CVE-2019-13372

What is this vulnerability?

CVE-2019-13372 is categorized as a critical Code Injection 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.

/web/Lib/Action/IndexAction.class.php in D-Link Central WiFi Manager CWM(100) before v1.03R0100_BETA6 allows remote attackers to execute arbitrary PHP code via a cookie because a cookie's username field allows eval injection, and an empty password bypasses authentication.

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-2019-13372

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

A command injection vulnerability exists in specific web applications utilizing older ThinkPHP/Action logic. Unauthenticated attackers inject OS commands via unvalidated URL routing parameters to take over the web server. Precogs API Security Engine tracks URL routing paths mapped to evaluation sinks.

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: Dynamic code evaluation
def process_data(user_input):
    # Taint sink: arbitrary code execution
    result = eval(user_input)
    return result

Secure Code Pattern

# ✅ SECURE: Safe parsing
import ast
def process_data(user_input):
    # Sanitized parsing: only evaluates literal structures
    result = ast.literal_eval(user_input)
    return result

How Precogs Detects This

Precogs AI Analysis Engine identifies unsafe dynamic code evaluation paths by tracking untrusted data into sinks like eval() and exec().\n