Strategic Proposal: Neutralizing AI Prompt Injection via Recursive Honey-potting
Submitted by: Adam Eehan
Reference ID: VRP 493408185
Topic: Active Cyber-Defense & AI Security
1. The Problem: Prompt Injection
Current Large Language Models (LLMs) like Gemini are vulnerable to "Prompt Injection" attacks, where malicious users trick the AI into bypassing safety filters or revealing internal system instructions.
2. The Solution: Recursive Honey-potting
Instead of just blocking an attack, my strategy involves creating a "Digital Trap".
The Mechanism: When the system detects a suspicious or high-risk prompt, it doesn't just say "Access Denied." Instead, it triggers a Recursive Honey-pot.
The Decoy: The system serves the attacker "Decoy Data" (fake but realistic-looking internal info).
The Trap: As the attacker tries to use or dig deeper into this decoy data, the system recursively creates more fake layers, keeping the attacker busy in a "Infinite Sandbox" while silently logging their IP and attack pattern.
3. Why Google Needs This?
Active Defense: It turns a passive block into an active intelligence-gathering tool.
Safety: Real user data remains untouched while the attacker is "played" by the system.
Data for Research: This provides Google with real-time data on new hacking methods.
4. Do not use current LLM Gemini use for the mission eg: Gemma
5. Conclusion
Iam seeking a opportunity iam don't know more codes but i have some ideas This strategy moves beyond traditional bug-fixing (VRP) and enters the realm of Proactive Security Architecture. I am eager to discuss the technical implementation of this logic with the Google Research or Cloud Security teams.