Devansh further provides practical advice for developers, such as designing atomic functions, limiting the length and type of user inputs, and considering the potential for indirect prompt injections in external resources. He acknowledges that while it may be impossible to build a completely secure LLM-powered application, the focus should be on mitigation and quick remediation. The article concludes with a list of resources for mitigating LLM attacks.
Key takeaways:
- Language models and language-powered applications are vulnerable to hacking and attacks, which can result in brand damage, financial losses, and data leaks.
- Developers can use various strategies to protect their applications, such as detecting and preventing system prompt leakage, blocking lengthy or non-reasonable characters in user inputs, and implementing access control for backend systems.
- Despite these defense strategies, it is nearly impossible to build a completely bulletproof language model-powered application. The focus should be on mitigation and quick remediation when issues arise.
- Several tools and resources are available to help developers detect harmful language, prevent data leakage, and protect against prompt injection attacks, such as Rebuff, NeMo Guardrails, LangKit, LLM Guard, and the LVE Repository.