CVE-2011-2720 in GLPI
Summary
by MITRE
The autocompletion functionality in GLPI before 0.80.2 does not blacklist certain username and password fields, which allows remote attackers to obtain sensitive information via a crafted POST request.
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Analysis
by VulDB Data Team • 11/17/2021
The vulnerability identified as CVE-2011-2720 affects the GLPI (Gestionnaire Libre de Parc Informatique) software version 0.80.1 and earlier, representing a critical information disclosure flaw within the application's autocompletion feature. This issue stems from inadequate input validation and sanitization mechanisms that fail to properly filter or blacklist sensitive fields during autocomplete operations. The autocompletion functionality in GLPI is designed to provide users with suggested values for form fields, typically to improve user experience and reduce data entry time. However, in the affected versions, this feature lacks proper security controls that would prevent the exposure of confidential information such as usernames and passwords through crafted HTTP POST requests.
The technical flaw manifests when the autocompletion service processes user input without implementing proper field filtering mechanisms. Specifically, the system does not maintain a comprehensive blacklist of sensitive fields that should not be exposed through autocomplete responses. This allows remote attackers to manipulate the autocompletion endpoint by crafting malicious POST requests that target fields containing authentication credentials or other sensitive data. The vulnerability is classified as a weakness in input validation and output encoding according to CWE-20, which represents a broad category of input validation flaws that can lead to information disclosure, injection attacks, and other security issues. The attack vector operates entirely through HTTP communication, making it accessible to any remote attacker who can interact with the GLPI web interface.
The operational impact of this vulnerability extends beyond simple information disclosure, as it can enable attackers to harvest authentication credentials and other sensitive data from the system. When an attacker successfully exploits this vulnerability, they can potentially access user accounts, administrative privileges, and other confidential information stored within the GLPI database. This represents a significant threat to organizational security, particularly in environments where GLPI is used for inventory management, help desk services, or asset tracking, as these systems often contain sensitive operational data. The vulnerability can be exploited without requiring authentication, making it particularly dangerous as it allows for passive reconnaissance and credential harvesting. According to ATT&CK framework category T1567, this vulnerability aligns with information harvesting techniques that target credential exposure through web application vulnerabilities.
The exploitation of this vulnerability typically involves sending a crafted POST request to the autocompletion endpoint with specifically formatted parameters that trigger the disclosure of sensitive fields. The lack of proper input sanitization means that even if attackers cannot directly query database fields, they can still extract information through the autocomplete service that should normally be protected. Organizations using GLPI versions prior to 0.80.2 face significant risk of credential compromise and data leakage, particularly in environments where the software is exposed to untrusted networks or where it handles sensitive organizational information. The vulnerability also highlights the importance of implementing proper field-level access controls and data protection mechanisms within web applications. Security practitioners should consider implementing additional monitoring and logging of autocompletion requests to detect potential exploitation attempts. The recommended mitigation involves upgrading to GLPI version 0.80.2 or later, which includes proper field blacklisting and input validation controls to prevent sensitive data exposure through autocomplete functionality. Additionally, organizations should review their web application security configurations and implement proper access controls to minimize the impact of similar vulnerabilities in other components of their IT infrastructure.