Large language models (LLMs) learn the grammar, semantics, and inherent patterns of human language through training on massive text corpora, thereby acquiring the core capabilities to comprehend and generate natural language text. Due to the incorporation of false, erroneous, and even maliciously manipulated data samples into their training data, LLMs suffer from data contamination, which is specifically manifested in two forms: model controllability contamination and content credibility contamination. The root causes of this issue can be attributed to four interrelated dimensions: data source risks arising from the open data ecosystem and insufficient data cleaning capabilities; inherent model defects stemming from inadequate statistical generation and secure generalization capabilities; the diffusion and secondary propagation of contamination under trust endorsement in application and interaction scenarios; and the dual dilemma of lagging defense measures and disjointed regulatory frameworks. To address the aforementioned challenges, it is imperative to implement targeted countermeasures across four key levels: at the data level, fortifying the source line of defense and establish a clean, controllable training data system; at the model level, optimizing generation logic and alignment capabilities to enhance model robustness; at the application and interaction level, dispelling users' blind trust to block the propagation of contamination and scenario-specific risks; at the defense and regulation level, adopting a dual-wheel drive model of law-based empowerment and technological collaboration to build a prevention and control system covering the full life cycle of data.