2,529+ academic papers on AI security, attacks, and defenses
Showing 201–220 of 294 papers
Clear filtersGirish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan +1 more
The rapid advancement of Audio Large Language Models (ALMs), driven by Neural Audio Codecs (NACs), has led to the emergence of highly realistic...
Robert Stanley, Avi Verma, Lillian Tsai +2 more
AI agents promise to serve as general-purpose personal assistants for their users, which requires them to have access to private user data (e.g.,...
Alankrit Chona, Igor Kozlov, Ambuj Kumar
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of...
Alankrit Chona, Igor Kozlov, Ambuj Kumar
We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of...
Divyesh Gabbireddy, Suman Saha
Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious...
Sarang Nambiar, Dhruv Pradhan, Ezekiel Soremekun
Pre-trained machine learning models (PTMs) are commonly provided via Model Hubs (e.g., Hugging Face) in standard formats like Pickles to facilitate...
Ali Al-Kaswan, Maksim Plotnikov, Maxim Hájek +3 more
Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive...
Jiamin Chang, Minhui Xue, Ruoxi Sun +3 more
Recent advances in embodied Vision-Language Agentic Systems (VLAS), powered by large vision-language models (LVLMs), enable AI systems to perceive...
Mikako Bito, Keita Nishimoto, Kimitaka Asatani +1 more
The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based multi-agent systems...
Euntae Kim, Soomin Han, Buru Chang
Large language models (LLMs) are increasingly used as co-authors in collaborative writing, where users begin with rough drafts and rely on LLMs to...
Kun Wang, Cheng Qian, Miao Yu +6 more
Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is...
MinJae Jung, YongTaek Lim, Chaeyun Kim +3 more
While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses....
Hugo Lyons Keenan, Christopher Leckie, Sarah Erfani
We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was...
Jiacheng Liang, Yao Ma, Tharindu Kumarage +5 more
Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical vulnerability: an...
Hanrui Luo, Shreyank N Gowda
Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only...
Ahson Saiyed, Sabrina Sadiekh, Chirag Agarwal
Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse...
Ruixuan Liu, David Evans, Li Xiong
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to...
Md Rysul Kabir, Zoran Tiganj
Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in...
Thamilvendhan Munirathinam
Current open-source prompt-injection detectors converge on two architectural choices: regular-expression pattern matching and fine-tuned transformer...
Sina Abdollahi, Mohammad M Maheri, Javad Forough +5 more
Large Language Model (LLM) agents provide powerful automation capabilities, but they also create a substantially broader attack surface than...
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