2,529+ academic papers on AI security, attacks, and defenses
Showing 2381–2400 of 2,529 papers
Yuhao Sun, Zhuoer Xu, Shiwen Cui +4 more
Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful...
Kedong Xiu, Churui Zeng, Tianhang Zheng +6 more
Existing gradient-based jailbreak attacks typically optimize an adversarial suffix to induce a fixed affirmative response, e.g., ``Sure, here...
Paschal C. Amusuo, Dongge Liu, Ricardo Andres Calvo Mendez +3 more
Fuzz testing has become a cornerstone technique for identifying software bugs and security vulnerabilities, with broad adoption in both industry and...
Davide Gabrielli, Simone Sestito, Iacopo Masi
The current landscape of defensive mechanisms for LLMs is fragmented and underdeveloped, unlike prior work on classifiers. To further promote...
Zhaoyan Wang, Zheng Gao, Arogya Kharel +1 more
Graph Neural Networks (GNNs) are widely adopted in Web-related applications, serving as a core technique for learning from graph-structured data,...
Clara Maathuis, Kasper Cools
In a time of rapidly evolving military threats and increasingly complex operational environments, the integration of AI into military operations...
Milad Nasr, Yanick Fratantonio, Luca Invernizzi +7 more
As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level...
Niloofar Mireshghallah, Tianshi Li
The discourse on privacy risks in Large Language Models (LLMs) has disproportionately focused on verbatim memorization of training data, while a...
John Hawkins, Aditya Pramar, Rodney Beard +1 more
Large Language Models (LLMs) suffer from a range of vulnerabilities that allow malicious users to solicit undesirable responses through manipulation...
Youwei Bao, Shuhan Yang, Hyunsoo Yang
Deterministic pseudo random number generators (PRNGs) used in generative artificial intelligence (GAI) models produce predictable patterns vulnerable...
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13 more
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to...
Luoxi Tang, Yuqiao Meng, Ankita Patra +3 more
Large Language Models (LLMs) are intensively used to assist security analysts in counteracting the rapid exploitation of cyber threats, wherein LLMs...
Jaiden Fairoze, Sanjam Garg, Keewoo Lee +1 more
As large language models (LLMs) advance, ensuring AI safety and alignment is paramount. One popular approach is prompt guards, lightweight mechanisms...
Isha Gupta, Rylan Schaeffer, Joshua Kazdan +2 more
The field of adversarial robustness has long established that adversarial examples can successfully transfer between image classifiers and that text...
Luca Cotti, Idilio Drago, Anisa Rula +2 more
System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of...
Shoumik Saha, Jifan Chen, Sam Mayers +3 more
Code-capable large language model (LLM) agents are increasingly embedded into software engineering workflows where they can read, write, and execute...
Yinuo Liu, Ruohan Xu, Xilong Wang +2 more
Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect general...
Muhammad Faheemur Rahman, Wayne Burleson
Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for...
Xiangfang Li, Yu Wang, Bo Li
With the rapid advancement of large language models (LLMs), ensuring their safe use becomes increasingly critical. Fine-tuning is a widely used...
Zhengliang Shi, Ruotian Ma, Jen-tse Huang +14 more
Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. However, the principles and values that...
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