CVE-2018-11759

The Apache Web Server (httpd) specific code that normalised the requested path before matching it to the URI-worker map in Apache Tomcat JK (mod_jk) Connector 1.

Verified by Precogs Threat Research
Last Updated: Mar 21, 2026
Base Score
9.8CRITICAL

Executive Summary

CVE-2018-11759 is a critical severity vulnerability affecting appsec. It is classified as an undisclosed flaw. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.

Precogs AI Insight

"Architecturally, this flaw occurs due to within The Apache Web Server (httpd) specific code, allowing the mishandling of memory allocation boundaries. A threat actor could leverage this oversight to inject malicious logic that alters the execution flow of the application engine. Precogs AI Analysis Engine utilizes semantic code analysis to safeguard the application against payload injection."

Exploit Probability (EPSS)
High (94.3%)
Public POC
Available
Exploit Probability
High (84%)
Public POC
Available
Affected Assets
appsecNVD Database

What is this vulnerability?

CVE-2018-11759 is categorized as a critical Application Verification Flaw flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

The Apache Web Server (httpd) specific code that normalised the requested path before matching it to the URI-worker map in Apache Tomcat JK (mod_jk) Connec.

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 StringN/A
PublishedMarch 21, 2026
Last ModifiedMarch 21, 2026
Related CWEsN/A

Impact on Systems

Unauthorized Access: Flaws in application logic can permit unauthorized interaction with protected APIs.

Data Manipulation: Adversaries may alter critical application states, such as user roles or configurations.

Service Disruption: Improper error handling or unvalidated inputs can lead to resource exhaustion.

How to fix this issue?

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

1. Defense in Depth Implement multi-layered validation (client-side, API gateway, and server-side).

2. Least Privilege Ensure backend service accounts operate with the absolute minimum rights required.

3. Security Regression Testing Integrate automated semantic security scanning into the deployment pipeline.

Vulnerability Signature

// Generic Application Security Flaw (Node.js)
app.post('/api/update-profile', (req, res) =\> \{
    // DANGEROUS: Mass Assignment / Object Injection
    // Attacker can pass \{ "isAdmin": true, "email": "..." \}
    User.update(\{ id: req.user.id \}, req.body);
    
    // SECURED: Explicitly select permitted fields
    const \{ email, displayName, bio \} = req.body;
    User.update(\{ id: req.user.id \}, \{ email, displayName, bio \});
\});

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-2018-11759 in compiled binaries, LLMs, and application layers — even without source code access.