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相关论文: How Secure Are Large Language Models (LLMs) for Na…

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Large Language Models (LLMs) are increasingly becoming the preferred foundation platforms for many Natural Language Processing tasks such as Machine Translation, owing to their quality often comparable to or better than task-specific…

计算与语言 · 计算机科学 2024-03-18 Zhifan Sun , Antonio Valerio Miceli-Barone

As the pre-trained language models (PLMs) continue to grow, so do the hardware and data requirements for fine-tuning PLMs. Therefore, the researchers have come up with a lighter method called \textit{Prompt Learning}. However, during the…

计算与语言 · 计算机科学 2022-09-07 Yundi Shi , Piji Li , Changchun Yin , Zhaoyang Han , Lu Zhou , Zhe Liu

This study systematically analyzes the vulnerability of 36 large language models (LLMs) to various prompt injection attacks, a technique that leverages carefully crafted prompts to elicit malicious LLM behavior. Across 144 prompt injection…

Existing navigation decision support systems often perform poorly when handling non-predefined navigation scenarios. Leveraging the generalization capabilities of large language model (LLM) in handling unknown scenarios, this research…

人工智能 · 计算机科学 2025-02-25 Feng Ma , Xiu-min Wang , Chen Chen , Xiao-bin Xu , Xin-ping Yan

Large Language Model-based systems (LLM systems) are information and query processing systems that use LLMs to plan operations from natural-language prompts and feed the output of each successive step into the LLM to plan the next. This…

密码学与安全 · 计算机科学 2024-10-11 Fangzhou Wu , Ethan Cecchetti , Chaowei Xiao

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…

The advent of Large Language Models LLMs marks a milestone in Artificial Intelligence, altering how machines comprehend and generate human language. However, LLMs are vulnerable to malicious prompt injection attacks, where crafted inputs…

计算与语言 · 计算机科学 2024-10-29 Sahasra Kokkula , Somanathan R , Nandavardhan R , Aashishkumar , G Divya

The integration of large language models (LLMs) into automated driving systems has opened new possibilities for reasoning and decision-making by transforming complex driving contexts into language-understandable representations. Recent…

机器学习 · 计算机科学 2025-11-19 Feilong Wang , Fuqiang Liu

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to input perturbations, such as typographical errors or slight…

计算与语言 · 计算机科学 2026-05-27 Lin Mu , Guowei Chu , Li Ni , Lei Sang , Yiwen Zhang

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.…

Most discussions about Large Language Model (LLM) safety have focused on single-agent settings but multi-agent LLM systems now create novel adversarial risks because their behavior depends on communication between agents and decentralized…

多智能体系统 · 计算机科学 2025-10-10 Rana Muhammad Shahroz Khan , Zhen Tan , Sukwon Yun , Charles Fleming , Tianlong Chen

Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts. This renders them susceptible to targeted adversarial…

密码学与安全 · 计算机科学 2023-05-08 Kai Greshake , Sahar Abdelnabi , Shailesh Mishra , Christoph Endres , Thorsten Holz , Mario Fritz

Large Language Models (LLMs) presents significant priority in text understanding and generation. However, LLMs suffer from the risk of generating harmful contents especially while being employed to applications. There are several black-box…

计算与语言 · 计算机科学 2023-12-11 Chengyuan Liu , Fubang Zhao , Lizhi Qing , Yangyang Kang , Changlong Sun , Kun Kuang , Fei Wu

Large Language Models (LLMs) have seen rapid adoption in recent years, with industries increasingly relying on them to maintain a competitive advantage. These models excel at interpreting user instructions and generating human-like…

密码学与安全 · 计算机科学 2025-09-09 Andrew Yeo , Daeseon Choi

Large Language Models (LLMs) are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient tool for resource-scarce organizations to adopt LLMs for their own purposes. At the same…

密码学与安全 · 计算机科学 2026-02-27 Piyush Jaiswal , Aaditya Pratap , Shreyansh Saraswati , Harsh Kasyap , Somanath Tripathy

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

Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. However, their capabilities can be exploited through prompt injection attacks. These attacks…

人工智能 · 计算机科学 2024-03-11 Xiaogeng Liu , Zhiyuan Yu , Yizhe Zhang , Ning Zhang , Chaowei Xiao

Large Language Models (LLMs) remain vulnerable to prompt injection attacks, representing the most significant security threat in production deployments. We present Prompt Fencing, a novel architectural approach that applies cryptographic…

密码学与安全 · 计算机科学 2025-11-26 Steven Peh

Large language models (LLMs) have demonstrated impressive results on natural language tasks, and security researchers are beginning to employ them in both offensive and defensive systems. In cyber-security, there have been multiple research…

密码学与安全 · 计算机科学 2024-03-05 Jiacen Xu , Jack W. Stokes , Geoff McDonald , Xuesong Bai , David Marshall , Siyue Wang , Adith Swaminathan , Zhou Li

Large language models (LLMs) have gained widespread adoption across diverse applications due to their impressive generative capabilities. Their plug-and-play nature enables both developers and end users to interact with these models through…

密码学与安全 · 计算机科学 2025-10-21 Zongze Li , Jiawei Guo , Haipeng Cai