CVE-2025-40916 in Mojolicious::Plugin::CaptchaPNG
Summary
by MITRE • 06/16/2025
Mojolicious::Plugin::CaptchaPNG version 1.05 for Perl uses a weak random number source for generating the captcha.
That version uses the built-in rand() function for generating the captcha text as well as image noise, which is insecure.
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Analysis
by VulDB Data Team • 06/16/2025
The vulnerability identified as CVE-2025-40916 affects Mojolicious::Plugin::CaptchaPNG version 1.05, a Perl module designed to generate captcha images for web applications. This module serves as a security mechanism to distinguish between human users and automated bots by presenting visual challenges that require human interpretation. The security implications of this flaw extend beyond simple authentication bypasses, as it fundamentally undermines the integrity of the captcha system that applications rely upon for protection against automated attacks. The vulnerability resides in the module's implementation of random number generation, which directly impacts the unpredictability and security strength of the generated captcha elements.
The technical flaw stems from the module's reliance on Perl's built-in rand() function for two critical aspects of captcha generation: the text content that users must decipher and the noise elements that obscure the captcha image. The rand() function in Perl is based on a linear congruential generator that provides poor randomness quality and predictable sequences, particularly when the seed is not properly initialized or when the function is called repeatedly in rapid succession. This weakness allows attackers to predict or reproduce the captcha values, effectively nullifying the security mechanism. The vulnerability maps to CWE-330 Use of Insufficiently Random Values, which specifically addresses the use of weak random number generators in security-sensitive contexts where predictability can lead to system compromise.
The operational impact of this vulnerability is significant for applications that depend on this captcha plugin for user authentication, form submission protection, or automated attack prevention. Attackers who can predict captcha values can bypass authentication mechanisms, submit spam forms, or perform automated attacks at scale without human intervention. This weakness particularly affects web applications that use the captcha for preventing brute force attacks, protecting contact forms, or securing user registration processes. The vulnerability creates a direct path for automated systems to perform actions that would normally require human verification, potentially leading to account takeovers, data breaches, or service abuse. From an attack perspective, this vulnerability aligns with ATT&CK technique T1110.003 Credential Stuffing, where weak authentication mechanisms enable automated credential testing.
The weakness in random number generation creates a cascading security issue where the entire captcha system becomes predictable and ineffective. The noise elements added to the captcha images, which are meant to make text recognition more difficult for automated systems, become predictable as well, reducing their effectiveness. This vulnerability demonstrates poor security practices in the implementation of security controls, where basic cryptographic requirements for randomness are not met. Organizations using this module should immediately assess their captcha-dependent systems for potential exploitation, as the vulnerability allows attackers to enumerate valid captcha values and bypass security protections. The impact extends beyond immediate exploitation to long-term security degradation, as the compromised randomness can be used to build more sophisticated attack vectors against the affected applications. Mitigation requires immediate module updates, but organizations should also consider implementing additional security layers and monitoring for unusual patterns in captcha usage that might indicate exploitation attempts.