Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

A new review argues that personalization in large language models introduces safety risks that existing research systematically overlooks, calling for a unified framework to address the intersection of the two fields [1]. The paper, submitted to arXiv in June 2026, presents what its authors describe as the first comprehensive, safety-aware review of personalized LLMs [1]. Large language models — neural networks trained on vast text corpora for generation, summarization, and analysis — have increasingly been adapted to individual users' preferences, contexts, and long-term interaction histories [3]. The mechanisms that make such adaptation possible, the authors contend, simultaneously broaden the safety landscape in ways the literature has not yet confronted [1][2]. Existing reviews typically treat personalization and safety as separate domains, leaving their intersection largely unexplored [1][2]. The new work organizes personalization along three dimensions: user representation, personalization paradigm, and evaluation [1][2]. It introduces a unified taxonomy of safety risks, analyzing vulnerabilities at the representation level and across eight mainstream paradigms — including prompting, retrieval augmentation, parameter fine-tuning, reinforcement learning, mixture-of-experts, pruning, agent frameworks, and multimodal personalization [1][2]. The review identifies three structural inadequacies in current research. Safety is evaluated as user-invariant rather than relational; personalization techniques are analyzed in isolation rather than in composition; and existing evaluation frameworks cannot capture emergent long-term risks [1][2]. These gaps matter because LLMs are foundational to modern chatbots and are being deployed across industry and academia for tasks ranging from language translation to decision-making [3][5]. Broader concerns about AI safety have intensified in recent years. In 2023, hundreds of AI experts and public figures signed a statement declaring that mitigating the risk of extinction from AI should be treated as a global priority alongside pandemics and nuclear war [4]. A 2022 survey of AI researchers found that a majority believed there was a 10 percent or greater chance that an inability to control AI would cause an existential catastrophe [4]. The new review does not address existential risk directly, but it situates personalized LLM safety within a growing body of work on alignment — the challenge of ensuring AI systems behave in ways compatible with human values [1][4]. Through a case study of OpenClaw, the authors analyze deployment trends in personalized agent ecosystems [1][2]. They synthesize mitigation strategies spanning the model lifecycle and summarize personalized datasets and evaluation methodologies [1][2]. The paper concludes by proposing a unified framework for developing safe personalized LLMs and outlining directions for future research [1][2].