2,529+ academic papers on AI security, attacks, and defenses
Showing 1–6 of 6 papers
Clear filtersKrishak 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...
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...
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...
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