Designing a Proxy Firewall for Visual Prompt Injection Detection
GoodCorgi4555 · reddit · 2026-08-05
A developer shares their final year project framework for Generalized Visual Prompt Injection Detection against commercial multimodal LLMs (like GPT-4o, Claude 3.5) and discusses engineering challenges.
- Architecture: Since commercial models are black boxes, the author designed an autonomous proxy firewall at the application boundary. After the frontend intercepts prompts and images, the backend uses local OCR to extract hidden malicious text. It then runs an intent alignment evaluation to check for conflicts with the user's explicit prompt before a risk scoring engine decides to allow, warn, or block.
- Engineering Challenges:
- How to optimize inference latency when chaining local OCR/layout parsers before hitting the third-party LLM API?
- Beyond raw text vectors, what crucial parameters should be tracked in a developer dashboard?
- Are there lightweight open-source multimodal models (e.g., Moondream, LLaVA) suitable for local deployment to act as a comparative control mirror?
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