PandaBench PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies. The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges. Dataset Description This repository contains the benchmark results from extensive evaluations of various LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.
Use this model
Pull with QuantumShield
quantumshield pull Beijing-AISI/panda-bench Verify integrity
quantumshield verify Beijing-AISI/panda-bench pip install
pip install quantumshield && quantumshield pull Beijing-AISI/panda-bench Unverified Model
This model has not been PQC-verified. File integrity cannot be guaranteed against quantum threats.
README.md
panda-bench
PandaBench PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies. The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges. Dataset Description This repository contains the benchmark results from extensive evaluations of various LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.
Intended Uses
This model is registered on the QuantaMrkt quantum-safe registry. This model has not yet been PQC-verified.
Quick Start
# Install the CLI pip install quantumshield # Pull the model quantumshield pull Beijing-AISI/panda-bench # Verify file integrity quantumshield verify Beijing-AISI/panda-bench
About
PandaBench PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies. The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges. Dataset Description This repository contains the benchmark results from extensive evaluations of various LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.
Get this model
Pull with QuantumShield
quantumshield pull Beijing-AISI/panda-bench Verify signatures
quantumshield verify Beijing-AISI/panda-bench