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This survey paper addresses the emerging threat of reasoning-based backdoor attacks in large language models, proposing a new taxonomy to categorize these vulnerabilities into associative, passive, and active types. By analyzing the mechanisms that exploit advanced LLM reasoning capabilities for malicious purposes, the work establishes a foundational framework for developing targeted defense strategies against this specific class of security risks. The study is essential for researchers aiming to build secure and trustworthy AI systems as reasoning abilities continue to expand.
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 risks, as adversaries can exploit these capabilities to conduct backdoor attacks. Existing surveys on backdoor attacks and reasoning security offer comprehensive overviews but lack in-depth analysis of backdoor attacks and defenses targeting LLMs' reasoning abilities. In this paper, we take the first step toward providing a comprehensive review of reasoning-based backdoor attacks in LLMs by analyzing their underlying mechanisms, methodological frameworks, and unresolved challenges. Specifically, we introduce a new taxonomy that offers a unified perspective for summarizing existing approaches, categorizing reasoning-based backdoor attacks into associative, passive, and active. We also present defense strategies against such attacks and discuss current challenges alongside potential directions for future research. This work offers a novel perspective, paving the way for further exploration of secure and trustworthy LLM communities.
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 risks, as adversaries can exploit these capabilities to conduct backdoor attacks. Existing surveys on backdoor attacks and reasoning security offer comprehensive overviews but lack in-depth analysis of backdoor attacks and defenses targeting LLMs' reasoning abilities. In this paper, we take the first step toward providing a comprehensive review of reasoning-based backdoor attacks in LLMs by analyzing their underlying mechanisms, methodological frameworks, and unresolved challenges. Specifically, we introduce a new taxonomy that offers a unified perspective for summarizing existing approaches, categorizing reasoning-based backdoor attacks into associative, passive, and active. We also present defense strategies against such attacks and discuss current challenges alongside potential directions for future research. This work offers a novel perspective, paving the way for further exploration of secure and trustworthy LLM communities.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2510.07697 [cs.CR]
(or arXiv:2510.07697v2 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2510.07697 Focus to learn more arXiv-issued DOI via DataCite
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