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MongoDeepDive — Context-Aware Memory Leak Scanner & Exploit for CVE-2025-14847. | Kitploit
Tools/GitHubGitHub/tunahantekeoglu/mongodeepdive
Vulnerability ScannersExploitationInformation GatheringPenetration TestingRed TeamingDatabase Security
GitHubtunahantekeoglu/mongodeepdive

MongoDeepDive

Context-Aware Memory Leak Scanner & Exploit for CVE-2025-14847.

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28 months agoNot yet reviewed

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🌊 MongoDeepDive

Context-Aware MongoDB Wire Protocol Exploit (CVE-2025-14847) > Advanced Heuristics Analyzer for Red Team Operations

Python License Security

💀 Overview

MongoDeepDive is a high-performance, asynchronous vulnerability scanner and forensic analyzer designed to detect uninitialized memory leaks in MongoDB servers affected by CVE-2025-14847.

Unlike standard PoC scripts that merely check for response size, MongoDeepDive employs Shannon Entropy analysis and heuristic filtering to distinguish between empty memory padding (garbage) and high-value secrets (e.g., Private Keys, AWS Tokens, Passwords).

This tool is engineered for Security Architects and Red Teamers who need actionable intelligence, not just noise.

📺 Demo Output

root@kitploit:~
────────────────────────── MongoDeepDive - Tunahan Tekeoğlu ──────────────────────────
Scanning... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:02

[+] VULNERABLE: 192.168.1.15:27017 | Response Size: 65552 bytes
    └── SECRETS FOUND: 3 critical blocks extracted
       ➜ SECRET_EXPOSED (AWS_KEY)
       ➜ HIGH_ENTROPY (Key/Encrypted)
       ➜ SECRET_EXPOSED (DB_URI)

[+] VULNERABLE: 192.168.1.18:27017 | Response Size: 65552 bytes
    └── Leak confirmed, but memory content is currently empty/low-entropy.

Scan Complete. Report saved to mongo_audit.json

🚀 Key Features

  • 🧠 Heuristic Intelligence: Real-time entropy calculation to identify encrypted data or keys within leaked memory chunks.
  • ⚡ High-Performance AsyncIO: Scans hundreds of targets simultaneously with minimal resource footprint.
  • 🛡️ Smart Filtering: Automatically removes null bytes and filters out low-entropy "junk" data.
  • ⚗️ Hybrid Analysis: Reports both the raw leak size (Scanner Mode) and the analyzed content (Forensic Mode).
  • 👻 Stealth Mode: Includes a --safe flag for throttled, production-safe scanning.
  • JSON Export: Structured output for easy integration with vulnerability management platforms.

📦 Installation

root@kitploit:~
# Clone the repository
git clone https://github.com/tunahantekeoglu/MongoDeepDive.git

# Enter the directory
cd MongoDeepDive

# Install dependencies
pip install -r requirements.txt

(Note: Requires rich library for CLI visualization)

🛠️ Usage

1. Mass Scanning (List Mode)

Scan a list of targets with high concurrency and generate a JSON report:

root@kitploit:~
python3 mongo_deep_dive.py -l targets.txt -o report.json -c 50

2. Deep Extraction (Single Target)

Aggressively siphon memory from a confirmed target to hunt for secrets (e.g., sending 1000 packets):

root@kitploit:~
python3 mongo_deep_dive.py -t 192.168.1.10 -n 1000

3. Production / Stealth Mode

Enable throttling to avoid network saturation or service instability on legacy systems:

root@kitploit:~
python3 mongo_deep_dive.py -l targets.txt --safe

🧠 How It Works

  1. Payload Injection: Sends a malformed OP_COMPRESSED packet with a spoofed uncompressed size.
  2. Memory Leak: The vulnerable server allocates memory based on the spoofed size but fails to initialize it, returning raw heap data.
  3. Entropy Analysis: The tool calculates the Shannon Entropy of the returned bytes.
  • Entropy > 4.5: Likely Encrypted Data, Keys, or Compressed Strings.
  • Entropy < 3.0: Likely Padding, Logs, or Null Bytes.
  1. Pattern Matching: Regex filters are applied to identify specific patterns like AKIA... (AWS Keys) or eyJ... (JWTs).

⚠️ Disclaimer

For Educational and Authorized Testing Purposes Only.

This tool is intended for security research, authorized Red Team engagements, and vulnerability assessment. The author takes no responsibility for the misuse of this code. Accessing computer systems without permission is illegal.


Author: Tunahan Tekeoğlu

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