Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Experiment with various prompts to discover the most effective ones, while monitoring the latency and expenses associated with each. Organize your prompts using dynamic variables and invoke them through an API, ensuring the variables are seamlessly integrated into the prompts. Leverage the strengths of multiple LLMs within your application to enhance functionality. Keep a detailed record of the latency and request costs to fine-tune your selections for optimal performance. Additionally, evaluate a range of prompts and archive the ones that yield the best results for future use. By doing so, you'll create a more efficient and effective system tailored to your needs.
Description
For those passionate about security, whether as a pentester or a cybersecurity researcher keen on discovering and exploiting vulnerabilities in AI technologies, LLMFuzzer serves as an ideal solution. This tool is designed to enhance the efficiency and effectiveness of your testing procedures. Comprehensive documentation is currently in development, which will include in-depth insights into the architecture, various fuzzing techniques, practical examples, and guidance on how to expand the tool's capabilities. Additionally, this resource aims to empower users to fully leverage LLMFuzzer's potential in their security assessments.
API Access
Has API
No
API Access
Has API
No
Integrations
JSON
No
Python
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Beakr
Website
www.beakr.dev/
Vendor Details
Company Name
LLMFuzzer
Website
github.com/mnns/LLMFuzzer