Sadeque, Farig YousufBhuiyan, Md Rezaur RahmanMahmood, Syed NaveedKhondaker, Jareen TasneemSakib, Md SameerZaman, Tasfia2026-04-122026-04-1220262026-01ID 22301294ID 22301257ID 22301308ID 24241344ID 22301779http://hdl.handle.net/10361/27862Cataloged from PDF version of thesis.Includes bibliographical references (pages 63-72).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2026.The rapid development of large language models (LLMs) has outpaced regulations (e.g. GDPR) and ethical frameworks, raising concerns about privacy compliance, bias, misuse, misinformation, and legal adaptability. This makes the ability to selec- tively erase knowledge from LLMs critical. Despite significant developments, current unlearning methods are not able to segregate behavioral suppression and true knowl- edge removal, allowing latent capabilities to persist beneath surface-level refusals. In this paper, we address this challenge by introducing Knowledge Immunization Framework (KIF), a representation-aware architecture that differentiates between true erasure and obfuscation by operating on the internal activation signatures of the model, as opposed to surface-level outputs. KIF achieves near-oracle erasure (FQ ≈ 0.99 vs. 1.00) and utility preservation (MU = 0.62), effectively breaking the stability-erasure tradeoff that has constrained all prior work. Our observation shows that standard models exhibit scale-independent true erasure (<3% utility drift), while reasoning-prior models reveal fundamental architectural divergence. Our comprehensive dual-metric evaluation protocol, combining surface-level leakage with latent trace persistence, operationalizes the obfuscation - erasure distinction and enables the first systematic diagnosis of mechanism-level forgetting behavior across model families and scales.85 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Large language modelKnowledge entanglementComputational overheadKnowledge immunization frameworkKIFMachine learning.Knowledge management.Copyright.Thesis