CVE-2026-25126 is a vulnerability identified in the open-source educational software PolarLearn, specifically affecting versions earlier than 0-PRERELEASE-15. The issue stems from improper input validation (CWE-20) in the vote API route (`POST /api/v1/forum/vote`). The API accepts a JSON body containing a 'direction' field intended to indicate vote direction, typically 'up' or null. However, because PolarLearn relies on TypeScript types that are not enforced at runtime, the API does not validate the 'direction' value properly. An attacker can submit arbitrary strings such as "x". The downstream VoteServer component interprets any value other than 'up' or null as a downvote and stores this invalid data in the votes_data repository. This behavior allows attackers to bypass intended business logic, potentially manipulating vote counts or forum feedback mechanisms. The vulnerability requires network access and low privileges but no user interaction, making it relatively easy to exploit remotely. Although the vulnerability does not directly affect confidentiality or availability, it compromises data integrity by allowing unauthorized vote manipulation. The issue was fixed in version 0-PRERELEASE-15 by implementing proper runtime validation of the 'direction' field to accept only valid values. No known exploits are currently reported in the wild, but the high CVSS score (7.1) reflects the significant impact on integrity and ease of exploitation. Organizations using PolarLearn should prioritize upgrading to the patched version and review vote-related data for signs of tampering.

For European organizations, especially those in education or training sectors using PolarLearn, this vulnerability poses a risk to the integrity of user-generated content and feedback mechanisms. Manipulated vote data can undermine trust in forum discussions, assessments, or peer reviews, potentially affecting learning outcomes and decision-making processes. Since the vulnerability allows low-privilege attackers to bypass business logic, it could be exploited to skew reputations, promote misinformation, or disrupt collaborative learning environments. Although it does not compromise confidentiality or availability, the integrity breach can lead to reputational damage and loss of confidence in the platform. Additionally, organizations relying on vote data for analytics or automated actions may experience erroneous results, impacting operational decisions. The lack of user interaction requirement and network-level exploitability increase the threat surface, especially in environments with exposed APIs or insufficient network segmentation. European institutions with strict data integrity and compliance requirements may face regulatory scrutiny if manipulated data affects educational records or assessments.

European organizations should immediately upgrade PolarLearn installations to version 0-PRERELEASE-15 or later to ensure proper input validation is enforced. In addition to patching, implement the following measures: 1) Conduct a thorough audit of vote-related data to detect and remediate any anomalies or suspicious entries caused by this vulnerability. 2) Employ Web Application Firewalls (WAFs) with custom rules to detect and block malformed or unexpected 'direction' values in API requests until patches are deployed. 3) Restrict network access to the vote API endpoint to trusted users and systems, using network segmentation and access controls to minimize exposure. 4) Implement runtime input validation at the API gateway or proxy level as an additional safeguard against malformed inputs. 5) Monitor logs for unusual voting patterns or repeated invalid input attempts to identify potential exploitation attempts early. 6) Educate developers and administrators about the limitations of relying solely on TypeScript types for input validation and promote secure coding practices that include runtime checks. 7) Review and strengthen business logic validation downstream to reject invalid or unexpected data before persistence. These targeted actions will reduce the risk of exploitation and help maintain data integrity.