2,529+ academic papers on AI security, attacks, and defenses
Showing 241–260 of 345 papers
Clear filtersBiagio Montaruli, Luca Compagna, Serena Elisa Ponta +1 more
The rise of supply chain attacks via malicious Python packages demands robust detection solutions. Current approaches, however, overlook two critical...
Xinzheng Wu, Junyi Chen, Naiting Zhong +1 more
The safe deployment of autonomous driving systems (ADSs) relies on comprehensive testing and evaluation. However, safety-critical scenarios that can...
Yixuan Tang, Yi Yang
Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human...
Weiwei Wang
Catastrophic forgetting remains a fundamental challenge in continual learning for large language models. Recent work revealed that performance...
Rongzhe Wei, Peizhi Niu, Xinjie Shen +7 more
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches...
Cen Lu, Yung-Chen Tang, Andrea Cavallaro
Large Vision-Language Models (LVLMs) have shown impressive multimodal understanding capabilities, yet their robustness is poorly understood. In this...
Henry Onyeka, Emmanuel Samson, Liang Hong +3 more
The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated...
Neemesh Yadav, Francesco Ortu, Jiarui Liu +5 more
Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a...
Fouad Trad, Ali Chehab
Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in...
Yaw Osei Adjei, Frederick Ayivor, Davis Opoku
Business Email Compromise (BEC) is a sophisticated social engineering threat that manipulates organizational hierarchies, leading to significant...
Axel Constant, Mahault Albarracin, Karl J. Friston
This paper presents a computational account of how legal norms can influence the behavior of artificial intelligence (AI) agents, grounded in the...
Junbo Zhang, Ran Chen, Qianli Zhou +2 more
Large language models demonstrate powerful capabilities across various natural language processing tasks, yet they also harbor safety...
Onat Gungor, Roshan Sood, Jiasheng Zhou +1 more
Large Language Models (LLMs) are highly effective for cybersecurity question answering (QA) but are difficult to deploy on edge devices due to their...
Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen +1 more
The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant...
Swastik Bhattacharya, Sanjay Das, Anand Menon +3 more
Deep Neural Networks (DNNs) continue to grow in complexity with Large Language Models (LLMs) incorporating vast numbers of parameters. Handling these...
Samih Fadli
Large language model safety is usually assessed with static benchmarks, but key failures are dynamic: value drift under distribution shift, jailbreak...
Zhaoxin Zhang, Borui Chen, Yiming Hu +3 more
Recent research on large language model (LLM) jailbreaks has primarily focused on techniques that bypass safety mechanisms to elicit overtly harmful...
Zheyu Lin, Jirui Yang, Yukui Qiu +3 more
Evaluating the safety robustness of LLMs is critical for their deployment. However, mainstream Red Teaming methods rely on online generation and...
Quoc Viet Vo, Tashreque M. Haq, Paul Montague +3 more
Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better...
Mohammad Marufur Rahman, Guanchu Wang, Kaixiong Zhou +2 more
Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones....
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