CVE-2008-2020 in phpMyBitTorrent
Summary
by MITRE
The CAPTCHA implementation as used in (1) Francisco Burzi PHP-Nuke 7.0 and 8.1, (2) my123tkShop e-Commerce-Suite (aka 123tkShop) 0.9.1, (3) phpMyBitTorrent 1.2.2, (4) TorrentFlux 2.3, (5) e107 0.7.11, (6) WebZE 0.5.9, (7) Open Media Collectors Database (aka OpenDb) 1.5.0b4, and (8) Labgab 1.1 uses a code_bg.jpg background image and the PHP ImageString function in a way that produces an insufficient number of different images, which allows remote attackers to pass the CAPTCHA test via an automated attack using a table of all possible image checksums and their corresponding digit strings.
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Analysis
by VulDB Data Team • 09/23/2018
The vulnerability described in CVE-2008-2020 represents a critical weakness in CAPTCHA implementation across multiple web applications, fundamentally undermining their security mechanisms designed to prevent automated abuse. This flaw specifically affects applications that utilize PHP's ImageString function combined with a code_bg.jpg background image to generate CAPTCHA images. The vulnerability stems from insufficient entropy in the generated CAPTCHA images, creating a predictable pattern that can be easily exploited by attackers. The issue affects a diverse range of applications including content management systems like PHP-Nuke and e107, e-commerce platforms such as 123tkShop, and torrent management systems like TorrentFlux and phpMyBitTorrent. These applications all share a common implementation flaw where the combination of background image and text rendering function produces a limited set of possible CAPTCHA combinations, making them susceptible to precomputed attack vectors.
The technical nature of this vulnerability aligns with CWE-310, which addresses cryptographic weakness, and more specifically CWE-312, which deals with exposure of sensitive information through cleartext storage or transmission. The flaw operates at the application layer where the CAPTCHA generation process fails to provide adequate randomness in image composition, leading to a situation where attackers can compile a lookup table of all possible image checksums and their corresponding digit strings. This approach effectively nullifies the CAPTCHA's intended purpose of distinguishing human users from automated bots. The vulnerability exploits the fundamental principle that CAPTCHA systems must maintain sufficient entropy to prevent automated solutions, but these applications fail to achieve that requirement through their image generation algorithms.
The operational impact of this vulnerability extends beyond simple bypassing of security measures, representing a significant risk to web application integrity and user authentication processes. Attackers can leverage this weakness to perform automated attacks including account registration spam, comment flooding, forum spamming, and other malicious activities that rely on circumventing CAPTCHA protections. The vulnerability affects the availability and integrity of web applications by allowing unauthorized automated access to protected resources, potentially leading to service degradation or complete system compromise. In environments where these applications handle sensitive user data or financial transactions, the implications become more severe, as attackers can systematically bypass security controls designed to prevent automated abuse. The vulnerability also demonstrates a failure in proper security testing and validation of authentication mechanisms, as the applications did not adequately test their CAPTCHA implementations against known attack patterns.
Mitigation strategies for this vulnerability require immediate implementation of stronger CAPTCHA generation algorithms that ensure sufficient entropy in image composition. Organizations should replace the existing CAPTCHA implementations with more robust alternatives such as those based on cryptographic random number generation or third-party CAPTCHA services like Google reCAPTCHA. The fix should involve modifying the PHP code to use more complex image generation techniques including varying font styles, colors, and positioning, as well as implementing dynamic background patterns that significantly increase the number of possible CAPTCHA combinations. Additionally, implementing rate limiting and behavioral analysis mechanisms can provide additional layers of protection against automated attacks. Security teams should conduct comprehensive vulnerability assessments of all applications using similar CAPTCHA implementations and ensure proper testing of authentication mechanisms against known attack vectors. The remediation process should follow established security frameworks such as those outlined in the OWASP Top Ten and NIST Cybersecurity Framework, ensuring that the solution addresses both the immediate vulnerability and prevents similar issues in future implementations.