CVE-2026-15077 in GitLab EE
Summary
by MITRE • 07/29/2026
GitLab has remediated an issue in GitLab EE affecting all versions from 19.1 before 19.1.3 and 19.2 before 19.2.1 that under certain conditions could have allowed an authenticated user to access information from unauthorized projects due to improper neutralization of untrusted content processed by the AI-assisted code review functionality.
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Analysis
by VulDB Data Team • 07/29/2026
This vulnerability resides within GitLab Enterprise Edition's AI-assisted code review feature, representing a critical information disclosure flaw that emerged in versions prior to 19.1.3 and 19.2.1. The issue stems from inadequate input validation and sanitization processes that fail to properly neutralize untrusted content when processing user-supplied data through the artificial intelligence code review system. Such improper handling creates a pathway for authenticated attackers to potentially access project information they should not legitimately possess, undermining the fundamental security boundaries that separate user access permissions within the platform.
The technical root cause manifests in how the AI code review functionality processes and interprets user-provided content without sufficient sanitization measures. When users interact with the AI-assisted features, untrusted data flows through processing pipelines that do not adequately validate or sanitize inputs before they are rendered or analyzed by the AI system. This vulnerability is classified as a weakness in input validation and output encoding practices, aligning with CWE-79 which describes improper neutralization of input during web application processing. The flaw essentially allows for cross-project data leakage through the AI processing layer where normal access controls should prevent such unauthorized information retrieval.
Operationally, this vulnerability presents significant risks to organizations relying on GitLab's enterprise capabilities, particularly in environments where multiple projects with varying security classifications coexist within the same instance. An authenticated user could potentially exploit this weakness to gain access to code repositories, configuration files, or other sensitive project artifacts belonging to other users or teams. The impact extends beyond simple information disclosure as it represents a privilege escalation vector that bypasses standard access control mechanisms through the AI processing pipeline rather than direct system exploitation. Attackers could leverage this vulnerability to gather intelligence for further attacks or to compromise the integrity of unauthorized projects.
Organizations should immediately upgrade to GitLab versions 19.1.3 or 19.2.1 and later to remediate this vulnerability, as these releases contain the necessary patches addressing the improper neutralization of untrusted content in the AI-assisted code review functionality. System administrators should also consider implementing additional monitoring controls around the AI processing features to detect anomalous usage patterns that might indicate exploitation attempts. The mitigation strategy should include comprehensive access control reviews and potentially limiting the scope of AI-assisted features for users with elevated privileges until further verification of system integrity is achieved. This vulnerability highlights the importance of secure input handling in AI-enabled applications and demonstrates how artificial intelligence processing can introduce new attack vectors when proper security controls are not implemented.