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Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants. These deployments are vulnerable to prompt injection and jailbreaking (collectively, prompt hacking), in…

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) 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

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…

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks,…

计算与语言 · 计算机科学 2024-11-11 Md Abdur Rahman , Fan Wu , Alfredo Cuzzocrea , Sheikh Iqbal Ahamed

LLM-integrated applications and agents are vulnerable to prompt injection attacks, where an attacker injects prompts into their inputs to induce attacker-desired outputs. A detection method aims to determine whether a given input is…

密码学与安全 · 计算机科学 2025-11-13 Yupei Liu , Yuqi Jia , Jinyuan Jia , Dawn Song , Neil Zhenqiang Gong

The proliferation of Large Language Models (LLMs) has introduced critical security challenges, where adversarial actors can manipulate input prompts to cause significant harm and circumvent safety alignments. These prompt-based attacks…

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

LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that evolves its strategies over hundreds of rounds and tested it against nine defense…

密码学与安全 · 计算机科学 2026-05-14 Priyal Deep , Shane Emmons , Amy Fox , Kyle Bacon , Kelley McAllister , Peter Ortiz , Krisztian Flautner

Large Language Models (LLMs) are seeing significant adoption in every type of organization due to their exceptional generative capabilities. However, LLMs are found to be vulnerable to various adversarial attacks, particularly prompt…

密码学与安全 · 计算机科学 2024-10-30 Md. Ahsan Ayub , Subhabrata Majumdar

Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to different prompt-based attacks, generating harmful content or sensitive information. Both closed-source and open-source LLMs are underinvestigated for these…

密码学与安全 · 计算机科学 2025-05-21 Jiawen Wang , Pritha Gupta , Ivan Habernal , Eyke Hüllermeier

Large Language Models (LLMs) are increasingly being integrated into the scientific peer-review process, raising new questions about their reliability and resilience to manipulation. In this work, we investigate the potential for hidden…

密码学与安全 · 计算机科学 2026-03-31 Matteo Gioele Collu , Umberto Salviati , Roberto Confalonieri , Mauro Conti , Giovanni Apruzzese

This report presents a real-world case study demonstrating how prompt injection can attack large language model platforms such as ChatGPT according to a proposed injection framework. By providing three real-world examples, we show how…

密码学与安全 · 计算机科学 2025-04-24 Xiangyu Chang , Guang Dai , Hao Di , Haishan Ye

Large Language Models (LLMs) are vulnerable to adversarial prompt based injects. These injects could jailbreak or exploit vulnerabilities within these models with explicit prompt requests leading to undesired responses. In the context of…

密码学与安全 · 计算机科学 2025-02-18 Jonathan Pan , Swee Liang Wong , Yidi Yuan , Xin Wei Chia

Large Language Models (LLMs), while powerful, are built and trained to process a single text input. In common applications, multiple inputs can be processed by concatenating them together into a single stream of text. However, the LLM is…

密码学与安全 · 计算机科学 2024-03-25 Keegan Hines , Gary Lopez , Matthew Hall , Federico Zarfati , Yonatan Zunger , Emre Kiciman

Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions…

密码学与安全 · 计算机科学 2025-12-16 Safwan Shaheer , G. M. Refatul Islam , Mohammad Rafid Hamid , Md. Abrar Faiaz Khan , Md. Omar Faruk , Yaseen Nur

Large Language Models (LLMs) have demonstrated exceptional proficiency in instruction-following, becoming increasingly crucial across various applications. However, this capability brings with it the risk of prompt injection attacks, where…

计算与语言 · 计算机科学 2023-11-28 Zekun Li , Baolin Peng , Pengcheng He , Xifeng Yan

Large language models and AI chatbots have been at the forefront of democratizing artificial intelligence. However, the releases of ChatGPT and other similar tools have been followed by growing concerns regarding the difficulty of…

密码学与安全 · 计算机科学 2024-02-05 Sippo Rossi , Alisia Marianne Michel , Raghava Rao Mukkamala , Jason Bennett Thatcher

The critical challenge of prompt injection attacks in Large Language Models (LLMs) integrated applications, a growing concern in the Artificial Intelligence (AI) field. Such attacks, which manipulate LLMs through natural language inputs,…

密码学与安全 · 计算机科学 2024-01-17 Xuchen Suo

Large Language Models (LLMs) are increasingly integrated into real-world applications, from virtual assistants to autonomous agents. However, their flexibility also introduces new attack vectors-particularly Prompt Injection (PI), where…

密码学与安全 · 计算机科学 2025-09-17 Mengxiao Wang , Yuxuan Zhang , Guofei Gu
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