2,077+ academic papers on AI security, attacks, and defenses
Showing 401–420 of 2,077 papers
Scott Thornton
AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can...
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...
Shahriar Golchin, Marc Wetter
We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice. In isolation, we...
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...
Haodong Zhao, Jinming Hu, Gongshen Liu
Federated learning security research has predominantly focused on backdoor threats from a minority of malicious clients that intentionally corrupt...
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...
Aditi Prabakaran, Priyesh Shukla
Transient objects in casual multi-view captures cause ghosting artifacts in 3D Gaussian Splatting (3DGS) reconstruction. Existing solutions relied on...
Udbhav Prasad, Aniesh Chawla
Cryptographic digests (e.g., MD5, SHA-256) are designed to provide exact identity. Any single-bit change in the input produces a completely different...
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...
Huijia Lin, Kameron Shahabi, Min Jae Song
Language models now routinely produce text that is difficult to distinguish from human writing, raising the need for robust tools to verify content...
Manuel Cherep, Pranav M R, Pattie Maes +1 more
The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs)....
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...
David Puertolas Merenciano, Ekaterina Vasyagina, Raghav Dixit +4 more
LoRA adapters let users fine-tune large language models (LLMs) efficiently. However, LoRA adapters are shared through open repositories like Hugging...
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....
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