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Large language models (LLMs), known for their capability in understanding and following instructions, are vulnerable to adversarial attacks. Researchers have found that current commercial LLMs either fail to be "harmless" by presenting…

密码学与安全 · 计算机科学 2023-10-05 Bocheng Chen , Advait Paliwal , Qiben Yan

Although many large language models (LLMs) have been trained to refuse harmful requests, they are still vulnerable to jailbreaking attacks which rewrite the original prompt to conceal its harmful intent. In this paper, we propose a new…

计算与语言 · 计算机科学 2024-06-10 Yihan Wang , Zhouxing Shi , Andrew Bai , Cho-Jui Hsieh

Large Language Models (LLMs) remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work presents a systematic taxonomy of existing jailbreak defenses across prompt-level,…

密码学与安全 · 计算机科学 2025-11-25 Ryan Wong , Hosea David Yu Fei Ng , Dhananjai Sharma , Glenn Jun Jie Ng , Kavishvaran Srinivasan

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis…

计算与语言 · 计算机科学 2024-12-18 Tarun Raheja , Nilay Pochhi , F. D. C. M. Curie

The widespread adoption of Large Language Models (LLMs), exemplified by OpenAI's ChatGPT, brings to the forefront the imperative to defend against adversarial threats on these models. These attacks, which manipulate an LLM's output by…

密码学与安全 · 计算机科学 2025-04-04 Amelia Kawasaki , Andrew Davis , Houssam Abbas

Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthcare, software engineering, and conversational systems.…

Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks that induce them to generate harmful content. More recent LLMs…

密码学与安全 · 计算机科学 2024-02-27 Neal Mangaokar , Ashish Hooda , Jihye Choi , Shreyas Chandrashekaran , Kassem Fawaz , Somesh Jha , Atul Prakash

Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety…

密码学与安全 · 计算机科学 2025-05-27 Qingsong Zou , Jingyu Xiao , Qing Li , Zhi Yan , Yuhang Wang , Li Xu , Wenxuan Wang , Kuofeng Gao , Ruoyu Li , Yong Jiang

Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security challenges, including the risk of 'jailbreak' attacks that…

密码学与安全 · 计算机科学 2024-01-31 Jie Li , Yi Liu , Chongyang Liu , Ling Shi , Xiaoning Ren , Yaowen Zheng , Yang Liu , Yinxing Xue

Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable…

机器学习 · 计算机科学 2024-06-17 Alexander Robey , Eric Wong , Hamed Hassani , George J. Pappas

The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to…

计算与语言 · 计算机科学 2023-10-20 Zhouxing Shi , Yihan Wang , Fan Yin , Xiangning Chen , Kai-Wei Chang , Cho-Jui Hsieh

Large Language Models (LLMs) have become central to numerous natural language processing tasks, but their vulnerabilities present significant security and ethical challenges. This systematic survey explores the evolving landscape of attack…

密码学与安全 · 计算机科学 2025-05-05 Zhiyu Liao , Kang Chen , Yuanguo Lin , Kangkang Li , Yunxuan Liu , Hefeng Chen , Xingwang Huang , Yuanhui Yu

Despite advances in AI alignment, large language models (LLMs) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries can modify prompts to induce unwanted behavior. While some defenses have been proposed, they have…

机器学习 · 计算机科学 2024-11-11 Andy Zhou , Bo Li , Haohan Wang

Over the past decade, there has been extensive research aimed at enhancing the robustness of neural networks, yet this problem remains vastly unsolved. Here, one major impediment has been the overestimation of the robustness of new defense…

人工智能 · 计算机科学 2023-10-31 Leo Schwinn , David Dobre , Stephan Günnemann , Gauthier Gidel

Large Language Models (LLMs) have achieved significantly advanced capabilities in understanding and generating human language text, which have gained increasing popularity over recent years. Apart from their state-of-the-art natural…

密码学与安全 · 计算机科学 2025-02-11 Yihe Zhou , Tao Ni , Wei-Bin Lee , Qingchuan Zhao

Large Language Models (LLMs) are powerful tools with profound societal impacts, yet their ability to generate responses to diverse and uncontrolled inputs leaves them vulnerable to adversarial attacks. While existing defenses often struggle…

计算与语言 · 计算机科学 2025-12-30 Samuel Simko , Mrinmaya Sachan , Bernhard Schölkopf , Zhijing Jin

The rapid progress of Large Language Models (LLMs) has opened up new opportunities across various domains and applications; yet it also presents challenges related to potential misuse. To mitigate such risks, red teaming has been employed…

密码学与安全 · 计算机科学 2025-06-10 Yifan Jiang , Kriti Aggarwal , Tanmay Laud , Kashif Munir , Jay Pujara , Subhabrata Mukherjee

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the…

计算与语言 · 计算机科学 2023-10-18 Erfan Shayegani , Md Abdullah Al Mamun , Yu Fu , Pedram Zaree , Yue Dong , Nael Abu-Ghazaleh

Large Language Models (LLMs), which bridge the gap between human language understanding and complex problem-solving, achieve state-of-the-art performance on several NLP tasks, particularly in few-shot and zero-shot settings. Despite the…

密码学与安全 · 计算机科学 2025-01-07 Shuai Zhao , Meihuizi Jia , Zhongliang Guo , Leilei Gan , Xiaoyu Xu , Xiaobao Wu , Jie Fu , Yichao Feng , Fengjun Pan , Luu Anh Tuan

Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adversarial prompts can bypass their safety measures. We propose…

计算与语言 · 计算机科学 2024-05-03 Mansi Phute , Alec Helbling , Matthew Hull , ShengYun Peng , Sebastian Szyller , Cory Cornelius , Duen Horng Chau
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