AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large
Language Models

AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models

23 October 2023

Bang An

Furong Huang

Papers citing "AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models"

8 / 8 papers shown

Title
Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction Y. Chen Haoran Li Yuan Sui Y. Liu Yufei He Y. Song Bryan Hooi AAML SILM 61 0 0 29 Apr 2025
Misaligned Roles, Misplaced Images: Structural Input Perturbations Expose Multimodal Alignment Blind Spots Erfan Shayegani G M Shahariar Sara Abdali Lei Yu Nael B. Abu-Ghazaleh Yue Dong AAML 44 0 0 01 Apr 2025
Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level Xinyi Zeng Yuying Shang Yutao Zhu Jingyuan Zhang Yu Tian AAML 48 2 0 09 Oct 2024
When LLMs Meet Cybersecurity: A Systematic Literature Review Jie Zhang Haoyu Bu Hui Wen Yu Chen Lun Li Hongsong Zhu 24 36 0 06 May 2024
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts Jiahao Yu Xingwei Lin Zheng Yu Xinyu Xing SILM 110 292 0 19 Sep 2023
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 278 3,784 0 18 Apr 2021
Gradient-based Adversarial Attacks against Text Transformers Chuan Guo Alexandre Sablayrolles Hervé Jégou Douwe Kiela SILM 98 225 0 15 Apr 2021