2,560+ academic papers on AI security, attacks, and defenses
Showing 181–200 of 1,937 papers
Clear filtersNay Myat Min, Long H. Pham, Jun Sun
Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant...
Pablo Mateo-Torrejón, Alfonso Sánchez-Macián
The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving...
Zihan Liu, Yizhen Wang, Rui Wang +2 more
Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for...
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2 more
Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial...
Zheng Wu, Yi Hua, Zhaoyuan Huang +8 more
The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via...
Zijun Feng, Yuming Feng, Yu Wang +4 more
Cross-chain bridges, the critical infrastructure of the multi-chain ecosystem, have become a primary target for attackers, resulting in over $2.8...
Mengnan Zhao, Lihe Zhang, Bo Wang +3 more
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant...
Víctor Mayoral-Vilches, María Sanz-Gómez, Francesco Balassone +6 more
As LLM-driven agents advance in cybersecurity, Jeopardy CTF benchmarks are approaching saturation and cyber ranges, the natural next evaluation...
Kaisheng Fan, Weizhe Zhang, Yishu Gao +2 more
Defending against backdoor attacks in large language models remains a critical practical challenge. Existing defenses mitigate these threats but...
Zonghao Ying, Haozheng Wang, Jiangfan Liu +5 more
Large Language Model (LLM) agents are increasingly used to automate complex workflows, but integrating untrusted external data with privileged...
Jeffrey Wong, Antoine Creux
Create an idea, prototype it, evaluate if users like it, then learn. It is the circle of business. If AI can operate in all parts of the circle, it...
Xinhe Wang, Katia Sycara, Yaqi Xie
Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim...
Eungyu Woo, Yooshin Kim, Wonje Heo +1 more
Industrial Control Systems (ICS) integrate computing, physical processes, and communication to operate critical infrastructures such as power grids,...
Sijia Li, Min Gao, Zongwei Wang +3 more
Sequential recommendation seeks to model the evolution of user interests by capturing temporal user intent and item-level transition patterns....
Jiaqi Li, Yang Zhao, Bin Sun +3 more
Autonomous AI agents deployed on platforms such as OpenClaw face prompt injection, memory poisoning, supply-chain attacks, and social engineering,...
Priyal Deep, Shane Emmons, Amy Fox +3 more
LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that...
Kato Mivule
This paper extends the Classification Error Gauge (x-CEG) framework, originally developed for measuring the privacy-utility trade-off in tabular...
Qi Li, Bo Yin, Weiqi Huang +6 more
Vision-Language-Action (VLA) models are emerging as a unified substrate for embodied intelligence. This shift raises a new class of safety...
Yu Cui, Ruiqing Yue, Hang Fu +6 more
With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM)...
Rong Xiang
Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally...
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