CVE-2023-36993 in TravianZ
Summary
by MITRE • 07/07/2023
The cryptographically insecure random number generator being used in TravianZ 8.3.4 and 8.3.3 in the password reset function allows an attacker to guess the password reset.parameters and to take over accounts.
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Analysis
by VulDB Data Team • 01/15/2026
The vulnerability identified as CVE-2023-36993 represents a critical security flaw in TravianZ version 8.3.4 and 8.3.3 that undermines the integrity of the account recovery mechanism. This issue stems from the implementation of a cryptographically insecure random number generator within the password reset functionality, creating a significant attack surface that adversaries can exploit to compromise user accounts. The flaw specifically affects the generation of reset parameters that are supposed to be unpredictable and secure, but instead are vulnerable to prediction and brute force attacks. The impact of this vulnerability extends beyond simple account theft, as it represents a fundamental breakdown in the application's security architecture and trust model. According to CWE-330, this vulnerability falls under the category of using insecure random number generators, which is a well-documented weakness in cryptographic systems. The attack vector leverages the predictability of the random number generation process to forge valid reset tokens or parameters that would normally be required to authenticate account recovery requests. This weakness directly violates the principles of secure authentication and authorization mechanisms that should be implemented in web applications and online services. The vulnerability allows attackers to systematically guess or calculate the password reset parameters without legitimate authorization, effectively bypassing the intended security controls. The operational impact of this flaw is severe as it enables unauthorized account access and potential data breaches, particularly when combined with other reconnaissance activities or social engineering tactics. From an ATT&CK perspective, this vulnerability maps to T1566 (Phishing) and T1078 (Valid Accounts) as attackers can leverage compromised accounts to maintain persistent access to user resources. The insecure random number generator creates a condition where the entropy of generated tokens is insufficient to provide meaningful security guarantees, making the system vulnerable to various attack techniques including but not limited to statistical analysis, pattern recognition, and brute force methodologies.
The technical implementation of this flaw demonstrates a failure in cryptographic best practices and secure coding standards that are fundamental to protecting user authentication systems. The password reset function in TravianZ relies on generating unpredictable tokens or parameters that should be computationally infeasible to guess or reproduce without legitimate access. However, the use of a cryptographically weak random number generator means that these values are predictable or easily reversible through mathematical analysis or computational methods. This vulnerability represents a critical failure in the principle of least privilege and the need for robust entropy sources in security-critical applications. The impact on the application's security posture is significant as it undermines the entire account recovery mechanism, which is designed to provide a secure fallback for users who have forgotten their credentials. Security researchers have identified that this vulnerability affects the core authentication infrastructure of the application, making it a prime target for malicious actors seeking to gain unauthorized access to user accounts and associated data. The flaw also indicates a lack of proper security testing and validation of cryptographic implementations within the application, which should have been caught during the development lifecycle or security assessment phases. Organizations implementing similar authentication mechanisms must ensure that all random number generation follows established cryptographic standards and that sufficient entropy is maintained throughout the generation process.
Mitigation strategies for this vulnerability require immediate implementation of secure random number generation algorithms that meet cryptographic standards such as those defined in NIST SP 800-90A or RFC 4086. The affected versions of TravianZ must be updated to utilize cryptographically secure pseudo-random number generators that provide sufficient entropy and unpredictability for security tokens and reset parameters. System administrators and developers should conduct comprehensive security assessments to identify all other instances where insecure random number generators may be in use throughout the application. The implementation of proper entropy sources and validation mechanisms for generated tokens should include monitoring and logging of token generation activities to detect potential anomalies or attacks. Organizations should also implement additional security controls such as rate limiting, account lockout mechanisms, and multi-factor authentication to provide defense-in-depth against exploitation attempts. The vulnerability highlights the importance of regular security audits and penetration testing to identify and remediate cryptographic weaknesses before they can be exploited by attackers. Security teams should also consider implementing automated tools to detect and flag insecure random number generator usage in applications, as this type of vulnerability can often be identified through static code analysis and runtime monitoring. The remediation process must include thorough testing to ensure that the new random number generation algorithms provide adequate security guarantees and do not introduce performance issues or compatibility problems with existing authentication workflows. Additionally, user education and awareness programs should be implemented to inform users about the importance of strong passwords and the risks associated with account compromise, particularly in light of vulnerabilities that can be exploited through predictable token generation processes.