What does this research mean for the field?

Large Language Models face significant data security risks due to their reliance on massive, uncurated training data, necessitating comprehensive defense strategies and future research in areas like data provenance and verifiable machine forgetting to ensure safe deployment. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To provide a comprehensive analysis of data-centric security vulnerabilities, defense techniques, evaluation datasets, and emerging research directions for large language models.

August 27, 2026Journal of King Saud University - Computer and Information Sciences0 citationsOpen Access

Data security in large language models: risks, defense, and directions

KCKang Chen XZXiuze Zhou YYYuanhui Yu

Key Points

To provide a comprehensive analysis of data-centric security vulnerabilities, defense techniques, evaluation datasets, and emerging research directions for large language models.
Categorized primary data security threats affecting models, including data poisoning, prompt injection, hallucinations, and toxic output generation.
Reviewed core defense paradigms, including data cleaning, adversarial training, reinforcement learning from human feedback (RLHF), guardrails, and retrieval-augmented generation defenses.
Synthesized benchmark datasets across domains and analyzed prospective research frontiers in verifiable machine forgetting, data provenance, and standardized governance frameworks.
Identified training reliance on massive, uncurated web data as the fundamental vector enabling prompt injection, poisoning attacks, and model behavioral corruption.
Determined that effective security necessitates multi-stage defenses spanning pre-training data curation, alignment via RLHF, and post-processing output guardrails.
Highlighted critical open needs for standardized security evaluation benchmarks, explainability-driven vulnerability analysis, and mechanisms for verifiable machine unlearning.

Abstract

Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models inherently rely on massive amounts of training data, often collected from diverse and uncurated sources, which exposes them to serious data security risks. Harmful or malicious data can compromise model behavior, leading to toxic outputs or hallucinations, while also creating vulnerabilities to data-driven attacks such as prompt injection and data poisoning. As LLMs continue to be integrated into critical real-world systems, understanding and addressing these data-centric security risks is imperative to safeguard user trust and system reliability. This survey offers a comprehensive overview of the main data security risks facing LLMs and reviews current defense strategies, including adversarial training, data cleaning, output guardrails, Reinforcement Learning from Human Feedback (RLHF), data augmentation, and Retrieval-Augmented Generation (RAG)/agent defenses. Additionally, we categorize and analyze relevant datasets used for assessing robustness and security across different domains, providing guidance for future research. Finally, we highlight key research directions that focus on data provenance and traceability, verifiable machine forgetting, secure model updates, standardized evaluation framework, explainability-driven security analysis, and effective governance frameworks, aiming to promote the safe and responsible development of LLM technology. This work seeks to inform researchers, practitioners, and policymakers, driving progress toward data security in LLMs.

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