2,077+ academic papers on AI security, attacks, and defenses
Showing 821–840 of 974 papers
Clear filtersShuai Li, Kejiang Chen, Jun Jiang +5 more
Large Language Models (LLMs) have demonstrated remarkable capabilities, but their training requires extensive data and computational resources,...
Sarah Egler, John Schulman, Nicholas Carlini
Large Language Model (LLM) providers expose fine-tuning APIs that let end users fine-tune their frontier LLMs. Unfortunately, it has been shown that...
Yang Feng, Xudong Pan
Malicious agents pose significant threats to the reliability and decision-making capabilities of Multi-Agent Systems (MAS) powered by Large Language...
Eduard Andrei Cristea, Petter Molnes, Jingyue Li
Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools...
Yuexiao Liu, Lijun Li, Xingjun Wang +1 more
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have gained significant attention due to their objective and verifiable...
Hanbin Hong, Shuya Feng, Nima Naderloui +6 more
Large Language Models (LLMs) have rapidly become integral to real-world applications, powering services across diverse sectors. However, their...
Ahmed Aly, Essam Mansour, Amr Youssef
Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these...
Issam Seddik, Sami Souihi, Mohamed Tamaazousti +1 more
As Large Language Models (LLMs) gain traction across critical domains, ensuring secure and trustworthy training processes has become a major concern....
Andrew Zhao, Reshmi Ghosh, Vitor Carvalho +4 more
Large language model (LLM) systems increasingly power everyday AI applications such as chatbots, computer-use assistants, and autonomous robots,...
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3 more
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires...
Fanchao Meng, Jiaping Gui, Yunbo Li +1 more
Modern Network Intrusion Detection Systems generate vast volumes of low-level alerts, yet these outputs remain semantically fragmented, requiring...
Edoardo Allegrini, Ananth Shreekumar, Z. Berkay Celik
Agentic AI systems, which leverage multiple autonomous agents and Large Language Models (LLMs), are increasingly used to address complex, multi-step...
Jianzhu Yao, Hongxu Su, Taobo Liao +4 more
Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little...
Qiushi Wu, Yue Xiao, Dhilung Kirat +3 more
Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project...
Yibo Peng, James Song, Lei Li +6 more
Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses almost exclusively...
Jonghyun Park, Minhyuk Seo, Jonghyun Choi
One of the key challenges of modern AI models is ensuring that they provide helpful responses to benign queries while refusing malicious ones. But...
Xin Zhao, Xiaojun Chen, Bingshan Liu +3 more
Large language models (LLMs) with Mixture-of-Experts (MoE) architectures achieve impressive performance and efficiency by dynamically routing inputs...
Yisen Wang, Yichuan Mo, Hongjun Wang +2 more
Despite the rapid progress of neural networks, they remain highly vulnerable to adversarial examples, for which adversarial training (AT) is...
Karthik Avinash, Nikhil Pareek, Rishav Hada
The increasing deployment of Large Language Models (LLMs) across enterprise and mission-critical domains has underscored the urgent need for robust...
Jiarui Li, Yuhan Chai, Lei Du +3 more
Rule-based network intrusion detection systems play a crucial role in the real-time detection of Web attacks. However, most existing works primarily...
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