2,077+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 798 papers
Clear filtersJianwei Li, Jung-Eun Kim
Backdoor attacks pose severe security threats to large language models (LLMs), where a model behaves normally under benign inputs but produces...
Chenlong Yin, Runpeng Geng, Yanting Wang +1 more
Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been...
Zheng Gao, Yifan Yang, Xiaoyu Li +4 more
Watermarking the initial noise of diffusion models has emerged as a promising approach for image provenance, but content-independent noise patterns...
Sihao Ding
We introduce Colluding LoRA (CoLoRA), an attack in which each adapter appears benign and plausibly functional in isolation, yet their linear...
Xiangkui Cao, Jie Zhang, Meina Kan +2 more
Large Vision-Language Models (LVLMs) have shown remarkable potential across a wide array of vision-language tasks, leading to their adoption in...
Darren Cheng, Wen-Kwang Tsao
Prompt injection remains one of the most practical attack vectors against LLM-integrated applications. We replicate the Microsoft LLMail-Inject...
Xinhai Wang, Shaopeng Fu, Shu Yang +3 more
Suffix jailbreak attacks serve as a systematic method for red-teaming Large Language Models (LLMs) but suffer from prohibitive computational costs,...
Davi 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...
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