2,529+ academic papers on AI security, attacks, and defenses
Showing 81–100 of 919 papers
Clear filtersZijun 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...
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,...
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,...
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...
Jialiang Wang, Yuchen Liu, Hang Xu +7 more
The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At...
Yuchen Shi, Xin Guo, Huajie Chen +3 more
Poisoning-based backdoor attacks pose significant threats to deep neural networks by embedding triggers in training data, causing models to...
Vishal Rajput
We prove that empirical risk minimisation (ERM) imposes a necessary geometric constraint on learned representations: any encoder that minimises...
Irti Haq, Belén Saldías
As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical....
Ari Azarafrooz
AI-agent guardrails are memoryless: each message is judged in isolation, so an adversary who spreads a single attack across dozens of sessions slips...
Mohammad Farhad, Shuvalaxmi Dass
Software security relies on effective vulnerability detection and patching, yet determining whether a patch fully eliminates risk remains an...
Mikko Lempinen, Joni Kemppainen, Niklas Raesalmi
As artificial intelligence (AI) systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of...
Krishiv Agarwal, Ramneet Kaur, Colin Samplawski +6 more
Effective safety auditing of large language models (LLMs) demands tools that go beyond black-box probing and systematically uncover vulnerabilities...
Hoang Nguyen, Lu Wang, Marta Gaia Bras
Freight brokerages negotiate thousands of carrier rates daily under dynamic pricing conditions where models frequently revise targets...
Abhijit Talluri
Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected...
Yuhang Wu, Qinyuan Liu, Qiuyang Zhao +1 more
Currently, Large Language Models (LLMs) feature a diversified architectural landscape, including traditional Transformer, GateDeltaNet, and Mamba....
Chao Pan, Yu Wu, Xin Yao
Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion...
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