2,529+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 625 papers
Clear filtersZihan Wang, Rui Zhang, Yu Liu +4 more
LLM agents increasingly rely on skills to encapsulate reusable capabilities via progressively disclosed instructions. High-quality skills inject...
Jiali Wei, Ming Fan, Guoheng Sun +3 more
The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent...
Guilin Deng, Silong Chen, Yuchuan Luo +6 more
Federated Large Language Models (FedLLMs) enable multiple parties to collaboratively fine-tune LLMs without sharing raw data, addressing challenges...
Jesse Zymet, Andy Luo, Swapnil Shinde +2 more
Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with...
Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis +2 more
The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the...
Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2 more
Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address...
Pranav Pallerla, Wilson Naik Bhukya, Bharath Vemula +1 more
Retrieval-augmented generation (RAG) systems are increasingly deployed in sensitive domains such as healthcare and law, where they rely on private,...
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....
Hanrui Luo, Shreyank N Gowda
Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only...
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...
Wentao Zhang, Yan Zhuang, ZhuHang Zheng +3 more
Existing jamming attacks on Retrieval-Augmented Generation (RAG) systems typically induce explicit refusals or denial-of-service behaviors, which are...
Jin Zhao, Marta Knežević, Tanja Käser
Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior...
Haochun Tang, Yuliang Yan, Jiahua Lu +2 more
Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the...
Meng Chen, Kun Wang, Li Lu +2 more
Modern Large audio-language models (LALMs) power intelligent voice interactions by tightly integrating audio and text. This integration, however,...
Fortunatus Aabangbio Wulnye, Justice Owusu Agyemang, Kwame Opuni-Boachie Obour Agyekum +3 more
Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things (IoT) environments,...
Andrii Vakhnovskyi
The United States designates Food and Agriculture as one of sixteen critical infrastructure sectors, yet no mandatory cybersecurity requirements...
Yingying Zhao, Chengyin Hu, Qike Zhang +7 more
Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies...
Jianhao Chen, Haoyang Chen, Hanjie Zhao +2 more
The rapid evolution of Vision-Language Models (VLMs) has catalyzed unprecedented capabilities in artificial intelligence; however, this continuous...
Junyu Ren, Xingjian Pan, Wensheng Gan +1 more
Prompt injection has emerged as a critical security threat to large language models (LLMs), yet existing studies predominantly focus on...
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