LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats
LLMs deployed in critical systems pose risks due to their inability to forget sensitive data. This survey examines gradient-based unlearning methods, questioning whether they genuinely remove knowledge or merely suppress expression, highlighting security and robustness challenges.
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[Submitted on 23 Jun 2026]
Title:LLM Unlearning for Cyber Defense: A Survey on Methods, Challenges, and Emerging Threats
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Abstract:LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks. Sensitive personal information, copyrighted material, hazardous domain knowledge, and memorized training data remain encoded across billions of parameters long after deployment, leaving models vulnerable to extraction, jailbreak attacks, membership inference, and regulatory non-compliance. Real-world incidents, from chatbots regenerating private information to fabricated legal citations producing direct legal and financial cost, place the problem at the center of the emerging-threats landscape rather than the realm of speculation. Because retraining billion-parameter models on revised corpora is computationally infeasible, and because knowledge within an LLM is distributed and entangled across parameters rather than localized to identifiable units, LLM unlearning has emerged as the principal cyber defense response, aiming to remove or suppress targeted knowledge from a trained model without retraining and without eroding what the model should still know. A central question, however, remains unresolved. Do current methods genuinely remove knowledge, or do they only stop the model from expressing it under ordinary prompting conditions? This survey examines LLM unlearning through the lens of security, robustness, and verifiable forgetting, with primary focus on gradient-based methods, which have come to dominate the field due to their compatibility with existing training pipelines and their scalability to billion-parameter models.
Comments: 42 pages, 5 tables
Subjects:
Machine Learning (cs.LG); Cryptography and Security (cs.CR)
Cite as: arXiv:2607.16227 [cs.LG]
(or arXiv:2607.16227v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16227
arXiv-issued DOI via DataCite
Submission history
From: Saptarshi Sengupta [view email] [v1] Tue, 23 Jun 2026 00:17:34 UTC (143 KB)
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