Attack and defense techniques in large language models: A survey and new perspectives

• URL: http://arxiv.org/abs/2505.00976v1
• Date: Fri, 02 May 2025 03:37:52 GMT
• Title: Attack and defense techniques in large language models: A survey and new perspectives
• Authors: Zhiyu Liao, Kang Chen, Yuanguo Lin, Kangkang Li, Yunxuan Liu, Hefeng Chen, Xingwang Huang, Yuanhui Yu,
• Abstract summary: Large Language Models (LLMs) have become central to numerous natural language processing tasks, but their vulnerabilities present security and ethical challenges.<n>This systematic survey explores the evolving landscape of attack and defense techniques in LLMs.
• Score: 5.600972861188751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Survey on Model Extraction Attacks and Defenses for Large Language Models [55.60375624503877]
  Model extraction attacks pose significant security threats to deployed language models.<n>This survey provides a comprehensive taxonomy of extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks.<n>We examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios.
  arXiv  Detail & Related papers  (2025-06-26T22:02:01Z)
• LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures [49.1574468325115]
  This survey seeks to define and categorize the various attacks targeting large language models (LLMs)<n>A thorough analysis of these attacks is presented, alongside an exploration of defense mechanisms designed to mitigate such threats.
  arXiv  Detail & Related papers  (2025-05-02T10:35:26Z)
• Tit-for-Tat: Safeguarding Large Vision-Language Models Against Jailbreak Attacks via Adversarial Defense [90.71884758066042]
  Large vision-language models (LVLMs) introduce a unique vulnerability: susceptibility to malicious attacks via visual inputs.<n>We propose ESIII (Embedding Security Instructions Into Images), a novel methodology for transforming the visual space from a source of vulnerability into an active defense mechanism.
  arXiv  Detail & Related papers  (2025-03-14T17:39:45Z)
• A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments [55.60375624503877]
  Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data.<n>This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements.<n>We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services.
  arXiv  Detail & Related papers  (2025-02-22T03:46:50Z)
• Model Privacy: A Unified Framework to Understand Model Stealing Attacks and Defenses [11.939472526374246]
  This work presents a framework called Model Privacy'', providing a foundation for comprehensively analyzing model stealing attacks and defenses.<n>We propose methods to quantify the goodness of attack and defense strategies, and analyze the fundamental tradeoffs between utility and privacy in ML models.
  arXiv  Detail & Related papers  (2025-02-21T16:29:11Z)
• Safety at Scale: A Comprehensive Survey of Large Model Safety [298.05093528230753]
  We present a comprehensive taxonomy of safety threats to large models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats.<n>We identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices.
  arXiv  Detail & Related papers  (2025-02-02T05:14:22Z)
• Jailbreaking and Mitigation of Vulnerabilities in Large Language Models [4.564507064383306]
  Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation. Despite these advancements, LLMs have shown considerable vulnerabilities, particularly to prompt injection and jailbreaking attacks. This review analyzes the state of research on these vulnerabilities and presents available defense strategies.
  arXiv  Detail & Related papers  (2024-10-20T00:00:56Z)
• Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations [0.0]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks.<n>This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming.
  arXiv  Detail & Related papers  (2024-10-09T01:35:38Z)
• Recent Advances in Attack and Defense Approaches of Large Language Models [27.271665614205034]
  Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities.<n>Their widespread deployment has raised significant safety and reliability concerns.<n>This paper reviews current research on LLM vulnerabilities and threats, and evaluates the effectiveness of contemporary defense mechanisms.
  arXiv  Detail & Related papers  (2024-09-05T06:31:37Z)
• The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness [56.174255970895466]
  Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications. This paper presents Safety and Over-Defensiveness Evaluation (SODE) benchmark.
  arXiv  Detail & Related papers  (2023-12-30T17:37:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.