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Exploring Vulnerabilities and Protections in Large Language Models: A Survey

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arxiv 2406.00240 v1 pith:6KWYJ2I4 submitted 2024-06-01 cs.LG cs.CLcs.CR

Frank Weizhen Liu , Chenhui Hu This is my paper · ORCID
classification cs.LGcs.CLcs.CR
keywords attackssecuritysurveyllmspromptvulnerabilitiesadversarialchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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As Large Language Models (LLMs) increasingly become key components in various AI applications, understanding their security vulnerabilities and the effectiveness of defense mechanisms is crucial. This survey examines the security challenges of LLMs, focusing on two main areas: Prompt Hacking and Adversarial Attacks, each with specific types of threats. Under Prompt Hacking, we explore Prompt Injection and Jailbreaking Attacks, discussing how they work, their potential impacts, and ways to mitigate them. Similarly, we analyze Adversarial Attacks, breaking them down into Data Poisoning Attacks and Backdoor Attacks. This structured examination helps us understand the relationships between these vulnerabilities and the defense strategies that can be implemented. The survey highlights these security challenges and discusses robust defensive frameworks to protect LLMs against these threats. By detailing these security issues, the survey contributes to the broader discussion on creating resilient AI systems that can resist sophisticated attacks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs

    cs.CR 2024-12 reject novelty 4.0 of 10

    Applying CVSS, DREAD, OWASP, and SSVC to 56 adversarial LLM attacks via three LLM judges yields near-constant factor scores, which the authors take as evidence that these metrics cannot differentiate LLM attacks.

  2. The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs

    cs.CL 2025-06 conditional novelty 3.0 of 10

    A structured survey of LLM safety evaluation that proposes a why/what/where/how taxonomy and catalogs metrics, datasets, benchmarks, evaluators, and frameworks.

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