#performance evaluation

Eyeballer is a tool designed for network penetration testing to efficiently identify potentially vulnerable web hosts. By interpreting screenshots from tools such as EyeWitness or GoWitness, Eyeballer applies AI to pinpoint possible security weaknesses. It classifies various web elements, including outdated sites, login interfaces, expansive web apps, personal 404 pages, and parked domains, aiding in the discernment of significant and insignificant attack surfaces. This open-source project, featuring high binary accuracy and rigorous evaluation, presents both user-friendly and automated output formats. Users can customize model training and evaluation, contributing to sustained security performance enhancements.

Explore a robust tool for assessing LLM API performance through load and correctness tests. Learn how it benchmarks latency, throughput, and accuracy across platforms like OpenAI and Hugging Face, ensuring reliable token counting with LlamaTokenizer. Includes advanced usage insights for developing new LLM clients.