What's Happening?
A medium-severity vulnerability has been identified in the open-source library Keras, which could allow attackers to load arbitrary local files or conduct server-side request forgery (SSRF) attacks. Keras, a deep
learning API, is used for building AI models compatible with frameworks like TensorFlow and PyTorch. The flaw, tracked as CVE-2025-12058, arises from the library's preprocessing layers that allow file paths or URLs as inputs, potentially exposing sensitive data during model deserialization. Attackers could exploit this by uploading malicious models to public repositories, leading to unauthorized access to sensitive information.
Why It's Important?
The vulnerability in Keras highlights significant security risks in AI model development, particularly concerning data exposure and unauthorized access. As AI models become integral to various applications, ensuring their security is paramount to prevent data breaches and protect sensitive information. The flaw underscores the need for robust security measures in AI development, including safe deserialization practices and validation of inputs. Addressing these vulnerabilities is crucial to maintaining trust in AI technologies and preventing potential exploitation by malicious actors.
What's Next?
The vulnerability has been resolved in Keras version 3.11.4, which embeds vocabulary files directly into the Keras archive and restricts arbitrary file loading when safe_mode is enabled. Developers using Keras are advised to update to the latest version to mitigate security risks. Continuous monitoring and improvement of security practices in AI development will be essential to prevent future vulnerabilities and ensure the safe deployment of AI models.
