2,104+ academic papers on AI security, attacks, and defenses
Showing 1341–1360 of 2,104 papers
Rebeka Toth, Tamas Bisztray, Richard Dubniczky
Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models (LLMs) to craft highly...
Rebeka Toth, Tamas Bisztray, Nils Gruschka
In this paper, we introduce a metadata-enriched generation framework (PhishFuzzer) that seeds real emails into Large Language Models (LLMs) to...
Yaw Osei Adjei, Frederick Ayivor, Davis Opoku
Business Email Compromise (BEC) is a sophisticated social engineering threat that manipulates organizational hierarchies, leading to significant...
Di Zhu, Chen Xie, Ziwei Wang +1 more
New York City reports over one hundred thousand motor vehicle collisions each year, creating substantial injury and public health burden. We present...
Herman Errico, Jiquan Ngiam, Shanita Sojan
The Model Context Protocol (MCP) replaces static, developer-controlled API integrations with more dynamic, user-driven agent systems, which also...
Jaehwan Park, Kyungchan Lim, Seonhye Park +1 more
The advent of Artificial Intelligence (AI), particularly large language models (LLMs), has revolutionized software development by enabling developers...
Trung Cuong Dang, David Mohaisen
Large language models, trained on massive corpora, are prone to verbatim memorization of training data, creating significant privacy and copyright...
Wei He, Kai Han, Hang Zhou +4 more
The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic...
Kaiyuan Zhang, Mark Tenenholtz, Kyle Polley +3 more
The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application...
Momoko Shiraishi, Yinzhi Cao, Takahiro Shinagawa
Command-line interface (CLI) fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of...
Jakub Hoscilowicz, Artur Janicki
We introduce the Adversarial Confusion Attack, a new class of threats against multimodal large language models (MLLMs). Unlike jailbreaks or targeted...
Sidahmed Benabderrahmane, James Cheney, Talal Rahwan
Advanced Persistent Threats (APTs) pose a significant challenge in cybersecurity due to their stealthy and long-term nature. Modern supervised...
Xuebo Qiu, Mingqi Lv, Yimei Zhang +4 more
Provenance-based threat hunting identifies Advanced Persistent Threats (APTs) on endpoints by correlating attack patterns described in Cyber Threat...
Sen Nie, Jie Zhang, Jianxin Yan +2 more
Adversarial attacks have evolved from simply disrupting predictions on conventional task-specific models to the more complex goal of manipulating...
David Amebley, Sayanton Dibbo
In the age of agentic AI, the growing deployment of multi-modal models (MMs) has introduced new attack vectors that can leak sensitive training data...
Abhijeet Pathak, Suvadra Barua, Dinesh Gudimetla +4 more
Large language models (LLMs) and autonomous coding agents are increasingly used to generate software across a wide range of domains. Yet a core...
Steven Peh
Large Language Models (LLMs) remain vulnerable to prompt injection attacks, representing the most significant security threat in production...
Angelo Gaspar Diniz Nogueira, Kayua Oleques Paim, Hendrio Bragança +2 more
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the...
Axel Constant, Mahault Albarracin, Karl J. Friston
This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the...
Yingjia Shang, Yi Liu, Huimin Wang +4 more
With the rapid advancement of retrieval-augmented vision-language models, multimodal medical retrieval-augmented generation (MMed-RAG) systems are...
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