CVE-2026-4274

Summary

A medium-severity incorrect authorization vulnerability (CVE-2026-4274) has been identified in Mattermost collaboration software, specifically within the version 11.x release line. The vulnerability allows authenticated users to perform actions or access data belonging to other users or channels they are not members of (CWE-863).

Technical Details

The issue is classified under CWE-863 (Incorrect Authorization). Specific API endpoints within Mattermost v11 extract user-provided references (such as a Channel ID or Object ID) but fail to verify if the currently authenticated session token holds the required permissions over that specific object.

A missing authorization check leads directly to Insecure Direct Object Reference (IDOR), where a standard user can access or mutate resources belonging to other users.

Exploitation Context

• Vector: Remote / Network-based
• Authentication: Low (standard user account required)
• Complexity: Low
• Impact: Medium (Confidentiality and Integrity)

While the CVSS score is 5.4 (Medium), the business impact can be severe depending on the data exposed. A malicious insider can read private channel communications if the channel ID is guessable or enumerable.

Remediation

Mattermost administrators should immediately:

1. Apply the latest security patches provided by the vendor for the v11 release branch.
2. Review API access logs for anomalous behavior, such as a single standard user requesting disparate Channel IDs or User IDs in rapid succession (indicating enumeration attacks).
3. Enforce horizontal authorization at the database query level (e.g., scoping queries with WHERE user_id = {session.userId}) rather than relying on application-layer if/else checks.

Precogs AI Integration

The Precogs AI Code Security Platform excels at identifying horizontal authorization flaws (IDOR). By performing inter-procedural taint analysis across Go routes, Precogs maps the exact flow from incoming HTTP context to the database query, flagging endpoints that accept external IDs without correlating them with the trusted session context.

Vulnerability Code Signature

Attack Data Flow

Vulnerable Code Pattern

# ❌ VULNERABLE: Unsanitized Input Flow
def process_request(request):
    user_input = request.GET.get('data')
    # Taint sink: processing untrusted data
    execute_logic(user_input)
    return {"status": "success"}

Secure Code Pattern

# ✅ SECURE: Input Validation & Sanitization
def process_request(request):
    user_input = request.GET.get('data')
    
    # Sanitized boundary check
    if not is_valid_format(user_input):
        raise ValueError("Invalid input format")
        
    sanitized_data = sanitize(user_input)
    execute_logic(sanitized_data)
    return {"status": "success"}

How Precogs Detects This

Precogs AI Analysis Engine maps untrusted input directly to execution sinks to catch complex application security vulnerabilities.\n