CVE-2017-12238

What is this vulnerability?

CVE-2017-12238 is categorized as a medium CWE-399 flaw with a CVSS base score of 6.5. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A vulnerability in the Virtual Private LAN Service (VPLS) code of Cisco IOS 15.0 through 15.4 for Cisco Catalyst 6800 Series Switches could allow an unauthenticated, adjacent attacker to cause a C6800-16P10G or C6800-16P10G-XL type line card to crash, resulting in a denial of service (DoS) condition. The vulnerability is due to a memory management issue in the affected software. An attacker could exploit this vulnerability by creating a large number of VPLS-generated MAC entries in the MAC address table of an affected device. A successful exploit could allow the attacker to cause a C6800-16P10G or C6800-16P10G-XL type line card to crash, resulting in a DoS condition. This vulnerability affects Cisco Catalyst 6800 Series Switches that are running a vulnerable release of Cisco IOS Software and have a Cisco C6800-16P10G or C6800-16P10G-XL line card in use with Supervisor Engine 6T. To be vulnerable, the device must also be configured with VPLS and the C6800-16P10G or C6800-16P10G-XL line card needs to be the core-facing MPLS interfaces. Cisco Bug IDs: CSCva61927.

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-2017-12238

1. Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Apply updates per vendor instructions.
2. Verify Patch Deployment: Confirm all instances are updated using Precogs continuous monitoring.
3. Review Audit Logs: Investigate historical access logs for indicators of compromise related to this attack surface.
4. Implement Defense-in-Depth: Deploy WAF rules, network segmentation, and endpoint detection to limit blast radius.

Defending with Precogs AI

VPLS packet processing lacks sufficient bounds checking when handling MAC address learning. Attackers flood the network with spoofed VPLS frames to exhaust memory and cause a denial of service. Precogs Binary Analysis intercepts unsafe memory allocations in Layer 2 protocols.

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.\n