LLM Security in Practice: Prompt Injection, Output Handling, and Model Poisoning
English summary
This article is a hands-on field guide covering three critical failure surfaces for large language models: prompt injection, unsafe output handling, and model poisoning. It presents practical attack and defense perspectives tailored for practitioners dealing with LLM security risks.
Chinese summary
本文是一份实战指南，涵盖大型语言模型三个关键故障面：提示注入、不安全的输出处理和模型投毒。文章从攻击与防御两个视角提供实用见解，面向需要应对LLM安全风险的从业者。
Key points
Addresses prompt injection attacks that manipulate model behavior via crafted inputs.
探讨通过精心设计的输入操纵模型行为的提示注入攻击。
Examines output handling vulnerabilities where model responses can trigger downstream exploits.
分析输出处理漏洞，模型响应可能触发下游利用。
Discusses model poisoning, where training data or fine-tuning corrupts model integrity.
讨论模型投毒，即训练数据或微调破坏模型完整性。