CVE-2020-4764 in Planning Analytics
Summary
by MITRE • 12/18/2020
IBM Planning Analytics 2.0 is vulnerable to cross-site request forgery which could allow an attacker to execute malicious and unauthorized actions transmitted from a user that the website trusts. IBM X-Force ID: 188898.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Analysis
by VulDB Data Team • 07/11/2026
Cross-site request forgery vulnerabilities in IBM Planning Analytics 2.0 represent a critical security weakness that undermines the integrity of web-based enterprise planning systems. This vulnerability stems from the application's failure to properly validate and authenticate cross-origin requests, creating an attack surface where malicious actors can exploit trust relationships between users and the web application. The flaw allows attackers to craft deceptive requests that appear to originate from legitimate authenticated users, potentially enabling unauthorized modifications to planning data, creation of fraudulent reports, or manipulation of business intelligence workflows.
The technical implementation of this CSRF vulnerability in IBM Planning Analytics 2.0 demonstrates a classic failure in state management and request validation mechanisms. When users authenticate to the system, their session cookies and authentication tokens are automatically included with every subsequent request without proper verification of the originating source. This design flaw aligns with CWE-352, which categorizes cross-site request forgery as a fundamental web application security weakness. Attackers can leverage this vulnerability by embedding malicious links or forms within compromised websites that target the planning analytics platform, causing authenticated users to unknowingly perform actions they would not normally authorize.
The operational impact of this vulnerability extends beyond simple data manipulation to potentially compromise business continuity and strategic decision-making processes. Organizations relying on IBM Planning Analytics for financial forecasting, resource allocation, and performance monitoring face significant risks when unauthorized changes can be made to critical planning models. Attackers could alter budget allocations, modify forecast parameters, or inject false data into planning workflows, leading to erroneous business decisions that may affect financial reporting, operational planning, and stakeholder confidence. The attack vector typically involves social engineering campaigns where users are tricked into clicking malicious links while authenticated to the system.
Mitigation strategies for this CSRF vulnerability must address both defensive coding practices and organizational security controls. Organizations should implement robust anti-CSRF token mechanisms that are generated per session and validated on every state-changing request, ensuring that each transaction originates from a legitimate source within the application context. The implementation of Content Security Policy headers and proper SameSite cookie attributes can further protect against cross-site request forgery attacks by restricting how cookies are transmitted across different origins. Additionally, regular security assessments and penetration testing should validate that CSRF protections remain effective against evolving attack techniques, with particular attention to maintaining consistent authentication validation across all web application interfaces. This vulnerability underscores the importance of adhering to secure coding practices as outlined in the OWASP Top Ten and aligns with ATT&CK technique T1531 for credential access through web application attacks.
The IBM Planning Analytics 2.0 CSRF vulnerability exemplifies how modern enterprise applications must maintain rigorous security controls to protect against sophisticated attack vectors that exploit trust relationships within web environments. Organizations should prioritize immediate patching of known vulnerabilities while implementing comprehensive security monitoring to detect unauthorized activities within planning analytics systems. Regular security awareness training for users can help prevent successful social engineering components of these attacks, while network segmentation and access control measures can limit the potential impact of successful exploitation attempts.