in data science and ML teams. 5) Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) tools to detect anomalous behavior during model loading. 6) Collaborate with vendors and the open-source community to track patch releases and apply updates promptly once available. 7) Review and harden access controls to limit who can load or deploy models, minimizing the risk of exploitation via insider threats or compromised credentials.
Description
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.