How I Built an OCR-Based Defense Against Prompt Injection for Local LLM Search

When you plug a local LLM into a web search tool, every fetched page becomes an attack surface. I found this out the hard way — my Ollama setup was pulling web content that contained invisible Unicode injection, fake system prompts, and markdown image tags designed to exfiltrate data through URL parameters. I went looking for a solution and found that Google DeepMind's own research showed their best model-level defenses fail 53.6% of the time against adaptive attacks. The "Attacker Moves Second...