CVE-2018-0349

What is this vulnerability?

CVE-2018-0349 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 Cisco SD-WAN Solution could allow an authenticated, remote attacker to overwrite arbitrary files on the underlying operating system of an affected device. The vulnerability is due to improper input validation of the request admin-tech command in the CLI of the affected software. An attacker could exploit this vulnerability by modifying the request admin-tech command in the CLI of an affected device. A successful exploit could allow the attacker to overwrite arbitrary files on the underlying operating system of an affected device and escalate their privileges to the root user. This vulnerability affects the following Cisco products if they are running a release of the Cisco SD-WAN Solution prior to Release 18.3.0: vBond Orchestrator Software, vEdge 100 Series Routers, vEdge 1000 Series Routers, vEdge 2000 Series Routers, vEdge 5000 Series Routers, vEdge Cloud Router Platform, vManage Network Management Software, vSmart Controller Software. Cisco Bug IDs: CSCvi69852, CSCvi69856.

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-2018-0349

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.