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March 14, 2025
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How to Fix CVE-2025-1550: Code Injection Vulnerability in Keras Model Loading?
The Keras library, a high-level API for building and training neural networks, is widely used in the machine learning community. However, a newly discovered vulnerability, CVE-2025-1550, poses a significant threat to systems that rely on Keras models. This code injection flaw can allow attackers to execute arbitrary code on a vulnerable system, potentially leading to severe consequences. This article dives deep into the technical details of CVE-2025-1550, explaining its impact, affected versions, and, most importantly, providing actionable steps for security professionals to mitigate this risk and secure their machine learning environments.
A Short Introduction to Keras
Keras is an open-source neural network library written in Python. It acts as a high-level API for various deep learning frameworks like TensorFlow and Theano. Its user-friendly design and focus on rapid experimentation have made it a popular choice for researchers and practitioners alike. Keras simplifies the process of building, training, evaluating, and deploying neural networks, making it an accessible tool for a wide range of machine learning tasks, from image classification and natural language processing to time series analysis and generative modeling.
Summary of CVE-2025-1550
CVE ID: CVE-2025-1550
Description: Improper Control of Generation of Code ('Code Injection') in Keras Model Loading.
CVSS Score: 7.3 (HIGH)
CVSS Vector: CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:A/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H
CVE-2025-1550 is a critical code injection vulnerability found in the Model.load_model function of the Keras library. The vulnerability allows for arbitrary code execution even when the safe_mode parameter is set to True. This occurs because the load_model function doesn't properly sanitize the config.json file within a .keras archive. An attacker can craft a malicious .keras archive, specifically manipulating the config.json file, to specify arbitrary Python modules and functions, along with their arguments. When a user loads this malicious model, the specified code will be executed. The flaw's presence despite the safe_mode parameter underscores the severity and potential for unexpected exploitation.
Impact of CVE-2025-1550
The impact of CVE-2025-1550 is significant. An attacker with local access and relatively low privileges can craft a malicious .keras archive that, when loaded, executes arbitrary code. This opens the door to a range of malicious activities. Successfully exploiting this vulnerability could allow an attacker to completely compromise the system, gain unauthorized access to sensitive information, modify system resources, and potentially move laterally within the network. The fact that the vulnerability can be triggered with user interaction (e.g., a user loading a seemingly benign model) makes it especially dangerous, as it bypasses common security assumptions about the safety of model loading operations. Given the widespread use of Keras in machine learning pipelines, the potential for widespread compromise is substantial. Understanding IOC can help identify potential compromises after exploitation.
Products Affected by CVE-2025-1550
The vulnerability affects any application or system utilizing the Keras Model.load_model function with affected Keras versions. The specific affected versions are detailed below:
| Product | Version(s) Affected |
|---|---|
| Keras | All versions prior to the patched version (check vendor advisory for details) |
It is important to note that the specific patched version is not explicitly defined in the provided information. Therefore, users are advised to consult the official Keras documentation and security advisories to determine the specific version that addresses this vulnerability.
How to Check Your Product is Vulnerable?
To determine if your system is vulnerable to CVE-2025-1550, follow these steps:
Identify Keras Version: Determine the version of Keras installed in your environment. You can usually do this through Python using
import keras; print(keras.__version__).Check Against Affected Versions: Compare your Keras version against the list of affected versions provided in the official Keras security advisory. If your version falls within the vulnerable range, your system is potentially at risk.
Inspect Model Loading Code: Review the code where you use the
Model.load_modelfunction. Pay close attention to where the model files originate. If you load models from untrusted sources, the risk is significantly higher.Test with a Safe Model (if possible): As a preliminary check, try loading a known-safe model. If you observe any unexpected behavior or errors during loading, it could indicate a potential issue. However, this is not a definitive test and should be followed up with the other steps.
Monitor Model Loading Events: Implement monitoring and logging of model loading events within your applications. This includes recording the source of the model, the user initiating the load, and any relevant system events during the process. Abnormal activity or attempts to load models from untrusted sources should be flagged for immediate investigation. To improve threat detection capabilities, consider leveraging UEBA solutions.
How to Fix CVE-2025-1550?
Addressing CVE-2025-1550 requires immediate action to prevent potential exploitation. Here's a breakdown of the recommended mitigation steps:
Update Keras: The primary remediation strategy is to update Keras to the latest patched version. Check the official Keras website or relevant security advisories for the specific version that addresses this vulnerability.
Implement Strict Input Validation: Even after patching, implement rigorous input validation for
.kerasarchives. Before loading any model, verify its integrity and origin. Check the file's checksum against a known good value and ensure that it comes from a trusted source.Restrict Model Loading to Trusted Sources Only: Limit model loading to trusted sources. Avoid loading models from untrusted websites, email attachments, or other potentially compromised locations.
Use Additional Sandboxing Techniques: Consider using Docker containers when loading external models. This can help to contain any malicious code that might be executed during the loading process. Tools like Docker containers or virtual machines can provide an extra layer of security.
Conduct a Thorough Review of Recently Loaded Models: If you have recently loaded models from potentially untrusted sources, conduct a thorough review of your system for any signs of compromise. Look for suspicious files, processes, or network activity.
Implement the Principle of Least Privilege: Apply the principle of least privilege to model loading processes. Ensure that the user account loading the model has only the necessary permissions to perform that task. Avoid using administrative accounts for model loading.
If a patch is not yet available, or if immediate patching is not possible, focus on the following protective measures:
Network Segmentation: Isolate systems that load Keras models within a segmented network.
Web Application Firewall (WAF): If Keras models are loaded through a web application, deploy a WAF with rules to detect and block malicious requests targeting the
load_modelfunction. Security misconfiguration can increase the impact of vulnerabilities.Monitor Official Channels: Continuously monitor official Keras channels, security mailing lists, and vulnerability databases for any security updates or patches related to this vulnerability.
By implementing these fixes, mitigations, and best practices, you can significantly reduce the risk posed by CVE-2025-1550 and improve the overall security posture of your machine learning environment.
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Arun KL
Arun KL is a cybersecurity professional with 15+ years of experience in IT infrastructure, cloud security, vulnerability management, Penetration Testing, security operations, and incident response. He is adept at designing and implementing robust security solutions to safeguard systems and data. Arun holds multiple industry certifications including CCNA, CCNA Security, RHCE, CEH, and AWS Security.
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