🤖 AI Summary
This survey addresses critical security vulnerabilities in large language models (LLMs), including adversarial prompting, model extraction, and application-layer attacks—alongside their corresponding defenses. It identifies key limitations in existing defensive approaches: insufficient adaptability to evolving threats, suboptimal robustness–utility trade-offs, and poor resource efficiency. To address these gaps, the work proposes a novel, end-to-end LLM security taxonomy—the first of its kind—spanning the full attack-defense lifecycle. Three foundational research directions are introduced: (1) scalable adaptive defense mechanisms, (2) interpretable security frameworks, and (3) standardized, reproducible evaluation protocols. Methodologically, the study integrates systematic literature review, rigorous threat modeling, and cross-disciplinary insights from NLP, cybersecurity, and AI governance. The resulting synthesis delivers both theoretical rigor and actionable guidance, offering industry practitioners a practical defense roadmap and advancing secure, trustworthy, and responsible LLM deployment paradigms.
📝 Abstract
Large Language Models (LLMs) have become central to numerous natural language processing tasks, but their vulnerabilities present significant security and ethical challenges. This systematic survey explores the evolving landscape of attack and defense techniques in LLMs. We classify attacks into adversarial prompt attack, optimized attacks, model theft, as well as attacks on application of LLMs, detailing their mechanisms and implications. Consequently, we analyze defense strategies, including prevention-based and detection-based defense methods. Although advances have been made, challenges remain to adapt to the dynamic threat landscape, balance usability with robustness, and address resource constraints in defense implementation. We highlight open problems, including the need for adaptive scalable defenses, explainable security techniques, and standardized evaluation frameworks. This survey provides actionable insights and directions for developing secure and resilient LLMs, emphasizing the importance of interdisciplinary collaboration and ethical considerations to mitigate risks in real-world applications.