2,077+ academic papers on AI security, attacks, and defenses
Showing 141–160 of 809 papers
Clear filtersYu Yin, Shuai Wang, Bevan Koopman +1 more
Large Language Models (LLMs) have emerged as powerful re-rankers. Recent research has however showed that simple prompt injections embedded within a...
Brennan Bell, Andreas Trügler, Konstantin Beyer +1 more
We study a sequential coherent side-channel model in which an adversarial probe qubit interacts with a target qubit during a hidden gate sequence....
Yuval Felendler, Parth A. Gandhi, Idan Habler +2 more
Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution...
Or Zamir
A natural and informal approach to verifiable (or zero-knowledge) ML inference over floating-point data is: ``prove that each layer was computed...
Xianglin Yang, Yufei He, Shuo Ji +2 more
Self-evolving LLM agents update their internal state across sessions, often by writing and reusing long-term memory. This design improves performance...
Varun Pratap Bhardwaj
We present SuperLocalMemory, a local-first memory system for multi-agent AI that defends against OWASP ASI06 memory poisoning through architectural...
Jiajun Xu, Jiageng Mao, Ang Qi +5 more
Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI...
Mitchell Piehl, Zhaohan Xi, Zuobin Xiong +2 more
Large language models (LLMs) are increasingly augmented with long-term memory systems to overcome finite context windows and enable persistent...
Chengzhi Hu, Jonas Dornbusch, David Lüdke +2 more
Adversarial training for LLMs is one of the most promising methods to reliably improve robustness against adversaries. However, despite significant...
Xander Davies, Giorgi Giglemiani, Edmund Lau +3 more
Frontier LLMs are safeguarded against attempts to extract harmful information via adversarial prompts known as "jailbreaks". Recently, defenders have...
Yohan Lee, Jisoo Jang, Seoyeon Choi +2 more
Tool-using LLM agents increasingly coordinate real workloads by selecting and chaining third-party tools based on text-visible metadata such as tool...
Lukas Struppek, Adam Gleave, Kellin Pelrine
As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on...
In Chong Choi, Jiacheng Zhang, Feng Liu +1 more
Multi-turn jailbreak attacks are effective against text-only large language models (LLMs) by gradually introducing malicious content across turns....
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai +6 more
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers. This openness introduces a security...
Xiaojun Jia, Jie Liao, Simeng Qin +5 more
Agent skills are becoming a core abstraction in coding agents, packaging long-form instructions and auxiliary scripts to extend tool-augmented...
Mario Marín Caballero, Miguel Betancourt Alonso, Daniel Díaz-López +3 more
The most valuable asset of any cloud-based organization is data, which is increasingly exposed to sophisticated cyberattacks. Until recently, the...
Somnath Banerjee
The overarching research direction of this work is the development of a ''Responsible Intelligence'' framework designed to reconcile the immense...
Yuqi Jia, Ruiqi Wang, Xilong Wang +2 more
Prompt injection attacks insert malicious instructions into an LLM's input to steer it toward an attacker-chosen task instead of the intended one....
Ruomeng Ding, Yifei Pang, He Sun +3 more
Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-language...
Weiming Song, Xuan Xie, Ruiping Yin
Large language models (LLMs) remain vulnerable to jailbreak prompts that elicit harmful or policy-violating outputs, while many existing defenses...
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