CVE-2025-31363 in Mattermost
Summary
by MITRE • 04/16/2025
Mattermost versions 10.4.x <= 10.4.2, 10.5.x <= 10.5.0, 9.11.x <= 9.11.9 fail to restrict domains the LLM can request to contact upstream which allows an authenticated user to exfiltrate data from an arbitrary server accessible to the victim via performing a prompt injection in the AI plugin's Jira tool.
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Analysis
by VulDB Data Team • 09/30/2025
The vulnerability identified as CVE-2025-31363 affects Mattermost server versions within the 9.11.9, 10.4.2, and 10.5.0 release branches, representing a critical security flaw in the AI plugin's Jira integration functionality. This issue stems from insufficient domain restriction mechanisms that govern how the Large Language Model can communicate with external services. The flaw manifests when an authenticated user leverages prompt injection techniques to manipulate the AI plugin's behavior, specifically within the Jira tool integration, enabling unauthorized data exfiltration from arbitrary servers that are accessible to the victim system.
The technical implementation of this vulnerability resides in the AI plugin's lack of proper input validation and domain whitelisting controls for upstream requests. When the Jira tool integration processes user prompts, it fails to validate or restrict the domains to which the LLM can make outbound requests, creating an attack surface where maliciously crafted prompts can instruct the AI system to contact any server accessible from the Mattermost server environment. This represents a classic prompt injection vulnerability that falls under CWE-94, specifically related to code injection in AI systems. The vulnerability allows an attacker to bypass normal network security controls and access internal systems that would otherwise be protected by firewalls or network segmentation.
The operational impact of this vulnerability extends beyond simple data exfiltration, as it enables attackers to potentially access sensitive internal resources, including but not limited to database servers, internal APIs, or other networked systems that the Mattermost server can reach. An authenticated user with access to the Mattermost platform can exploit this weakness to perform reconnaissance activities, gather system information, or extract confidential data from connected services. The attack vector is particularly concerning because it leverages legitimate platform functionality to achieve malicious objectives, making detection more challenging and allowing the attacker to operate within the bounds of normal user behavior.
Organizations utilizing affected Mattermost versions face significant risk from this vulnerability, particularly those with complex network architectures or systems where the Mattermost server has access to sensitive internal resources. The security implications include potential data breaches, system compromise, and unauthorized access to confidential information. To mitigate this vulnerability, administrators should immediately upgrade to patched versions of Mattermost, implement network segmentation to limit the Mattermost server's access to sensitive internal systems, and consider implementing additional monitoring controls to detect unusual outbound network requests from the AI plugin. The ATT&CK framework categorizes this vulnerability under T1566 for phishing and T1041 for data exfiltration, with potential lateral movement implications if the attacker can access additional systems through the compromised Mattermost instance.