Summary of Exploring Vulnerabilities and Protections in Large Language Models: a Survey, by Frank Weizhen Liu et al.
Exploring Vulnerabilities and Protections in Large Language Models: A Survey
by Frank Weizhen Liu, Chenhui Hu
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey delves into the security vulnerabilities of Large Language Models (LLMs) in two primary areas: Prompt Hacking and Adversarial Attacks. Within Prompt Hacking, we explore Prompt Injection and Jailbreaking Attacks, discussing their mechanisms, potential impacts, and mitigation strategies. Similarly, we dissect Adversarial Attacks into Data Poisoning Attacks and Backdoor Attacks. This structured analysis enables us to understand the relationships between these vulnerabilities and defense strategies that can be implemented. The survey emphasizes these security challenges and discusses robust defensive frameworks for protecting LLMs against these threats, ultimately contributing to the development of resilient AI systems capable of resisting sophisticated attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) can be hacked or attacked by bad actors. It focuses on two main ways this happens: tricking the model with fake prompts and sneaky data attacks. The authors examine these threats, explaining how they work and why they’re dangerous. They also discuss ways to protect LLMs from these attacks, so that we can have more trustworthy AI systems in the future. |
Keywords
» Artificial intelligence » Prompt
