LLM Security 101
🔐 LLM Security 101 🔒
Say you're deploying a chatbot or AI assistant for your business...
How do you prevent unsavoury answers, off-topic responses, and data breaches?
I cover some critical risks:
🔒 Jailbreaking:
Problem: Attackers try to override safety features -> LLM produces responses posing risks to your business reputation.
Solution: Use instruction-tuned models and implement a custom guard or use Llama Guard.
🎯 Off-topic requests:
Problem: These waste your resources.
Solution: Implement a system prompt to keep responses focused on your business domain.
💉 Prompt injection:
Problem: This can lead to data theft or server overload.
Solution: Use sanitisation, whitelisted commands, and parameterised queries.
💻 Malicious code execution:
Problem: Attackers gain control of your server and access files.
Solution: Run code in secure, containerised environments. Use tools like CodeShield.
Tips:
1. Focus on prevention, not just detection
2. Use system prompts to guide responses
3. Implement custom guards for sensitive use cases
4. Always sanitize inputs and outputs
5. Run code in secure containers
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That’s it for this week, cheers, Ronan
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