CVE-2026-123289 in Datalore
Summary
by MITRE • 11/06/2026
In JetBrains Datalore before 2026.4 improper authorisation allowed notebook viewers to modify files
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Analysis
by VulDB Data Team • 11/06/2026
The vulnerability identified in JetBrains Datalore prior to version 2026.4 represents a critical failure in access control mechanisms, specifically categorized under CWE-862: Missing Authorization. This flaw allows users who are granted only viewer permissions on collaborative notebooks or associated project files to execute unauthorized write operations. In the context of cloud-based data science and machine learning platforms like Datalore, proper segregation of duties is essential for maintaining data integrity and preventing accidental or malicious modification of shared resources. The application failed to enforce strict role-based access controls at the API level when processing file modification requests, effectively treating viewer-level authentication tokens with the same privilege as editor or owner roles during specific write operations.
From a technical perspective, this improper authorization likely stems from a lack of server-side validation for user permissions before committing changes to the underlying storage system. When a client application sends a request to update code cells, modify data files, or alter notebook metadata, the backend service did not adequately verify that the authenticated principal possessed the necessary write privileges for the specific resource being targeted. This oversight enables an attacker with read-only access to bypass intended restrictions and inject malicious code, alter datasets used for model training, or corrupt shared project configurations. Such behavior aligns closely with MITRE ATT&CK technique T1078: Valid Accounts, where legitimate credentials are exploited due to insufficient privilege separation, allowing lateral movement within the workspace environment without detection by standard audit logs that might only track authentication events rather than authorization decisions.
The operational impact of this vulnerability is significant for teams relying on Datalore for collaborative analysis and secure data handling. Unauthorized modifications can lead to data poisoning in machine learning pipelines if training datasets are altered, resulting in compromised model outputs or biased predictions. Furthermore, the ability to modify code cells allows an attacker to introduce backdoors or exfiltrate sensitive information embedded within scripts, violating confidentiality requirements. For organizations adhering to strict compliance frameworks such as SOC 2 or ISO 27001, this flaw constitutes a direct violation of access control policies and data integrity safeguards, potentially leading to regulatory penalties and loss of stakeholder trust if exploited in production environments containing proprietary algorithms or sensitive customer data.
Mitigation strategies must prioritize immediate patching to version 2026.4 or later, where JetBrains has addressed the authorization logic flaws. In addition to updating software, organizations should implement defense-in-depth measures by enabling comprehensive audit logging that captures not just who accessed a resource but what actions were attempted and whether they were permitted. Role-based access control policies should be reviewed regularly to ensure least-privilege principles are strictly enforced, particularly for shared workspaces where multiple users with varying levels of trust interact with the same data assets. Network-level monitoring can also help detect anomalous patterns of file modifications originating from viewer accounts, providing an additional layer of security against exploitation while internal controls are being hardened.