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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

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

Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation of the input text. These so-called jailbreak prompts are designed to trick the LLM into…

计算与语言 · 计算机科学 2025-10-13 John Hawkins , Aditya Pramar , Rodney Beard , Rohitash Chandra

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…

The integration of Large Language Models (LLMs) like GPT-4o into robotic systems represents a significant advancement in embodied artificial intelligence. These models can process multi-modal prompts, enabling them to generate more…

机器人学 · 计算机科学 2024-09-10 Wenxiao Zhang , Xiangrui Kong , Conan Dewitt , Thomas Braunl , Jin B. Hong

The adoption of large language models (LLMs) in many applications, from customer service chat bots and software development assistants to more capable agentic systems necessitates research into how to secure these systems. Attacks like…

密码学与安全 · 计算机科学 2024-12-03 Erick Galinkin , Martin Sablotny

Although multimodal large language models (MLLMs) are increasingly deployed in real-world applications, their instruction-following behavior leaves them vulnerable to prompt injection attacks. Existing prompt injection methods predominantly…

计算机视觉与模式识别 · 计算机科学 2026-04-01 Meiwen Ding , Song Xia , Chenqi Kong , Xudong Jiang

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

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

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

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

Backdoor attacks are an insidious security threat against machine learning models. Adversaries can manipulate the predictions of compromised models by inserting triggers into the training phase. Various backdoor attacks have been devised…

计算与语言 · 计算机科学 2023-05-29 Xuanli He , Jun Wang , Benjamin Rubinstein , Trevor Cohn

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.…

Recent developments in adversarial attacks on deep learning leave many mission-critical natural language processing (NLP) systems at risk of exploitation. To address the lack of computationally efficient adversarial defense methods, this…

计算与语言 · 计算机科学 2024-10-17 Hao-Yuan Chang , Kang L. Wang

Prompt-based learning has been proved to be an effective way in pre-trained language models (PLMs), especially in low-resource scenarios like few-shot settings. However, the trustworthiness of PLMs is of paramount significance and potential…

计算与语言 · 计算机科学 2023-09-15 Zihao Tan , Qingliang Chen , Wenbin Zhu , Yongjian Huang

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

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) 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 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

While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to exhibit…

计算与语言 · 计算机科学 2024-03-05 Yue Deng , Wenxuan Zhang , Sinno Jialin Pan , Lidong Bing
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