2,077+ academic papers on AI security, attacks, and defenses
Showing 281–300 of 986 papers
Clear filtersPatrick Cooper, Alireza Nadali, Ashutosh Trivedi +1 more
Large language models (LLMs) are known to exhibit brittle behavior under adversarial prompts and jailbreak attacks, even after extensive alignment...
Najmul Hasan, Prashanth BusiReddyGari
The Uniform Resource Locator (URL), introduced in a connectivity-first era to define access and locate resources, remains historically limited,...
Rodrigo Tertulino, Ricardo Almeida, Laercio Alencar
The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training...
Ching-Yun Ko, Pin-Yu Chen
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for...
Zeming Wei, Zhixin Zhang, Chengcan Wu +3 more
Recent advancements in LLMs have led to significant breakthroughs in various AI applications. However, their sophisticated capabilities also...
Ali Mahdavi, Santa Aghapour, Azadeh Zamanifar +1 more
Existing Byzantine robust aggregation mechanisms typically rely on fulldimensional gradi ent comparisons or pairwise distance computations, resulting...
Alsharif Abuadbba, Nazatul Sultan, Surya Nepal +1 more
AI is moving from domain-specific autonomy in closed, predictable settings to large-language-model-driven agents that plan and act in open,...
Siqi Wen, Shu Yang, Shaopeng Fu +3 more
Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very...
Siqi Wen, Shu Yang, Shaopeng Fu +3 more
Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very...
Yilin Geng, Omri Abend, Eduard Hovy +1 more
It is not only what we ask large language models (LLMs) to do that matters, but also how we prompt. Phrases like "This is urgent" or "As your...
Yen-Shan Chen, Zhi Rui Tam, Cheng-Kuang Wu +1 more
Current evaluations of LLM safety predominantly rely on severity-based taxonomies to assess the harmfulness of malicious queries. We argue that this...
Max Manolov, Tony Gao, Siddharth Shukla +2 more
Large language models (LLMs) are increasingly used to assist developers with code, yet their implementations of cryptographic functionality often...
Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1 more
Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved...
Eliron Rahimi, Elad Hirshel, Rom Himelstein +3 more
Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering parallel decoding and...
Abhilekh Borah, Shubhra Ghosh, Kedar Joshi +2 more
Tasks such as solving arithmetic equations, evaluating truth tables, and completing syllogisms are handled well by large language models (LLMs) in...
Xinyi Hou, Shenao Wang, Yifan Zhang +4 more
Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that...
Manveer Singh Tamber, Hosna Oyarhoseini, Jimmy Lin
Research on adversarial robustness in language models is currently fragmented across applications and attacks, obscuring shared vulnerabilities. In...
Naen Xu, Hengyu An, Shuo Shi +7 more
Recent advancements in large language models (LLMs) have significantly enhanced the capabilities of collaborative multi-agent systems, enabling them...
Zeyuan He, Yupeng Chen, Lang Lin +7 more
Diffusion large language models (D-LLMs) offer an alternative to autoregressive LLMs (AR-LLMs) and have demonstrated advantages in generation...
Abhishek Mishra, Mugilan Arulvanan, Reshma Ashok +3 more
Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population...
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