2,077+ academic papers on AI security, attacks, and defenses
Showing 81–100 of 113 papers
Clear filtersZimo Ji, Xunguang Wang, Zongjie Li +6 more
Large Language Model (LLM)-based agents with function-calling capabilities are increasingly deployed, but remain vulnerable to Indirect Prompt...
Gioliano de Oliveira Braga, Pedro Henrique dos Santos Rocha, Rafael Pimenta de Mattos Paixão +3 more
Wi-Fi Channel State Information (CSI) has been repeatedly proposed as a biometric modality, often with reports of high accuracy and operational...
Shaowei Guan, Hin Chi Kwok, Ngai Fong Law +3 more
Retrieval-augmented generation (RAG) has rapidly emerged as a transformative approach for integrating large language models into clinical and...
James Jin Kang, Dang Bui, Thanh Pham +1 more
The growing use of large language models in sensitive domains has exposed a critical weakness: the inability to ensure that private information can...
Gabrielle M Gauthier, Eesha Ali, Amna Asim +2 more
Human content moderators (CMs) routinely review distressing digital content at scale. Beyond exposure, the work context (e.g., workload, team...
Gian Maria Campedelli
While the possibility of reaching human-like Artificial Intelligence (AI) remains controversial, the likelihood that the future will be characterized...
Yuntao Wang, Siamak P. Najad-Davarani, Elizabeth Bossart +3 more
Background: Modern large language models (LLMs) offer powerful reasoning that converts narratives into structured, taxonomy-aligned data, revealing...
Qin Zhou, Zhexin Zhang, Zhi Li +1 more
With the rapid advancement of AI models, their deployment across diverse tasks has become increasingly widespread. A notable emerging application is...
Kathrin Grosse, Nico Ebert
Recent improvement gains in large language models (LLMs) have lead to everyday usage of AI-based Conversational Agents (CAs). At the same time, LLMs...
Dominik Schwarz
As Large Language Models (LLMs) are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust...
Robert A. Bridges, Thomas R. Mitchell, Mauricio Muñoz +1 more
The advent of Large Language Models (LLMs) promised to resolve the long-standing paradox in honeypot design, achieving high-fidelity deception with...
Xinwei Wu, Heng Liu, Jiang Zhou +5 more
Large Language Models (LLMs) have advanced machine translation but remain vulnerable to hallucinations. Unfortunately, existing MT benchmarks are not...
Bin Wang, Zexin Liu, Hao Yu +6 more
The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and...
Ronghao Ni, Aidan Z. H. Yang, Min-Chien Hsu +5 more
Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical...
Wu Yichao, Wang Yirui, Ding Panpan +3 more
With the wide application of deep reinforcement learning (DRL) techniques in complex fields such as autonomous driving, intelligent manufacturing,...
Wm. Matthew Kennedy, Cigdem Patlak, Jayraj Dave +10 more
AI systems have the potential to produce both benefits and harms, but without rigorous and ongoing adversarial evaluation, AI actors will struggle to...
Oleksandr Adamov, Anders Carlsson
This paper explores the challenges of cyberattack attribution, specifically APTs, applying the case study approach for the WhisperGate cyber...
Hanbin Hong, Shuya Feng, Nima Naderloui +6 more
Large Language Models (LLMs) have rapidly become integral to real-world applications, powering services across diverse sectors. However, their...
Francesco Giarrusso, Olga E. Sorokoletova, Vincenzo Suriani +1 more
Jailbreaking techniques pose a significant threat to the safety of Large Language Models (LLMs). Existing defenses typically focus on single-turn...
Zhenyu Mao, Jacky Keung, Fengji Zhang +3 more
The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs)....
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