2,529+ academic papers on AI security, attacks, and defenses
Showing 61–80 of 272 papers
Clear filtersSarbartha Banerjee, Prateek Sahu, Anjo Vahldiek-Oberwagner +2 more
Rapid progress in generative AI has given rise to Compound AI systems - pipelines comprised of multiple large language models (LLM), software tools...
Frank Li
Tool-augmented LLM agents introduce security risks that extend beyond user-input filtering, including indirect prompt injection through fetched...
Chingkwun Lam, Jiaxin Li, Lingfei Zhang +1 more
Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong...
Raj Sanjay Shah, Jing Huang, Keerthiram Murugesan +2 more
Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the right to be...
Xiangwen Wang, Ananth Balashankar, Varun Chandrasekaran
Large language models remain vulnerable to jailbreak attacks, yet we still lack a systematic understanding of how jailbreak success scales with...
Zixun Xiong, Gaoyi Wu, Lingfeng Yao +3 more
Communication topology is a critical factor in the utility and safety of LLM-based multi-agent systems (LLM-MAS), making it a high-value intellectual...
Yu He, Haozhe Zhu, Yiming Li +4 more
LLM agents are highly vulnerable to Indirect Prompt Injection (IPI), where adversaries embed malicious directives in untrusted tool outputs to hijack...
Panagiotis Georgios Pennas, Konstantinos Papaioannou, Marco Guarnieri +1 more
Large Language Models (LLMs) rely on optimizations like Automatic Prefix Caching (APC) to accelerate inference. APC works by reusing previously...
Zhengyang Shan, Jiayun Xin, Yue Zhang +1 more
Code agents powered by large language models can execute shell commands on behalf of users, introducing severe security vulnerabilities. This paper...
Shriti Priya, Julian James Stephen, Arjun Natarajan
Enterprises and organizations today increasingly deploy in-house, cloud based applications and APIs for internal operations or external customers....
Eeham Khan, Luis Rodriguez, Marc Queudot
Retrieval-Augmented Generation (RAG) significantly improves the factuality of Large Language Models (LLMs), yet standard pipelines often lack...
Yinpeng Wu, Yitong Chen, Lixiang Wang +3 more
Device-side Large Language Models (LLMs) have witnessed explosive growth, offering higher privacy and availability compared to cloud-side LLMs....
Tzafrir Rehan
We present Test-Driven AI Agent Definition (TDAD), a methodology that treats agent prompts as compiled artifacts: engineers provide behavioral...
JV Roig
How much do large language models actually hallucinate when answering questions grounded in provided documents? Despite the critical importance of...
Yuhang Huang, Boyang Ma, Biwei Yan +5 more
The Model Context Protocol (MCP) is an open and standardized interface that enables large language models (LLMs) to interact with external tools and...
Neha Nagaraja, Hayretdin Bahsi
Large Language Models (LLMs) are increasingly integrated into safety-critical workflows, yet existing security analyses remain fragmented and often...
Punyajoy Saha, Sudipta Halder, Debjyoti Mondal +1 more
Safety alignment is critical for deploying large language models (LLMs) in real-world applications, yet most existing approaches rely on large...
Touseef Hasan, Blessing Airehenbuwa, Nitin Pundir +2 more
Large language models (LLMs) have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security...
Furkan Mumcu, Yasin Yilmaz
As Large Language Models (LLMs) transition into autonomous multi-agent ecosystems, robust minimax training becomes essential yet remains prone to...
Max Landauer, Wolfgang Hotwagner, Thorina Boenke +2 more
Log data are essential for intrusion detection and forensic investigations. However, manual log analysis is tedious due to high data volumes,...
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