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Survey details reasoning-based backdoor attacks in LLMs

By PulseAugur Editorial · [1 sources] ·

A new survey paper published on arXiv details reasoning-based backdoor attacks in large language models (LLMs). The paper introduces a novel taxonomy categorizing these attacks into associative, passive, and active types. It also outlines existing defense strategies and highlights challenges and future research directions for creating more secure and trustworthy LLM communities. AI

IMPACT Highlights potential security vulnerabilities in LLMs, guiding future research towards more robust defenses.

RANK_REASON The cluster contains a survey paper on a specific technical topic within AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details reasoning-based backdoor attacks in LLMs

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The cluster contains a survey paper on a specific technical topic within AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Man Hu, Xinyi Wu, Zhufeng Suo, Jinbo Feng, Linghui Meng, Yanhao Jia, Anh Tuan Luu, Shuai Zhao ·

    Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs

    arXiv:2510.07697v2 Announce Type: replace-cross Abstract: With the rise of advanced reasoning capabilities, large language models (LLMs) are receiving increasing attention. However, although reasoning improves LLMs' performance on downstream tasks, it also introduces new security…

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