A new tool called ffufai has been released, offering an innovative approach to web fuzzing by incorporating artificial intelligence. This wrapper for the popular web fuzzer ffuf leverages AI to intelligently select file extensions during the fuzzing process, improving the efficiency and effectiveness of web security testing.
Developed by the prominent hacker Joseph Thacker, ffufai integrates seamlessly with ffuf while adding the power of AI-driven decision-making. The tool can utilize either OpenAI’s GPT or Anthropic’s Claude AI models to analyze the target URL and its headers, suggesting relevant file extensions for more targeted fuzzing.
Key features of ffufai include:
- Automatic suggestion of relevant file extensions based on the target
- Support for both OpenAI and Anthropic AI models
- Compatibility with all existing ffuf parameters
- Customizable maximum number of suggested extensions
To use ffufai, users need to have Python 3.6 or later installed on their computer, along with the ffuf tool. They also need an API key from either OpenAI or Anthropic to access the AI capabilities. Setting up ffufai is a simple process that involves cloning the repository, installing the required Python packages, and setting up the API key as an environment variable.
Users can specify the target URL, wordlist, and other ffuf parameters as usual. The tool also introduces a few additional parameters, such as the ability to set the maximum number of extensions to suggest.
ffufai works in a similar way to ffuf, but with the added benefit of AI-powered suggestions. Users can input the URL they want to test and other necessary information, just as they would with ffuf. The tool then uses AI to enhance the testing process.
By combining the established capabilities of ffuf with AI-driven insights, ffufai has the potential to enhance web security testing processes, which allows for more intelligent and targeted fuzzing operations.
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