2,560+ academic papers on AI security, attacks, and defenses
Showing 921–940 of 1,220 papers
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Phishing and related cyber threats are becoming more varied and technologically advanced. Among these, email-based phishing remains the most dominant...
Shaowei Guan, Yu Zhai, Zhengyu Zhang +2 more
Large Language Models (LLMs) are increasingly vulnerable to adversarial attacks that can subtly manipulate their outputs. While various defense...
Shanmin Wang, Dongdong Zhao
Knowledge Distillation (KD) is essential for compressing large models, yet relying on pre-trained "teacher" models downloaded from third-party...
Lucas Fenaux, Christopher Srinivasa, Florian Kerschbaum
Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of...
Yanbo Dai, Zongjie Li, Zhenlan Ji +1 more
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, demonstrating human-level...
Ruoxi Cheng, Haoxuan Ma, Teng Ma +1 more
Large Vision-Language Models (LVLMs) exhibit powerful reasoning capabilities but suffer sophisticated jailbreak vulnerabilities. Fundamentally,...
Farhad Abtahi, Fernando Seoane, Iván Pau +1 more
Healthcare AI systems face major vulnerabilities to data poisoning that current defenses and regulations cannot adequately address. We analyzed eight...
Zichao Wei, Jun Zeng, Ming Wen +8 more
Software vulnerabilities are increasing at an alarming rate. However, manual patching is both time-consuming and resource-intensive, while existing...
Feilong Wang, Fuqiang Liu
The integration of large language models (LLMs) into automated driving systems has opened new possibilities for reasoning and decision-making by...
Guangke Chen, Yuhui Wang, Shouling Ji +2 more
Modern text-to-speech (TTS) systems, particularly those built on Large Audio-Language Models (LALMs), generate high-fidelity speech that faithfully...
Dennis Wei, Ronny Luss, Xiaomeng Hu +6 more
Large Language Models (LLMs) have become ubiquitous in everyday life and are entering higher-stakes applications ranging from summarizing meeting...
Fred Heiding, Simon Lermen
We present an end-to-end demonstration of how attackers can exploit AI safety failures to harm vulnerable populations: from jailbreaking LLMs to...
Jialin Wu, Kecen Li, Zhicong Huang +3 more
Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code...
Catherine Xia, Manar H. Alalfi
AI programming assistants have demonstrated a tendency to generate code containing basic security vulnerabilities. While developers are ultimately...
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...
Daniyal Ganiuly, Nurzhau Bolatbek
The increasing virtualization of fifth generation (5G) networks expands the attack surface of the user plane, making spoofing a persistent threat to...
Zexu Wang, Jiachi Chen, Zewei Lin +7 more
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract...
Yunfei Yang, Xiaojun Chen, Yuexin Xuan +3 more
Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific...
Kazuki Iwahana, Yusuke Yamasaki, Akira Ito +2 more
Backdoor attacks pose a critical threat to machine learning models, causing them to behave normally on clean data but misclassify poisoned data into...
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