Visual-Adversarial-Examples-Jailbreak-Large-Language-Models
Enrichment pendingRepository for the Paper (AAAI 2024, Oral) --- Visual Adversarial Examples Jailbreak Large Language Models
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Overview
Repository for the Paper (AAAI 2024, Oral) --- Visual Adversarial Examples Jailbreak Large Language Models
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- Languages
- python
Source: github.language · Jul 11, 2026
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README
Installation
We take MiniGPT-4 (13B) as the sandbox to showcase our attacks. The following installation instructions are adapted from the MiniGPT-4 repository.
1. Set up the environment
git clone https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models.git
cd Visual-Adversarial-Examples-Jailbreak-Large-Language-Models
conda env create -f environment.yml
conda activate minigpt4
2. Prepare the pretrained weights for MiniGPT-4
As we directly inherit the MiniGPT-4 code base, the guide from the MiniGPT-4 repository can also be directly used to get all the weights.
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Get Vicuna: MiniGPT-4 (13B) is built on the v0 version of Vicuna-13B. Please refer to this guide from the MiniGPT-4 repository to get the weights of Vicuna.
Then, set the path to the vicuna weight in the model config file here at Line 16.
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Get MiniGPT-4 (the 13B version) checkpoint: download from here.
Then, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigpt4_eval.yaml at Line 11.