In the ever-evolving landscape of cybersecurity, the emergence of BruteForceAI marks a significant advancement in penetration testing methodologies. Developed by Mor David, this innovative framework seamlessly integrates large language models (LLMs) with browser automation to autonomously identify login forms and execute sophisticated brute-force attacks. By harnessing AI-driven form analysis coupled with advanced evasion techniques and comprehensive logging, BruteForceAI streamlines credential-testing workflows, enabling security teams to uncover weak authentication mechanisms with unprecedented speed and efficiency.
Core Functionality of BruteForceAI
At the heart of BruteForceAI lies a meticulously designed two-stage process that enhances the precision and effectiveness of penetration testing:
Stage 1: Intelligent Form Detection
In this initial phase, BruteForceAI leverages an LLM, accessible via platforms like Ollama or Groq, to meticulously parse HTML content and accurately identify login form elements and selectors. This AI-driven analysis examines page structures, input fields, and submission endpoints, automatically generating precise CSS selectors essential for subsequent exploitation. Real-world tests have demonstrated an impressive accuracy rate of up to 95% in detecting login forms, significantly reducing the manual effort traditionally required in this process.
Stage 2: Smart Attack Execution
Building upon the insights gained from Stage 1, the Smart Attack phase initiates multi-threaded credential testing using the AI-discovered selectors. BruteForceAI offers users two distinct modes to tailor their attack strategies:
– Brute-Force Mode: This mode exhaustively cycles through a comprehensive list of username and password combinations, aiming to identify valid credentials through sheer volume.
– Password-Spray Mode: Designed to minimize account lockouts, this mode tests each password against a predefined set of usernames, distributing attempts in a manner that reduces detection risks.
To enhance the stealth and effectiveness of these attacks, BruteForceAI incorporates intelligent retry logic that adapts based on feedback learning and detects changes in the Document Object Model (DOM) to validate successful logins. Additionally, the tool employs synchronized delays, jitter, and human-like timing patterns to mimic genuine user behavior, effectively evading detection mechanisms.
Advanced Features Enhancing BruteForceAI’s Capabilities
BruteForceAI distinguishes itself from traditional brute-force tools through a suite of advanced features designed to optimize performance and evade detection:
– Multi-Threading: The tool supports 1 to over 100 threads, allowing for concurrent execution of multiple credential attempts. This multi-threaded approach significantly accelerates the testing process while maintaining synchronized delays between attempts to avoid triggering security defenses.
– Evasion Techniques: To further reduce the likelihood of detection, BruteForceAI implements several evasion strategies:
– Random User-Agent Rotation: By cycling through a variety of User-Agent strings, the tool prevents pattern recognition by security systems.
– Proxy Support: Utilizing proxies enables the tool to distribute requests across different IP addresses, complicating detection efforts.
– Configurable Jitter: Introducing random delays between requests mimics human behavior, making automated attacks less discernible.
– Browser Visibility Control: Adjusting browser visibility settings helps in evading detection by systems monitoring for automated browsing activities.
– Notifications and Logging: BruteForceAI ensures comprehensive monitoring and record-keeping through:
– Real-Time Webhook Alerts: Integration with platforms like Discord, Slack, Teams, and Telegram allows for immediate notifications upon successful credential discovery.
– SQLite Database Logging: Detailed records of every attempt are maintained in an SQLite database, facilitating thorough analysis and auditing.
– Verbose Output: Providing detailed console output enables users to monitor the tool’s operations in real-time.
– Operational Tools: To enhance usability and maintain optimal performance, BruteForceAI includes:
– Automatic Update Checks: Ensuring the tool remains current with the latest features and security patches.
– Network Retry Mechanism: Implementing robust retry logic to handle network interruptions gracefully.
– Database Cleanup: Offering commands to manage and clean the database, maintaining efficiency and organization.
– Colored Terminal Interface: Enhancing user experience through a visually intuitive command-line interface.
Installation and Configuration
Deploying BruteForceAI requires a system equipped with Python 3.8 or higher, Playwright browsers, and standard libraries such as `requests` and `PyYAML`. The installation process involves cloning the repository from GitHub and installing the necessary dependencies using pip.
Users have the flexibility to configure their preferred LLM provider:
– Local Deployment (Ollama): For balanced analysis, users can pull models like `llama3.2:3b`, or opt for `llama3.2:1b` for speed-optimized tasks.
– Cloud Deployment (Groq): By authenticating with an API key, users can select from models such as `llama-3.3-70b-versatile` (recommended) or alternatives like `gemma2-9b-it` for lightweight tasks.
Ethical Considerations and Legal Compliance
BruteForceAI is explicitly designed for authorized penetration testing, security research, and educational purposes. Unauthorized use against systems without explicit permission is both illegal and unethical. Organizations must ensure proper scope and authorization before deploying this tool. The developer disclaims any liability for misuse or illicit activities conducted using BruteForceAI.
Conclusion
By automating the detection of login forms and enriching brute-force methodologies with AI-driven intelligence and evasion techniques, BruteForceAI represents a significant evolution in credential testing tools. Its advanced features and user-centric design empower security professionals to identify and address weak authentication mechanisms more effectively, contributing to the overall strengthening of cybersecurity defenses.
