CVE-2025-56748 in Academy LMS
Summary
by MITRE • 10/15/2025
Creativeitem Academy LMS up to and including 5.13 uses predictable password reset tokens based on Base64 encoded templates without rate limiting, allowing brute force attacks to guess valid reset tokens and compromise user accounts.
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Analysis
by VulDB Data Team • 10/15/2025
The vulnerability identified as CVE-2025-56748 affects Creativeitem Academy LMS versions 5.13 and earlier, presenting a critical security weakness in the password reset functionality. This flaw stems from the implementation of predictable password reset tokens that are generated using Base64 encoded templates rather than cryptographically secure random values. The system fails to incorporate proper entropy in token generation, making it susceptible to prediction and brute force attacks. The absence of rate limiting mechanisms further compounds the issue, allowing attackers to rapidly iterate through potential token combinations without detection or restriction.
The technical implementation of this vulnerability aligns with CWE-330 Use of Insufficiently Random Values, where the predictable nature of the tokens stems from inadequate randomness in the cryptographic process. The Base64 encoding of templates suggests a deterministic approach to token creation rather than utilizing secure random number generation. This weakness creates a direct pathway for attackers to compromise user accounts through systematic guessing of valid reset tokens, bypassing normal authentication controls. The vulnerability represents a significant failure in the principle of least privilege and secure credential management.
Operationally, this vulnerability exposes users to account takeover attacks where malicious actors can systematically guess valid password reset tokens and gain unauthorized access to user accounts. The lack of rate limiting means that attackers can perform high-volume brute force attempts without triggering protective mechanisms, making the attack surface particularly dangerous. Successful exploitation could lead to complete account compromise, data theft, and potential lateral movement within the system. The impact extends beyond individual user accounts to potentially affect the entire learning management system and its associated data integrity.
Mitigation strategies should focus on implementing cryptographically secure random token generation that adheres to industry standards such as NIST SP 800-90A for random number generation. The system must incorporate proper rate limiting mechanisms to prevent brute force attacks, with thresholds that detect and block suspicious activity patterns. Additionally, implementing time-based token expiration, requiring multi-factor authentication for account recovery, and logging all reset token attempts would significantly reduce the attack surface. Organizations should also consider implementing the ATT&CK framework's T1566.002 technique countermeasures, which address credential access through social engineering and password reset attacks. Regular security testing and vulnerability assessments should be conducted to ensure proper implementation of secure authentication mechanisms and prevent similar weaknesses in future updates.