2,560+ academic papers on AI security, attacks, and defenses
Showing 221–240 of 982 papers
Clear filtersDavi Bonetto
State Space Models (SSMs) such as Mamba achieve linear-time sequence processing through input-dependent recurrence, but this mechanism introduces a...
Alexandre Le Mercier, Thomas Demeester, Chris Develder
State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while...
Haodong Zhao, Jinming Hu, Yijie Bai +6 more
Federated Language Model (FedLM) allows a collaborative learning without sharing raw data, yet it introduces a critical vulnerability, as every...
J Alex Corll
Prompt injection defenses are often framed as semantic understanding problems and delegated to increasingly large neural detectors. For the first...
Indranil Halder, Annesya Banerjee, Cengiz Pehlevan
Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial...
Nasim 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...
Scott Thornton
Retrieval-Augmented Generation (RAG) systems extend large language models (LLMs) with external knowledge sources but introduce new attack surfaces...
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...
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...
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,...
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...
Alexander Erlei, Lukas Meub
As AI agents increasingly act on behalf of human stakeholders in economic settings, understanding their behavior in complex market environments...
Junxian Li, Tu Lan, Haozhen Tan +2 more
Modern vision-language-model (VLM) based graphical user interface (GUI) agents are expected not only to execute actions accurately but also to...
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