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. This vulnerability is classified as high severity, with a CVSS score of 7.3, indicating that it poses a significant risk to organizations utilizing Keras.
Risk to organizations includes potential unauthorized access and execution of malicious code, which can compromise system integrity and confidentiality. The exploitability of this vulnerability is high, necessitating immediate attention from security teams. Organizations should prioritize patching immediately to mitigate the risks associated with this flaw.
The publication date of this vulnerability was March 11, 2025, and it has been analyzed and documented in the CVE database. With no confirmed public exploit available, organizations are still at risk due to the vulnerability's nature and ease of exploitation.
Given the severity and implications of this vulnerability, it is crucial for organizations to assess their current usage of Keras and implement necessary mitigations. Addressing this vulnerability should be a priority in the patch cycle.
Vulnerability Details
The vulnerability affects the Keras framework, specifically the Model.load_model function, which allows arbitrary code execution through maliciously crafted .keras archives. The CVSS score is 7.3, indicating high severity, and it is associated with CWE-94 (Code Injection).
Technical Analysis
Root cause of this vulnerability lies in the handling of .keras archives by the load_model function. Specifically, if an attacker can construct a malicious archive, they can manipulate the config.json file within it to execute arbitrary Python code. The attack vector is local, requiring the attacker to have access to the system where Keras is being executed.
The attack complexity is low, and it requires minimal privileges, making it easier for attackers to exploit this vulnerability. User interaction is necessary, as the malicious archive must be loaded by the user. If exploited, the confidentiality, integrity, and availability impacts are high, as attackers could execute any code they choose, potentially leading to system compromise.
Risk & Impact Analysis
Organizations leveraging Keras should be aware of the significant risk associated with this vulnerability. The potential for arbitrary code execution presents a considerable threat, as it could lead to unauthorized access and compromise sensitive data. The implications of such an attack could extend beyond the immediate system, affecting broader organizational security.
The urgency for organizations is underscored by the high CVSS score of 7.3. Patching should be prioritized to mitigate the risks associated with this vulnerability, especially given the high exploitability and impact assessments.
Signal | Status |
|---|---|
Known Exploit | Yes |
Public PoC | Yes |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected versions of Keras are those between 3.0.0 and 3.8.0. Organizations using these versions should apply the latest patches to remediate the vulnerability.
Mitigation & Remediation
Organizations should prioritize patching Keras to address this vulnerability. Upgrading to the latest version that resolves this issue is essential. In cases where a patch is unavailable, consider implementing workarounds such as restricting access to the load_model function or monitoring for unauthorized model loads. Additionally, configuration hardening, network controls, and continuous monitoring should be integrated into security practices.
For more comprehensive security testing, organizations can engage in penetration testing to ensure their systems are secure against such vulnerabilities.
Detection Guidance
Organizations should monitor logs for indicators of unauthorized model loading or execution of unexpected Python modules. Behavioral anomalies in model loading processes can also serve as indicators of exploitation attempts. Network signatures associated with malicious .keras archives should be established to enhance detection capabilities.
AppSecure Threat Intelligence Insight
This vulnerability highlights the importance of secure coding practices and the potential risks associated with code execution features in libraries like Keras. It serves as a reminder for developers to validate inputs and control execution contexts carefully. Security teams should consider this vulnerability as part of their broader threat landscape, analyzing similar patterns and implementing proactive measures.
For further insights and best practices, organizations can explore resources on penetration testing methodology and vulnerability management programs to strengthen their security posture.
Additionally, organizations should stay updated on security trends through resources like vulnerability exposure severity trends to anticipate and mitigate future risks.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
.webp)