2,529+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 715 papers
Clear filtersJi Guo, Xiaolong Qin, Cencen Liu +3 more
Vision-Language Models (VLMs) have achieved remarkable success in tasks such as image captioning and visual question answering (VQA). However, as...
Mingyu Luo, Zihan Zhang, Zesen Liu +7 more
Bring-Your-Own-Key (BYOK) agent architectures let users route LLM traffic through third-party relays, creating a critical integrity gap: a malicious...
Wenwei Zhao, Xiaowen Li, Yao Liu +1 more
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients upload manipulated updates to degrade the performance of the...
Jiajia Li, Xiaoyu Wen, Zhongtian Ma +3 more
The growing capabilities of large language models (LLMs) have driven their widespread deployment across diverse domains, even in potentially...
George Fatouros, Georgios Makridis, John Soldatos +18 more
European financial institutions face mounting regulatory pressure while their security operations centres remain constrained not by data or staffing...
Yanting Wang, Chenlong Yin, Ying Chen +1 more
Long-context large language models (LLMs)-for example, Gemini-3.1-Pro and Qwen-3.5-are widely used to empower many real-world applications, such as...
Prashant Kulkarni
Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where...
Bowen Sun, Chaozhuo Li, Yaodong Yang +2 more
Decompositional jailbreaks pose a critical threat to large language models (LLMs) by allowing adversaries to fragment a malicious objective into a...
Jona te Lintelo, Lichao Wu, Marina Krček +2 more
Mixture-of-Experts (MoE) architectures in Large Language Models (LLMs) have significantly reduced inference costs through sparse activation. However,...
Han Liu, Shanghao Shi, Yevgeniy Vorobeychik +2 more
Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved...
Zi Li, Tian Zhou, Wenze Li +3 more
Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although ''local...
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +1 more
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning,...
Mahshid Rezakhani, Nowfel Mashnoor, Kimia Azar +1 more
As large language models (LLMs) are increasingly fine-tuned for hardware tasks like RTL code generation, the scarcity of high-quality datasets often...
Soheil Khodayari, Xuenan Zhang, Bhupendra Acharya +1 more
As LLMs are increasingly integrated into systems that browse, retrieve, summarize, and act on web content, webpages have become an untrusted input...
Benjamin Probst, Andreas Happe, Jürgen Cito
Recent research has demonstrated the potential of Large Language Models (LLMs) for autonomous penetration testing, particularly when using...
Wenhao Lan, Shan Li, Junbin Yang +2 more
Safety-aligned language models must refuse harmful requests without collapsing into broad over-refusal, but the training-time mechanisms behind this...
Hanna Foerster, Ilia Shumailov, Cheng Zhang +3 more
Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static...
Shirin Alanova, Bogdan Minko, Sabrina Sadiekh +1 more
Safety mechanisms for large language models (LLMs) remain predominantly English-centric, creating systematic vulnerabilities in multilingual...
Mengyao Du, Han Fang, Haokai Ma +4 more
Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection...
Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2 more
Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the...
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