2,077+ academic papers on AI security, attacks, and defenses
Showing 161–180 of 341 papers
Clear filtersNasim Soltani, Shayan Nejadshamsi, Zakaria Abou El Houda +4 more
Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network...
Manit Baser, Alperen Yildiz, Dinil Mon Divakaran +1 more
The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing...
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...
Scott Thornton
Retrieval-Augmented Generation (RAG) systems extend large language models (LLMs) with external knowledge sources but introduce new attack surfaces...
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....
Pratyay Kumar, Miguel Antonio Guirao Aguilera, Srikathyayani Srikanteswara +2 more
Model Context Protocol (MCP) servers have rapidly emerged over the past year as a widely adopted way to enable Large Language Model (LLM) agents to...
Nanzi Yang, Weiheng Bai, Kangjie Lu
The Model Context Protocol (MCP) is a recently proposed interoperability standard that unifies how AI agents connect with external tools and data...
Eeham Khan, Luis Rodriguez, Marc Queudot
Retrieval-Augmented Generation (RAG) significantly improves the factuality of Large Language Models (LLMs), yet standard pipelines often lack...
Ailiya Borjigin, Igor Stadnyk, Ben Bilski +2 more
OpenClaw-style agent stacks turn language into privileged execution: LLM intents flow through tool interception, policy gates, and a local executor....
Fan Yang
The widespread adoption of thinking mode in large language models (LLMs) has significantly enhanced complex task processing capabilities while...
Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro, Peter Kairouz
As AI assistants become widely used, privacy-aware platforms like Anthropic's Clio have been introduced to generate insights from real-world AI use....
Jia Hu, Youcheng Sun, Pierre Olivier
Software compartmentalization breaks down an application into compartments isolated from each other: an attacker taking over a compartment will be...
Ali Raza, Gurang Gupta, Nikolay Matyunin +1 more
Warning: This article includes red-teaming experiments, which contain examples of compromised LLM responses that may be offensive or upsetting. Large...
Quanchen Zou, Moyang Chen, Zonghao Ying +6 more
Large Vision-Language Models (LVLMs) undergo safety alignment to suppress harmful content. However, current defenses predominantly target explicit...
Amir Al-Maamari
Large Language Models (LLMs) show promise for Automated Program Repair (APR), yet their effectiveness on security vulnerabilities remains poorly...
Shaswata Mitra, Raj Patel, Sudip Mittal +2 more
Multi-agent systems (MAS) powered by LLMs promise adaptive, reasoning-driven enterprise workflows, yet granting agents autonomous control over tools,...
Harry Owiredu-Ashley
Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to capture...
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....
Pratyay Kumar, Abu Saleh Md Tayeen, Satyajayant Misra +4 more
Deep learning (DL)-based Network Intrusion Detection System (NIDS) has demonstrated great promise in detecting malicious network traffic. However,...
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +3 more
Vision-language models are emerging for autonomous driving, yet their robustness to physical adversarial attacks remains unexplored. This paper...
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