PromptBench - Ai Hacking Tool

What is PromptBench?

PromptBench is a PyTorch-based, open-source Python library developed by Microsoft Research Asia that streamlines comprehensive evaluation of LLMs—including generative and multimodal models—from multiple angles: functionality, robustness, and dynamic behavior under adversarial conditions.

Why PromptBench Matters:

• Unified Interface: Simplifies comparing LLMs across tasks, prompt methods, and adversarial scenarios with consistent APIs.

• Robustness-Centric: Specifically designed to evaluate vulnerabilities to adversarial prompts—a growing concern in LLM safety.

• Dynamic & Efficient: DyVal combats data leakage; PromptEval minimizes evaluation cost while still giving reliable insights.

• Extensible & Open: Researchers can add new models, tasks, metrics, and analysis tools—backed by thorough docs, tutorials, and leaderboard support.

Community & Future Roadmap

• Frequently updated:

  • Support for GPT‑4o, Gemini, etc. (May 2024)

  • Multi‑modal datasets and multi‑prompt evaluation (Mar–Aug 2024).

• Active development with examples, leaderboards, docs, and research integration .

• Encourages contributions—add new components via GitHub workflow with CLA.

Final Thoughts

PromptBench is more than a toolkit—it's a renaissance in LLM evaluation. By integrating:

• classical performance tests,

• adversarial resilience checks,

• dynamic evaluation pipelines, and

• cost-efficient prompt sampling,

…it provides an all-in-one, modular platform for researchers and engineers to benchmark, debias, and harden the next generation of LLMs.