HOW TO PROTECT YOUR AI AGENT FROM PROMPT INJECTION ATTACKS

Summary from the TLDR Web Dev newsletter

Prompt injection attacks are a new class of security vulnerabilities that exploit LLMs by manipulating their natural language processing abilities, similar to SQL injection attacks. There are six design patterns proposed by researchers to protect AI agents from these attacks, each with different "guardian" components that act as security checkpoints at various stages of the AI pipeline. These patterns include Action-Selector (using predefined safe actions), Plan-Then-Execute (creating fixed plans before processing untrusted data), LLM Map-Reduce (isolating data processing), Dual LLM (separating privileged and quarantined models), Code-Then-Execute (sandboxed execution), and Context-Minimization (removing potentially malicious inputs from memory).