Rio, José Américo Alves SusteloJarząbkowski, Mikołaj2025-11-122025-10-29http://hdl.handle.net/10362/190605Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis thesis explores the privacy, security, and ethical risks of large language models (LLMs) and proposes practical defenses to reduce these risks when using LLMs in real-world settings. After reviewing existing research, three representative vulnerabilities - cognitive prompt injection via persona roleplay, data extraction through repeated token sequences, and fine‑tuning backdoors activated by rare triggers - were examined through experiment on a state‑of‑the‑art LLM. For each scenario, specific defenses were designed and tested: an extra output-scanning step to catch policy violations, an inference‑time filter to block excessive token repetitions, and a combined rare‑token filter with a prompt‑classification step. The results showed that these defenses significantly lowered the success rate of attacks, although none offered complete protection, although no single approach achieves perfect scores. Expert consultations with security and quality‑assurance professionals supported these findings and pointed out on-going challenges in stress‑testing, red‑teaming, and maintaining usability while ensuring strong safeguards. Based on both experimental results and expert feedback, the thesis provides practical recommendations for safely integrating LLMs. These include prompt engineering guidelines, layered output checks, input sanitization, access controls, privacy-focused training methods, ongoing red-teaming, and governance measures that comply with GDPR, CCPA, and other upcoming AI regulations.engLarge Language ModelsLLM SecurityPrompt InjectionData ExtractionBackdoor AttacksDefense MechanismsOutput ScanningInput ValidationDifferential PrivacyEthical AIAI GovernanceDesign Science ResearchEmpirical EvaluationRed-TeamingRegulatory ComplianceSDG 9 - Industry, innovation and infrastructureSDG 16 - Peace, justice and strong institutionsSDG 17 - Partnerships for the goalsmaster thesis204075360