As large language models (LLMs) increasingly depend on web-scraped datasets, concerns arise over their potential to generate verbatim training content with copyrighted or private information. However, current protections against web crawling or sample-specific memorization are inherently limited, as they require compliance from crawlers (e.g., respecting robots.txt) or model trainers (e.g., applying differential privacy). To empower data owners with direct control, we propose ExpShiled, a proactive self-defense mechanism that mitigates sample-specific memorization via imperceptible text perturbations. This approach requires no external collaboration while maintaining original readability. To evaluate individual-level defense efficacy, we first propose the metric of instance exploitation: a zero value indicates perfect defense, achieved when a protected text's log-perplexity ranking aligns with its counterfactual untrained ranking. We then reveal and validate the memorization trigger hypothesis, demonstrating that a model's memorization of a specific text sample stems primarily from its outlier tokens. Leveraging this insight, we design targeted perturbations that (1) prioritize inherent trigger tokens and (2) introduce artificial trigger tokens as pitfalls to disrupt memorization on the protected sample. Experiments validate our defense across model scales, languages, vision-to-language tasks, and fine-tuning methods. Even with privacy backdoors, the Membership Inference Attack (MIA) AUC drops from 0.95 to 0.55, and instance exploitation approaches zero. This suggests that compared to the ideal no-misuse scenario, the risk of exposing a text instance remains nearly unchanged despite its inclusion in training data.