2,077+ academic papers on AI security, attacks, and defenses
Showing 1–20 of 62 papers
Clear filtersYanming Mu, Hao Hu, Feiyang Li +7 more
Retrieval-Augmented Generation (RAG) significantly mitigates the hallucinations and domain knowledge deficiency in large language models by...
Saket Sanjeev Chaturvedi, Joshua Bergerson, Tanwi Mallick
As large language models (LLMs) evolve into autonomous "AI scientists," they promise transformative advances but introduce novel vulnerabilities,...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Zichen Tang, Zirui Zhang, Qian Wang +3 more
Current Large Language Models (LLMs) are gradually exploited in practically valuable agentic workflows such as Deep Research, E-commerce...
Yi Ting Shen, Kentaroh Toyoda, Alex Leung
The Model Context Protocol (MCP) introduces a structurally distinct attack surface that existing threat frameworks, designed for traditional software...
Abhijeet Sahu, Shuva Paul, Richard Macwan
Cyber deception assists in increasing the attacker's budget in reconnaissance or any early phases of threat intrusions. In the past, numerous methods...
Kai Wang, Biaojie Zeng, Zeming Wei +7 more
With the rapid development of LLM-based multi-agent systems (MAS), their significant safety and security concerns have emerged, which introduce novel...
Elzo Brito dos Santos Filho
AI-assisted software generation has increased development speed, but it has also amplified a persistent engineering problem: systems that are...
G. Madan Mohan, Veena Kiran Nambiar, Kiranmayee Janardhan
We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured,...
Tatiana Chakravorti, Pranav Narayanan Venkit, Sourojit Ghosh +1 more
Generative AI tools are increasingly entering academic peer review workflows, raising questions about fairness, accountability, and the legitimacy of...
Zhihang Deng, Jiaping Gui, Weinan Zhang
Large Language Models (LLMs) are increasingly deployed as agentic systems that plan, memorize, and act in open-world environments. This shift brings...
Yanna Jiang, Delong Li, Haiyu Deng +4 more
Agentic systems increasingly rely on reusable procedural capabilities, \textit{a.k.a., agentic skills}, to execute long-horizon workflows reliably....
Kiarash Ahi, Vaibhav Agrawal, Saeed Valizadeh
Large Language Models (LLMs) & Generative AI are transforming cybersecurity, enabling both advanced defenses and new attacks. Organizations now use...
Boyang Ma, Hechuan Guo, Peizhuo Lv +5 more
Embodied AI systems (e.g., autonomous vehicles, service robots, and LLM-driven interactive agents) are rapidly transitioning from controlled...
Matic Korun
We propose a geometric taxonomy of large language model hallucinations based on observable signatures in token embedding cluster structure. By...
Varpu Vehomäki, Kimmo K. Kaski
Understanding cyber security is increasingly important for individuals and organizations. However, a lot of information related to cyber security can...
Viet Hoang Luu, Amirmohammad Pasdar, Wachiraphan Charoenwet +3 more
Modern fuzzers scale to large, real-world software but often fail to exercise the program states developers consider most fragile or...
Ashwath Vaithinathan Aravindan, Mayank Kejriwal
Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the...
Yunlong Lyu, Yixuan Tang, Peng Chen +4 more
Modern AI-integrated IDEs are shifting from passive code completion to proactive Next Edit Suggestions (NES). Unlike traditional autocompletion, NES...
Yilin Geng, Omri Abend, Eduard Hovy +1 more
It is not only what we ask large language models (LLMs) to do that matters, but also how we prompt. Phrases like "This is urgent" or "As your...
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