CVE-2017-12249

What is this vulnerability?

CVE-2017-12249 is categorized as a critical CWE-16 flaw with a CVSS base score of 9.1. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A vulnerability in the Traversal Using Relay NAT (TURN) server included with Cisco Meeting Server (CMS) could allow an authenticated, remote attacker to gain unauthenticated or unauthorized access to components of or sensitive information in an affected system. The vulnerability is due to an incorrect default configuration of the TURN server, which could expose internal interfaces and ports on the external interface of an affected system. An attacker could exploit this vulnerability by using a TURN server to perform an unauthorized connection to a Call Bridge, a Web Bridge, or a database cluster in an affected system, depending on the deployment model and CMS services in use. A successful exploit could allow the attacker to gain unauthenticated access to a Call Bridge or database cluster in an affected system or gain unauthorized access to sensitive meeting information in an affected system. To exploit this vulnerability, the attacker must have valid credentials for the TURN server of the affected system. This vulnerability affects Cisco Meeting Server (CMS) deployments that are running a CMS Software release prior to Release 2.0.16, 2.1.11, or 2.2.6. Cisco Bug IDs: CSCvf51127.

This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.

Risk Assessment

Impact on Systems

✅ Data Exfiltration: Attackers can extract sensitive data from backend databases, configuration files, or internal services.

✅ Authentication Bypass: Exploiting this flaw may allow unauthorized access to protected resources and administrative interfaces.

✅ Lateral Movement: Once initial access is gained, attackers can pivot to internal systems and escalate privileges.

How to Fix and Mitigate CVE-2017-12249

1. Apply Vendor Patches: Upgrade affected components to their latest, non-vulnerable versions immediately.
2. Implement Input Validation: Ensure all user-supplied data is validated, sanitized, and type-checked before processing.
3. Deploy Runtime Protection: Use Precogs continuous monitoring to detect exploitation attempts in real time.
4. Audit Dependencies: Review and update all third-party libraries and transitive dependencies.

Defending with Precogs AI

Precogs AI Analysis Engine identifies this vulnerability class through semantic code analysis powered by Code Property Graph (CPG) technology, performing inter-procedural taint tracking to detect injection flaws, broken authentication, and insecure data flows across your entire codebase.

Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.

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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.