CVE-2024-2196 in aim
Summary
by MITRE • 04/10/2024
aimhubio/aim is vulnerable to Cross-Site Request Forgery (CSRF), allowing attackers to perform actions such as deleting runs, updating data, and stealing data like log records and notes without the user's consent. The vulnerability stems from the lack of CSRF and CORS protection in the aim dashboard. An attacker can exploit this by tricking a user into executing a malicious script that sends unauthorized requests to the aim server, leading to potential data loss and unauthorized data manipulation.
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Analysis
by VulDB Data Team • 07/29/2025
The CVE-2024-2196 vulnerability affects aimhubio/aim, a machine learning experiment tracking platform, by exposing a critical cross-site request forgery flaw that undermines the application's security posture. This vulnerability resides in the dashboard component where insufficient protection mechanisms fail to validate the origin and authenticity of incoming requests. The absence of proper CSRF token validation and CORS policy enforcement creates an exploitable condition that allows malicious actors to manipulate the application's functionality through crafted requests. The vulnerability specifically targets the dashboard interface where users interact with experiment data, making it a prime target for unauthorized operations.
The technical exploitation of this vulnerability relies on the fundamental weakness in the application's request validation process. Without CSRF tokens or proper origin checking mechanisms, legitimate user sessions become vulnerable to manipulation by attackers who can craft malicious requests that appear to originate from authenticated users. The vulnerability manifests when users visit compromised web pages or click on malicious links that trigger unauthorized actions against the aim server. Attackers can leverage this flaw to delete experiment runs, modify dataset parameters, or extract sensitive information including log records and user notes. This represents a classic CSRF attack vector where the attacker exploits the browser's automatic inclusion of cookies and authentication credentials in requests to the target application.
The operational impact of CVE-2024-2196 extends beyond simple data manipulation to encompass potential data loss and unauthorized access to sensitive experimental information. Machine learning teams relying on aim for experiment tracking face significant risks including the deletion of critical research data, alteration of experimental parameters that could compromise study integrity, and unauthorized access to proprietary model training logs. The vulnerability particularly affects collaborative environments where multiple users share dashboards and experiment data, as unauthorized modifications can propagate through shared workspaces. Additionally, the ability to steal log records and notes exposes potentially sensitive information about research methodologies and experimental outcomes that could be valuable for competitive intelligence or academic misconduct.
Security mitigations for this vulnerability should focus on implementing comprehensive CSRF protection mechanisms within the aim dashboard. The solution requires the integration of anti-CSRF tokens that are generated per session and validated on each state-changing request, along with proper CORS policy enforcement that restricts cross-origin resource sharing. The implementation should follow established security frameworks such as CWE-352, which specifically addresses cross-site request forgery vulnerabilities, and align with ATT&CK technique T1566.001 for credential access through social engineering. Organizations should also consider implementing Content Security Policy headers, proper session management, and regular security audits to prevent similar vulnerabilities in web applications. The fix must ensure that all user-initiated actions on the dashboard require explicit authentication tokens that cannot be forged or reused by unauthorized parties.