2,529+ academic papers on AI security, attacks, and defenses
Showing 1–13 of 13 papers
Clear filtersNikita Kezins, Urbas Ekka, Pascal Berrang +1 more
Guardrail Classifiers defend production language models against harmful behavior, but although results seem promising in testing, they provide no...
Giordano De Marzo, Alessandro Bellina, Claudio Castellano +2 more
Artificial intelligence safety research focuses on aligning individual language models with human values, yet deployed AI systems increasingly...
Krishak Aneja, Manas Mittal, Anmol Goel +2 more
Fine-tuning Large Language Models (LLMs) on benign narrow data can sometimes induce broad harmful behaviors, a vulnerability termed emergent...
Tianyuan Zhang, Peng Yue, Zihao Peng +8 more
Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse...
Wenxin Tang, Xiang Zhang, Junliang Liu +11 more
Automated vulnerability detection is a fundamental task in software security, yet existing learning-based methods still struggle to capture the...
Aleksandr Bowkis, Marie Davidsen Buhl, Jacob Pfau +1 more
A leading proposal for aligning artificial superintelligence (ASI) is to use AI agents to automate an increasing fraction of alignment research as...
Leo Linqian Gan, Jeffery Wu, Longyuan Ge +6 more
Autonomous LLM agents face a critical security risk known as workflow hijacking, where attackers subtly alter tool and skill invocations. Existing...
Guoxin Lu, Letian Sha, Qing Wang +4 more
The safety alignment of Large Language Models (LLMs) remains vulnerable to Harmful Fine-tuning (HFT). While existing defenses impose constraints on...
Siyuan Li, Aodu Wulianghai, Xi Lin +6 more
The increasing prevalence of Large Language Models (LLMs) in content creation has made distinguishing human-written textual content from...
Xinjie Shen, Rongzhe Wei, Peizhi Niu +6 more
Hidden malicious intent in multi-turn dialogue poses a growing threat to deployed large language models (LLMs). Rather than exposing a harmful...
Fabrice Harel-Canada, Amit Sahai
LLM watermarks must be detectable without compromising text quality, yet most existing schemes bias the next-token distribution and pay for detection...
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera +2 more
The open-source ecosystem has accelerated the democratization of Large Language Models (LLMs) through the public distribution of specialized Low-Rank...
Hanum Ko, Sangheum Yeon, Jong Hwan Ko +1 more
As DRAM scales in density and adopts 3D integration, raw fault rates increase and multi-bit errors are no longer rare. Such errors can severely...
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