Explain prompt injection and how to defend against it
This question tests your understanding of LLM security vulnerabilities and how untrusted user input can manipulate model behavior. A strong answer defines prompt injection as hijacking the model's instructions, then outlines a layered defense including input sanitization, instruction-tuned models, and separating user input from system prompts. A common red flag is confusing it with traditional SQL injection or suggesting simple input filtering is a sufficient solution.
This question tests your understanding of a critical LLM security vulnerability: how untrusted user input can be crafted to hijack the model's intended function. A strong answer first defines prompt injection as malicious user input that overrides or subverts the system's original instructions. It then outlines a multi-layered defense strategy, including strict input validation, using instruction-tuned models that better differentiate instructions from data, employing techniques like dual-LLM setups or XML/JSON tagging to demarcate user input, and implementing output monitoring. A common red flag is treating prompt injection like classic SQL injection, suggesting that simple string filtering or escaping is a sufficient solution, which it is not.
Read the original → Wikipedia: Prompt engineering
- #llm
- #security
- #ai
- #prompt engineering
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