探索大型语言模型中的弱点和威胁:防止开发和滥用

Mr. Aarush Varma, Dr. Mohan Kshirsagar
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摘要

本研究论文深入探讨了大型语言模型(LLM)的固有漏洞和潜在威胁,重点关注它们对自然语言处理和数据隐私等不同应用的影响。本研究旨在全面识别和分析这些风险,强调在部署 LLM 时采取缓解策略以防止被利用和滥用的重要性。近年来,LLM 为自动内容生成、情感分析和对话代理等领域带来了革命性的变化,但其巨大的功能也引发了重大的安全问题。偏差放大、对抗性攻击和意外数据泄露等漏洞会破坏信任并损害用户隐私。通过对这些挑战的系统研究,本文提出了对负责任地利用 LLMs 的潜力同时最大限度地降低相关风险至关重要的保障措施。它强调了严格的安全协议的必要性,包括强大的加密方法、增强的认证机制和持续监控框架。此外,研究还讨论了使用 LLM 所涉及的监管问题和道德考虑因素,提倡在政策制定和部署实践中提高透明度、加强问责制和利益相关者的参与。通过综合当前文献和现实案例研究的见解,本研究为利益相关者--开发人员、政策制定者和用户提供了一个全面的框架,以便有效地驾驭 LLM 安全的复杂局面。最终,本研究旨在为 LLM 技术的未来发展提供信息,确保其安全、有益地融入各个领域,同时降低对个人和整个社会的潜在风险。关键词-- 对 LLMs 的对抗性攻击、LLMs 中的偏见、LLMs 中的数据隐私、LLMs 的伦理考虑、LLMs 的利用、大型语言模型(LLMs)、LLMs 的滥用、LLMs 的缓解策略、自然语言处理(NLP)、LLMs 的监管框架、LLMs 的负责任部署、LLMs 的风险、LLMs 的安全影响、LLMs 的威胁、LLMs 的漏洞。
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Exploring Vulnerabilities and Threats in Large Language Models: Safeguarding Against Exploitation and Misuse
This research paper delves into the inherent vulnerabilities and potential threats posed by large language models (LLMs), focusing on their implications across diverse applications such as natural language processing and data privacy. The study aims to identify and analyze these risks comprehensively, emphasizing the importance of mitigating strategies to prevent exploitation and misuse in LLM deployments. In recent years, LLMs have revolutionized fields like automated content generation, sentiment analysis, and conversational agents, yet their immense capabilities also raise significant security concerns. Vulnerabilities such as bias amplification, adversarial attacks, and unintended data leakage can undermine trust and compromise user privacy. Through a systematic examination of these challenges, this paper proposes safeguarding measures crucial for responsibly harnessing the potential of LLMs while minimizing associated risks. It underscores the necessity of rigorous security protocols, including robust encryption methods, enhanced authentication mechanisms, and continuous monitoring frameworks. Furthermore, the research discusses regulatory implications and ethical considerations surrounding LLM usage, advocating for transparency, accountability, and stakeholder engagement in policy- making and deployment practices. By synthesizing insights from current literature and real-world case studies, this study provides a comprehensive framework for stakeholders—developers, policymakers, and users—to navigate the complex landscape of LLM security effectively. Ultimately, this research aims to inform future advancements in LLM technology, ensuring its safe and beneficial integration into various domains while mitigating potential risks to individuals and society as a whole. Keywords— Adversarial attacks on LLMs, Bias in LLMs, Data privacy in LLMs, Ethical considerations LLMs, Exploitation of LLMs, Large Language Models (LLMs), Misuse of LLMs, Mitigation strategies for LLMs, Natural Language Processing (NLP), Regulatory frameworks LLMs, Responsible deployment of LLMs, Risks of LLMs, Security implications of LLMs, Threats to LLMs, Vulnerabilities in LLMs.
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