CVE-2018-19953

QNAP NAS File Station Cross-Site Scripting Vulnerability

Verified by Precogs Threat Research
Last Updated: May 24, 2022
Base Score
9.8CRITICAL

Executive Summary

CVE-2018-19953 is a critical severity vulnerability affecting appsec. It is classified as an undisclosed flaw. This vulnerability is actively being exploited in the wild.

Precogs AI Insight

"The primary vulnerability vector is rooted in within A cross-site, allowing bypassed validation checks on external interactions. Adversaries commonly weaponize this defect by trigger a denial of service state, crashing critical operational components. Precogs AI Analysis Engine leverages inter-procedural taint tracking to block malicious interactions before they reach production."

Exploit Probability (EPSS)
Elevated (31.5%)
Public POC
Available
Exploit Probability
High (84%)
Public POC
Actively Exploited
Affected Assets
appsecNVD Database

What is this vulnerability?

CVE-2018-19953 is categorized as a critical Cross-Site Scripting (XSS) flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A cross-site scripting vulnerability affecting QNAP NAS File Station could allow remote attackers to inject malicious code..

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

MetricValue
CVSS Base Score9.8 (CRITICAL)
Vector StringCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
PublishedMay 24, 2022
Last ModifiedMay 24, 2022
Related CWEsN/A

Impact on Systems

Session Hijacking: Attackers can steal active user session tokens (cookies) to impersonate the victim.

Phishing Execution: Malicious scripts can dynamically alter DOM content to present fraudulent login forms.

Worm Propagation: Stored XSS can spread autonomously as users visit the infected page.

How to fix this issue?

Implement the following strategic mitigations immediately to eliminate the attack surface.

1. Output Encoding Implement strict context-aware output encoding (HTML, JavaScript, Attribute, CSS) before rendering user data.

2. Content Security Policy (CSP) Deploy a rigorous CSP header to restrict script execution exclusively to trusted domains.

3. Framework Defenses Utilize native UI framework protections (e.g., React DOM escaping) and avoid dangerouslySetInnerHTML.

Vulnerability Signature

// Example DOM-based XSS vulnerability
const user_input = new URLSearchParams(window.location.search).get('q');
// VULNERABLE: Direct insertion into innerHTML
document.getElementById('results').innerHTML = "Results for: " + user_input; 

// EXPLOIT PAYLOAD: ?q=\<img src=x onerror=alert(document.cookie)\>

References and Sources

Vulnerability Code Signature

Attack Data Flow

StageDetail
SourceUntrusted User Input
VectorInput flows through the application logic without sanitization
SinkExecution or Rendering Sink
ImpactApplication compromise, Logic Bypass, Data Exfiltration

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

Is your system affected?

Precogs AI detects CVE-2018-19953 in compiled binaries, LLMs, and application layers — even without source code access.