From 5df7bc07b01cbe6cbcefc4c8c6fd211e53669d3c Mon Sep 17 00:00:00 2001
From: Daizong Liu <dzliu@stu.pku.edu.cn>
Date: Fri, 31 May 2024 17:25:12 +0800
Subject: [PATCH] update 5.31.2024

---
 README.md | 34 +++++++++++++++++++++-------------
 1 file changed, 21 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index 3cf5891..d518e63 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
 # Awesome-LVLM-Attack [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)
-A **continual** collection of papers related to Text-guided 3D Visual Grounding (T-3DVG).
+A **continual** collection of papers related to Attacks on Large-Vision-Language-Models (LVLMs).
 
-Text-guided 3D visual grounding (T-3DVG) aims to locate a specific object that semantically corresponds to a language query from a complicated 3D scene, has drawn increasing attention in the 3D research community over the past few years.
-T-3DVG presents great potential and challenges due to its closer proximity to the real world and the complexity of data collection and 3D point cloud source processing.
+Large vision-language models (LVLMs) have achieved significant success and demonstrated promising capabilities in various multimodal downstream tasks.
+Despite their remarkable capabilities, the increased complexity and deployment of LVLMs have also exposed them to various security threats and vulnerabilities, making the study of attacks on these models a critical area of research.
 
-In the T-3DVG community, we've summarized existing T-3DVG methods in our survey paper👍.
+Here, we've summarized existing LVLM Attack methods in our survey paper👍.
 
 
 > If you find some important work missed, it would be super helpful to let me know (`dzliu@stu.pku.edu.cn`). Thanks!
@@ -12,25 +12,33 @@ In the T-3DVG community, we've summarized existing T-3DVG methods in our survey
 > If you find our survey useful for your research, please consider citing:
 
 ```
-@article{liu2024survey,
-  title={A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directions},
-  author={Liu, Daizong and Liu, Yang and Huang, Wencan and Hu, Wei},
+@article{liu2024attack,
+  title={A Survey of Attacks on Large Vision-Language Models: Resources, Recent Advances, and Future Trends},
+  author={Liu, Daizong and Yang, Mingyu and Qu, Xiaoye and Hu, Wei},
   journal={arXiv preprint arXiv},
   year={2024}
 }
 ```
 
 **Table of Contents**
-- [Fully-supervised two-stage approach](#Fully-Supervised-Two-Stage)
-- [Fully-supervised one-stage approach](#Fully-Supervised-One-Stage)
-- [Weakly-supervised approach](#Weakly-supervised)
-- [Approaches using No Point Cloud Input](#Other-Modality)
-- [Approaches using LLMs](#LLMs-based)
+- [Adversarial Attacks](#Adversarial-Attack)
+- [Jailbreack Attacks](#Jailbreack-Attack)
+- [Prompt Injection](#Prompt-Injection)
+- [Data Poisoning](#Data-Poisoning)
 ---
 
-## Fully-Supervised-Two-Stage
+## Adversarial-Attack
 * **ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language** | [Github](https://github.com/daveredrum/ScanRefer)
   * Dave Zhenyu Chen, Angel X. Chang, Matthias Nießner
   * Technical University of Munich, Simon Fraser University
   * [ECCV2020] https://arxiv.org/abs/1912.08830
   * A dataset, two-stage approach, proposal-then-selection
+
+
+## Jailbreak-Attack
+
+
+## Prompt-Injection
+
+
+## Data-Poisoning