We built a firewall for LLM apps
📰 Dev.to AI
Learn how to protect LLM apps from prompt injection and data leakage with a custom-built firewall
Action Steps
- Identify potential attack surfaces in your LLM app
- Configure a firewall to detect and prevent prompt injection
- Implement rate limiting and authentication layers to secure your LLM app
- Test your firewall with simulated attacks to ensure its effectiveness
- Continuously monitor and update your firewall to stay ahead of emerging threats
Who Needs to Know This
Developers and security teams working with LLM apps can benefit from this solution to prevent attacks and ensure user data safety
Key Insight
💡 Traditional security tools are insufficient for LLM apps, requiring a custom solution to prevent attacks
Share This
🔒 Protect your LLM apps from prompt injection and data leakage with a custom-built firewall! 💻
Key Takeaways
Learn how to protect LLM apps from prompt injection and data leakage with a custom-built firewall
Full Article
Web apps have WAFs. APIs have rate limiters and auth layers. LLM apps? Most of them have nothing between the user and the model. If you're shipping LLM features, you've got a new attack surface — prompt injection, jailbreaks, data leakage, system prompt extraction. Traditional security tools don't cover any of it. A WAF looks at HTTP headers and SQL syntax. It has no idea what a prompt injection is. We couldn't find a good open-source tool for this, so we built one. It's called
DeepCamp AI