Model-Based Input Validation: Preventing Prompt Injection Attacks
summary
Claims a practical defense: a meta prompt runs before the LLM to classify inputs, returning a pass/fail JSON plus a secret token that detects tampering. Prototypes include a travel‑planner demo where malicious inputs are blocked and the original internal prompt is prevented from leaking. Tests run across curated malicious and valid datasets against multiple models (GPT‑4‑mini and Claude variants) and hit near‑100% similarity on selected runs. Results require iterative dataset tuning and model choice to avoid false failures for ambiguous inputs.
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chapters (7)
- 0:00Introduction to Arato AI and Today's Topic
- 0:37Understanding Prompt Injection Attacks
- 2:01Demo: Prompt Injection in Action
- 3:08Preventing Prompt Injection Attacks
- 3:53Deep Dive: Model-Based Input Validation
- 9:44Testing and Experimentation
- 15:55Conclusion and Final Thoughts
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