CVE-2026-15814 in Mattermost
Summary
by MITRE • 09/14/2026
Mattermost versions 11.9.x <= 11.9.0, 11.8.x <= 11.8.4, 11.7.x <= 11.7.7, 10.11.x <= 10.11.22 fail to limit the amount of memory allocated when decoding uploaded image files which allows an authenticated user to cause excessive server memory consumption and potential denial of service via uploading a specially crafted image as a profile picture, channel file attachment, team icon, or custom brand image. Mattermost Advisory ID: MMSA-2026-00719
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Analysis
by VulDB Data Team • 09/14/2026
The vulnerability identified in Mattermost versions 11.9.x through 11.8.4 and earlier branches up to version 10.11.22 represents a critical resource exhaustion flaw rooted in the application's image processing subsystem. Specifically, the software fails to enforce strict limits on memory allocation when decoding uploaded image files for various user-facing components such as profile pictures, channel file attachments, team icons, or custom brand images. This deficiency allows an authenticated attacker to craft and upload maliciously constructed image files that trigger disproportionate memory consumption during the parsing and rendering phases. The core technical flaw lies in the lack of validation regarding the dimensions, pixel count, or metadata complexity of incoming image data before it is fully decoded into system memory. By exploiting this gap, a user can induce the server to allocate excessive amounts of Random Access Memory (RAM) relative to the file size on disk, leading to rapid resource depletion and potential service disruption for all users connected to the instance.
From an operational perspective, this vulnerability poses a significant risk to the availability and stability of Mattermost deployments. Since authentication is required to exploit the flaw, it classifies as an insider threat or compromised account scenario rather than an unauthenticated remote attack vector. However, in environments where user accounts may be shared, easily guessed, or subject to credential stuffing attacks, the barrier to entry for exploitation remains low enough to warrant immediate attention. The impact manifests primarily as a Denial of Service condition, where the server's memory usage spikes uncontrollably, potentially causing application crashes, restarts, or degradation in performance for legitimate users due to resource contention with other processes. In containerized environments such as Kubernetes or Docker, this could lead to pod evictions if memory limits are not strictly enforced at the infrastructure level, thereby disrupting continuous operations and requiring manual intervention to restore service stability.
This vulnerability aligns closely with Common Weakness Enumeration (CWE) category CWE-787: Out-of-bounds Write, although in this specific context, it more accurately reflects CWE-400: Uncontrolled Resource Consumption. The failure to limit memory allocation during image decoding is a classic example of insufficient input validation regarding resource-intensive operations. Furthermore, the exploitation technique maps directly to MITRE ATT&CK tactic T1496: Resource Hijacking, specifically under the subtechnique for denial-of-service via resource exhaustion. Attackers leverage this weakness not necessarily to gain unauthorized access or exfiltrate data, but to degrade service quality and availability, which is a common objective in disruptive cyberattacks against collaborative communication platforms. The advisory MMSA-2026-00719 highlights the necessity for robust input sanitization and resource governance within image processing libraries used by enterprise software.
To mitigate this vulnerability, administrators must upgrade Mattermost to patched versions that address the memory allocation limits in the image decoding pipeline. Until an upgrade is feasible, organizations should implement network-level controls such as Web Application Firewalls (WAF) or reverse proxy configurations that enforce strict size limits on uploaded files and validate MIME types rigorously before they reach the application layer. Additionally, deploying infrastructure-level safeguards like Kubernetes Horizontal Pod Autoscaling with conservative memory requests and limits can help contain the blast radius of an attack by isolating resource consumption to specific pods rather than affecting the entire cluster. Security teams should also audit user permissions to ensure that only trusted individuals have access to features involving image uploads, such as profile picture changes or channel file attachments, thereby reducing the potential attack surface for authenticated users who might attempt exploitation.