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Large language models (LLMs) have exhibited remarkable fluency across various tasks. However, their unethical applications, such as disseminating disinformation, have become a growing concern. Although recent works have proposed a number of…

计算与语言 · 计算机科学 2024-10-07 James Wang , Ran Li , Junfeng Yang , Chengzhi Mao

Ensuring the security of large language models (LLMs) is an ongoing challenge despite their widespread popularity. Developers work to enhance LLMs security, but vulnerabilities persist, even in advanced versions like GPT-4. Attackers…

密码学与安全 · 计算机科学 2023-12-19 Aysan Esmradi , Daniel Wankit Yip , Chun Fai Chan

Textual backdoor attacks present a substantial security risk to Large Language Models (LLM). It embeds carefully chosen triggers into a victim model at the training stage, and makes the model erroneously predict inputs containing the same…

计算与语言 · 计算机科学 2024-07-08 Xinglin Li , Xianwen He , Yao Li , Minhao Cheng

Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text. However, with their rising prominence, the…

Large Language Models (LLMs) have revolutionized Artificial Intelligence (AI) services due to their exceptional proficiency in understanding and generating human-like text. LLM chatbots, in particular, have seen widespread adoption,…

密码学与安全 · 计算机科学 2024-02-14 Gelei Deng , Yi Liu , Yuekang Li , Kailong Wang , Ying Zhang , Zefeng Li , Haoyu Wang , Tianwei Zhang , Yang Liu

With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the security of LLMs in math solving ability. Instead of attacking…

计算与语言 · 计算机科学 2023-09-06 Zihao Zhou , Qiufeng Wang , Mingyu Jin , Jie Yao , Jianan Ye , Wei Liu , Wei Wang , Xiaowei Huang , Kaizhu Huang

Research shows that errors in natural language can be corrected by translating texts to another language and back using language models. We explore to what extent this latent correction capability extends to Automated Program Repair (APR)…

软件工程 · 计算机科学 2025-10-16 Fernando Vallecillos Ruiz , Anastasiia Grishina , Max Hort , Leon Moonen

Safety alignment in large language models (LLMs) is increasingly compromised by jailbreak attacks, which can manipulate these models to generate harmful or unintended content. Investigating these attacks is crucial for uncovering model…

密码学与安全 · 计算机科学 2025-05-26 Linbao Li , Yannan Liu , Daojing He , Yu Li

Large Language Models (LLMs) have performed exceptionally in various text-generative tasks, including question answering, translation, code completion, etc. However, the over-assistance of LLMs has raised the challenge of "jailbreaking",…

密码学与安全 · 计算机科学 2024-09-02 Sibo Yi , Yule Liu , Zhen Sun , Tianshuo Cong , Xinlei He , Jiaxing Song , Ke Xu , Qi Li

The proliferation of jailbreak attacks against large language models (LLMs) highlights the need for robust security measures. However, in multi-round dialogues, malicious intentions may be hidden in interactions, leading LLMs to be more…

密码学与安全 · 计算机科学 2025-05-26 Weiyang Guo , Jing Li , Wenya Wang , YU LI , Daojing He , Jun Yu , Min Zhang

The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilities to data poisoning attacks, where the adversary inserts…

Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This paper introduces Transient Turn Injection(TTI), a new multi-turn attack technique that…

密码学与安全 · 计算机科学 2026-04-24 Naheed Rayhan , Sohely Jahan

Large Language Models (LLMs) are increasingly popular, powering a wide range of applications. Their widespread use has sparked concerns, especially through jailbreak attacks that bypass safety measures to produce harmful content. In this…

密码学与安全 · 计算机科学 2025-12-25 Zhengchun Shang , Wenlan Wei , Weiheng Bai

Large Language Models (LLMs) are widely deployed in diverse real-world settings, yet remain vulnerable to jailbreaking, where prompt-based attacks bypass safety filters. We present THREAT (Targeted Harmful generation via Reframing and…

密码学与安全 · 计算机科学 2026-05-22 Shahnewaz Karim Sakib , Swati Kar , Anindya Bijoy Das

The increasing deployment of Large Language Models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper, we present a comprehensive study on the robustness of GPT…

计算与语言 · 计算机科学 2024-12-24 Yiyi Tao , Yixian Shen , Hang Zhang , Yanxin Shen , Lun Wang , Chuanqi Shi , Shaoshuai Du

Large language models (LLMs), such as ChatGPT, have emerged with astonishing capabilities approaching artificial general intelligence. While providing convenience for various societal needs, LLMs have also lowered the cost of generating…

计算与语言 · 计算机科学 2023-08-28 Zhenhua Wang , Wei Xie , Kai Chen , Baosheng Wang , Zhiwen Gui , Enze Wang

Safety, security, and compliance are essential requirements when aligning large language models (LLMs). However, many seemingly aligned LLMs are soon shown to be susceptible to jailbreak attacks. These attacks aim to circumvent the models'…

密码学与安全 · 计算机科学 2025-06-05 Chen Xiong , Xiangyu Qi , Pin-Yu Chen , Tsung-Yi Ho

Modern large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities for coding tasks including writing and reasoning about code. They improve upon previous neural network models of code, such as code2seq or…

机器学习 · 计算机科学 2023-11-23 Chi Zhang , Zifan Wang , Ravi Mangal , Matt Fredrikson , Limin Jia , Corina Pasareanu

Malicious attackers can exploit large language models (LLMs) by engaging them in multi-turn dialogues to achieve harmful objectives, posing significant safety risks to society. To address this challenge, we propose a novel defense…

Multi-turn jailbreak attacks simulate real-world human interactions by engaging large language models (LLMs) in iterative dialogues, exposing critical safety vulnerabilities. However, existing methods often struggle to balance semantic…