smart-url-fuzzer
by avilum
An asynchronous and highly configurable URL fuzzing tool designed to quickly discover active endpoints on websites for penetration testing purposes.
Explore URLs of domains fast and efficiently using fuzzing techniques
Primary Use Case
This tool is primarily used by penetration testers and red teamers to efficiently explore and identify active URLs and endpoints on target websites using customizable fuzzing wordlists. It helps uncover potentially hidden or unlinked resources that could be vulnerable or useful for further security assessments.
- Asynchronous and fast URL fuzzing for efficient endpoint discovery
- Highly configurable with support for custom wordlists
- Automatic adjustment of concurrency workers to avoid blocking and bypass DDOS protections
- Robust error handling during fuzzing operations
- Logging of all activities to a dedicated logs folder
- Lightweight with minimal dependencies compared to similar tools
- Exports discovered endpoints to a file for further analysis
Installation
- git clone [email protected]:avilum/smart-url-fuzzer.git
- cd smart-url-fuzzer
- pip install -r requirements.txt
Usage
>_ ./fuzzRuns the fuzzer against the default or specified target URL to find all active endpoints.
>_ python fuzz.py --helpDisplays help information and usage options for the fuzz.py script.
>_ python fuzz.py -u https://example.com/Starts fuzzing the specified target website using the default wordlist.
>_ python fuzz.py -u https://www.facebook.com -l words_lists/list-php.txtFuzzes the target website using a custom wordlist file to discover PHP endpoints.
- Integrate with automated exploitation frameworks like Metasploit to streamline vulnerability validation.
- Use custom wordlists tailored to target environments for more effective endpoint discovery.
- Incorporate into CI/CD pipelines for continuous reconnaissance and early detection of exposed endpoints.
- Leverage its asynchronous capabilities to scale fuzzing operations without overwhelming target systems.
- Combine fuzzing results with web application firewalls (WAF) tuning to reduce false positives and improve detection.
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