by Invicti-Security · Agent Tool · ★ 384
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brainstorm A smarter web fuzzing tool that combines local LLM models (via Ollama) and ffuf to optimize directory and file discovery. I wrote a blog post about the ideas behind this tool: Brainstorm tool release: Optimizing web fuzzing with local LLMs Short Description Combines traditional web fuzzing techniques with AI-powered path generation to discover hidden endpoints, files, and directories in web applications. Screenshot Long Description This tool enhances traditional web fuzzing by using local AI language models (via Ollama) to generate intelligent guesses for potential paths and filenames. It works by: Extracting initial links from the target website Using AI to analyze the structure and suggest new potential paths Fuzzing these paths using ffuf Learning from discoveries to generate more targeted suggestions Repeat There are 2 tools: : Main fuzzer focusing on general path discovery : Specialized variant for short filename discovery (e.g., legacy 8.3 format) Prerequisites Python 3.6+ ffuf (https://github.com/ffuf/ffuf) Ollama (https://ollama.ai) Required Python packages (see requirements.txt) Local Ollama models By default, the tool is using the model .
| Stars | 384 |
| Forks | 50 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 40.75/100 |
| Open Issues | 4 |
| Last Updated | 2024-11-26 |
| Created | 2024-11-24 |
| Platforms | browser, python |
| Est. Tokens | ~9k |
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brainstorm is A smarter web fuzzing tool that combines local LLM models and ffuf to optimize directory and file discovery. It is categorized as a Agent Tool with 384 GitHub stars.
brainstorm is primarily written in Python. It covers topics such as fuzzing, infosec, llm.
You can find installation instructions and usage details in the brainstorm GitHub repository at github.com/Invicti-Security/brainstorm. The project has 384 stars and 50 forks, indicating an active community.
The top alternatives to brainstorm on Agent Skills Hub include FuzzyAI, pentest-ai-agents, awesome-hacking-lists. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.