CVE-2018-0125

What is this vulnerability?

CVE-2018-0125 is categorized as a critical Improper Input Validation flaw with a CVSS base score of 9.8. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A vulnerability in the web interface of the Cisco RV132W ADSL2+ Wireless-N VPN and RV134W VDSL2 Wireless-AC VPN Routers could allow an unauthenticated, remote attacker to execute arbitrary code and gain full control of an affected system, including issuing commands with root privileges. The attacker could also cause an affected system to reload, resulting in a denial of service (DoS) condition. The vulnerability is due to an incomplete input validation on user-controlled input in an HTTP request to the targeted device. An attacker could exploit this vulnerability by sending a crafted HTTP request to an affected system. A successful exploit could allow the attacker to execute arbitrary code as the root user and gain full control of the affected system or cause it to reload, resulting in a DoS condition. This vulnerability is fixed in firmware version 1.0.1.11 for the following Cisco products: RV132W ADSL2+ Wireless-N VPN Router and RV134W VDSL2 Wireless-AC VPN Router. Cisco Bug IDs: CSCvg92737, CSCvh60170.

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-2018-0125

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

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.\n