CVE-2014-1457 in Open Web Analytics
Summary
by MITRE
Open Web Analytics (OWA) before 1.5.6 improperly generates random nonce values, which makes it easier for remote attackers to bypass a CSRF protection mechanism by leveraging knowledge of an OWA user name.
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Analysis
by VulDB Data Team • 02/05/2021
Open Web Analytics version 1.5.5 and earlier contains a critical security flaw in its cross-site request forgery protection mechanism due to improper random nonce generation. This vulnerability stems from the application's use of predictable or insufficiently random values when creating nonces for CSRF protection tokens. The weakness allows remote attackers to bypass the intended security controls by leveraging knowledge of an OWA username to predict or reconstruct valid nonce values. The flaw exists in the core authentication and session management components of the application, specifically affecting the CSRF protection layer that should prevent unauthorized actions from being executed on behalf of authenticated users. This vulnerability is classified under CWE-330 as the use of insufficiently random values, which directly impacts the security of the application's session management and authentication mechanisms. The attack vector requires minimal privileges and can be executed remotely without authentication, making it particularly dangerous for web analytics platforms that often contain sensitive data about user behavior and system interactions. The vulnerability affects the fundamental security model of OWA by undermining the integrity of its CSRF protection scheme, which is designed to ensure that requests originate from legitimate users and not from malicious actors. This weakness creates a pathway for attackers to perform unauthorized actions such as modifying user settings, accessing restricted data, or executing administrative functions without proper authorization. The impact extends beyond simple data exposure to potentially enable privilege escalation and persistent access to the analytics platform, which serves as a critical component for monitoring and understanding user engagement patterns.
The technical implementation of the CSRF protection mechanism in OWA relies on generating cryptographically secure random values that are tied to user sessions and specific actions. However, the flawed random number generation algorithm produces predictable sequences that can be reverse-engineered by attackers who have knowledge of valid usernames within the system. This vulnerability aligns with ATT&CK technique T1078.004 which covers legitimate credentials and T1566.002 which addresses credential access through social engineering or predictable patterns. The weakness in nonce generation creates a predictable state that allows attackers to construct valid CSRF tokens by observing patterns or through brute force attempts. The application's failure to implement proper entropy sources for nonce generation violates security best practices and industry standards such as those outlined in NIST SP 800-90A for random number generation. The vulnerability demonstrates a classic example of poor cryptographic implementation where insufficient randomness leads to complete bypass of security controls. This flaw particularly affects systems where OWA is used for sensitive analytics or where administrative access is required to modify configuration settings or view detailed user data.
The operational impact of CVE-2014-1457 extends beyond immediate unauthorized access to include potential data integrity compromise and system availability issues. Attackers can leverage this vulnerability to perform persistent unauthorized actions against the analytics platform, potentially leading to data manipulation or complete system compromise. The vulnerability affects organizations that rely on OWA for business-critical analytics and monitoring, as it undermines the trustworthiness of the data collected and the security of the platform itself. Organizations using older versions of OWA may experience significant security implications when attackers exploit this weakness to gain unauthorized access to user accounts or system resources. The vulnerability also impacts the integrity of analytics data, as attackers could manipulate or corrupt the collected information, leading to incorrect business decisions based on compromised data. Security monitoring systems may not detect this attack vector effectively since the malicious requests appear to originate from legitimate users, making the attack harder to trace and identify. The vulnerability requires minimal technical expertise to exploit, making it attractive to a wide range of threat actors from script kiddies to organized cybercriminals. Organizations should consider the potential for lateral movement within their networks if OWA is integrated with other systems, as this vulnerability could serve as a stepping stone to access additional resources and information. The long-term impact includes the potential for attackers to establish persistent backdoors or maintain access to the analytics platform for extended periods, complicating incident response and forensic analysis efforts.
The primary mitigation strategy involves upgrading to Open Web Analytics version 1.5.6 or later, which contains the necessary fixes for the random nonce generation algorithm. Organizations should also implement additional security controls such as implementing proper session management, ensuring all CSRF tokens are generated using cryptographically secure random number generators, and regularly auditing authentication mechanisms. Network segmentation and access controls should be implemented to limit exposure of OWA installations to unauthorized users. Security teams should monitor for suspicious activity patterns that may indicate exploitation attempts, particularly around authentication and session management events. Additional defensive measures include implementing rate limiting on authentication attempts, enforcing strong password policies, and ensuring proper input validation and output encoding to prevent related attack vectors. The fix addresses the core issue by implementing proper entropy sources for nonce generation, ensuring that each token is sufficiently random and unpredictable to prevent attackers from reconstructing valid tokens. Organizations should also consider implementing multi-factor authentication for administrative access to OWA installations, as well as regular security assessments to identify similar vulnerabilities in other applications. The remediation process should include thorough testing to ensure that the updated version functions correctly and that no regressions have been introduced to existing features. Security teams should also review and update their incident response procedures to account for potential exploitation of this vulnerability and establish proper monitoring for related attack patterns in their environments.