This research introduces a novel defense mechanism against prompt injection attacks that safeguards LLMs from malicious instructions embedded in external content.

• Advances the existing Base64 defense with a mixture of encodings approach
• Creates unpredictability that makes attacks significantly harder to execute
• Demonstrates improved security while maintaining LLM functionality
• Addresses a critical vulnerability in LLM-powered applications

As LLMs continue to be deployed in business-critical applications, these security enhancements provide essential protection against attackers attempting to manipulate AI systems through injected prompts.

Defense against Prompt Injection Attacks via Mixture of Encodings