Discover the impact of CVE-2020-25459 in WeBank FATE 0.1 through 1.4.2, allowing unauthorized access to sensitive information during machine learning training. Learn mitigation steps here.
An issue was discovered in function sync_tree in hetero_decision_tree_guest.py in WeBank FATE (Federated AI Technology Enabler) 0.1 through 1.4.2, allowing attackers to read sensitive information during the training process of machine learning joint modeling.
Understanding CVE-2020-25459
This CVE involves a vulnerability in WeBank FATE that could be exploited to access sensitive information during machine learning training.
What is CVE-2020-25459?
The vulnerability in function sync_tree in WeBank FATE allows malicious actors to extract confidential data while machine learning models are being trained.
The Impact of CVE-2020-25459
The exploitation of this vulnerability could lead to unauthorized access to sensitive information, compromising the confidentiality of machine learning joint modeling processes.
Technical Details of CVE-2020-25459
This section provides technical insights into the vulnerability.
Vulnerability Description
The issue lies in the sync_tree function in hetero_decision_tree_guest.py within WeBank FATE versions 0.1 through 1.4.2, enabling unauthorized data access during machine learning model training.
Affected Systems and Versions
Exploitation Mechanism
Attackers can exploit this vulnerability to read sensitive information during the training phase of machine learning joint modeling.
Mitigation and Prevention
Protecting systems from CVE-2020-25459 requires immediate actions and long-term security practices.
Immediate Steps to Take
Long-Term Security Practices
Patching and Updates
Ensure timely installation of security patches and updates for WeBank FATE to prevent exploitation of this vulnerability.