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CVE-2026-25222: CWE-200: Exposure of Sensitive Information to an Unauthorized Actor in polarnl PolarLearn

0
Medium
VulnerabilityCVE-2026-25222cvecve-2026-25222cwe-200
Published: Mon Feb 02 2026 (02/02/2026, 23:01:02 UTC)
Source: CVE Database V5
Vendor/Project: polarnl
Product: PolarLearn

Description

PolarLearn is a free and open-source learning program. In 0-PRERELEASE-15 and earlier, a timing attack vulnerability in the sign-in process allows unauthenticated attackers to determine if a specific email address is registered on the platform. By measuring the response time of the login endpoint, an attacker can distinguish between valid and invalid email addresses. This occurs because the server only performs the computationally expensive Argon2 password hashing if the user exists in the database. Requests for existing users take significantly longer (~650ms) than requests for non-existent users (~160ms).

AI-Powered Analysis

Machine-generated threat intelligence

AILast updated: 02/10/2026, 10:43:23 UTC

Technical Analysis

CVE-2026-25222 is a timing side-channel vulnerability affecting PolarLearn, an open-source educational platform. In versions up to 0-PRERELEASE-15, the login endpoint's response time varies significantly depending on whether the submitted email address exists in the user database. Specifically, the server performs the computationally expensive Argon2 password hashing only if the user exists, resulting in a response time of approximately 650 milliseconds for valid users, compared to about 160 milliseconds for invalid users. This timing discrepancy enables unauthenticated attackers to enumerate registered email addresses by measuring response times, thus exposing sensitive information about user registration status (CWE-200). While the vulnerability does not allow direct access to passwords or other confidential data, it facilitates reconnaissance activities that can lead to targeted phishing, social engineering, or brute-force attacks. The vulnerability has a CVSS 4.0 base score of 6.3, reflecting a medium severity level due to network attack vector, high attack complexity, no privileges or user interaction required, and limited confidentiality impact. No patches are currently linked, and no known exploits have been reported in the wild. The root cause is the conditional execution of the Argon2 hashing function based on user existence, which leaks timing information. Mitigation requires implementing constant-time response behavior or introducing artificial delays to mask timing differences during authentication attempts.

Potential Impact

For European organizations using PolarLearn, this vulnerability poses a privacy risk by allowing attackers to confirm the presence of specific email addresses in the platform’s user database. This can facilitate targeted phishing campaigns, social engineering attacks, or credential stuffing attempts, potentially leading to broader compromise of user accounts or organizational resources. Educational institutions and training providers in Europe that rely on PolarLearn for user authentication are particularly at risk, as attackers can harvest user lists and craft more convincing attacks. While the vulnerability does not directly expose passwords or enable account takeover, the information disclosure can degrade user trust and privacy compliance, especially under GDPR regulations that mandate protection of personal data. The timing attack can be executed remotely without authentication, increasing the attack surface. Organizations may face reputational damage and regulatory scrutiny if user data is indirectly exposed through such side-channel attacks. The medium severity indicates that while the immediate impact is limited, the vulnerability can be a stepping stone for more damaging attacks if combined with other weaknesses.

Mitigation Recommendations

To mitigate CVE-2026-25222, organizations should implement constant-time authentication responses regardless of user existence. Specifically, the server should perform the Argon2 password hashing operation for all login attempts, including those with non-existent email addresses, to equalize response times. If this is not feasible, introducing artificial delays to match the longer processing time for invalid users can reduce timing discrepancies. Additionally, rate limiting login attempts and monitoring for abnormal request patterns can help detect enumeration attempts. Updating to a patched version of PolarLearn once available is recommended. In the interim, consider deploying web application firewalls (WAFs) with custom rules to detect and block timing attack patterns. Educate users about phishing risks and encourage strong, unique passwords combined with multi-factor authentication (MFA) where possible to reduce the impact of potential account enumeration. Finally, review logging and alerting mechanisms to capture suspicious authentication activity indicative of enumeration attempts.

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Technical Details

Data Version
5.2
Assigner Short Name
GitHub_M
Date Reserved
2026-01-30T14:44:47.327Z
Cvss Version
4.0
State
PUBLISHED

Threat ID: 69813006f9fa50a62f63a418

Added to database: 2/2/2026, 11:15:18 PM

Last enriched: 2/10/2026, 10:43:23 AM

Last updated: 3/23/2026, 4:18:16 PM

Views: 71

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