CVE-2022-28892 in Mahara
Summary
by MITRE • 04/28/2022
Mahara before 20.10.5, 21.04.4, 21.10.2, and 22.04.0 is vulnerable to Cross Site Request Forgery (CSRF) because randomly generated tokens are too easily guessable.
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Analysis
by VulDB Data Team • 05/22/2026
The vulnerability identified as CVE-2022-28892 affects the Mahara learning management system across multiple version ranges including 20.10.5, 21.04.4, 21.10.2, and 22.04.0. This represents a critical security flaw that undermines the system's ability to prevent cross site request forgery attacks through weak token generation mechanisms. The vulnerability stems from the implementation of randomly generated tokens used for CSRF protection, which exhibit insufficient entropy to prevent successful guessing attempts by attackers. This weakness directly violates fundamental security principles for session management and request validation.
The technical flaw manifests in the cryptographic weakness of the token generation algorithm employed by Mahara's CSRF protection mechanism. When tokens are generated with predictable patterns or insufficient randomness, attackers can exploit this predictability to forge legitimate requests that bypass the intended security controls. The vulnerability specifically targets the entropy properties of the random number generators used for CSRF token creation, making these tokens susceptible to brute force attacks or pattern recognition techniques. This issue falls under the CWE-330 category of 'Use of Insufficiently Random Values' and aligns with ATT&CK technique T1078.004 for valid accounts, as successful CSRF exploitation can lead to unauthorized actions being performed on behalf of legitimate users.
The operational impact of this vulnerability is severe as it allows attackers to perform unauthorized actions within the Mahara system without proper authentication. An attacker could potentially manipulate user accounts, modify content, or execute administrative functions by crafting malicious requests that leverage the guessable CSRF tokens. This vulnerability is particularly dangerous in educational environments where Mahara systems manage sensitive user data, academic records, and collaborative learning materials. The attack surface extends to any authenticated user session that relies on CSRF protection, potentially enabling privilege escalation and data compromise. The vulnerability's exploitation requires minimal technical expertise and can be automated, making it a significant threat to system integrity and user privacy.
Organizations using affected Mahara versions should immediately implement mitigations including upgrading to patched versions 20.10.5, 21.04.4, 21.10.2, or 22.04.0 respectively. Additional defensive measures should include implementing proper session management controls, monitoring for suspicious request patterns, and ensuring that all CSRF tokens are generated using cryptographically secure random number generators. Network segmentation and access controls should be reviewed to limit potential impact from successful exploitation attempts. The vulnerability demonstrates the critical importance of proper entropy requirements in security token generation and highlights the need for regular security assessments of authentication mechanisms. Security teams should also consider implementing additional layers of protection such as request origin validation and enhanced logging to detect potential CSRF attack attempts.