CodeCloak A DRL-Based Method for Mitigating Code Leakage by LLM Code Assistants

CodeCloak A DRL-Based Method for Mitigating Code Leakage by LLM Code Assistants

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AI Summary

Summary of Codec Cloak Presentation

Speaker: Amit Finman
Duration: 40 minutes

Agenda Overview:

1. Introduction to AI code assistant models.
2. Risks associated with using AI code assistants.
3. Presentation of Codec Cloak as a solution.
4. Key takeaways and future steps.
5. Q&A session.

AI Code Assistant Models:

• Revolutionized software development.
• Examples: GitHub Copilot, Code Whisperer, etc.
• Improve coding speed and reduce errors.

Risks:

• Sensitive code exposure as AI models require substantial context.
• Potential for leakage of proprietary information, leading to security concerns and intellectual property violations.
• Notable cases cited involving companies like Samsung, Google, and Apple.

Codec Cloak Solution:

• Designed to mitigate code leakage risks.
• Aims to protect intellectual property and reduce security breaches.
• Modifies prompts before sending to code assistants (e.g., summarizes code, alters function names).
• Uses reinforcement learning to optimize methods for reducing leakage.

Key Components:

• States, Actions, Rewards: Defined within a reinforcement learning framework to ensure effective manipulation of source code.
• Codeblue Metric: Evaluates suggestion quality while minimizing similarity to original code.

Implementation & Results:

• Created a custom dataset reflecting real-time developer interactions with AI assistants.
• Achieved an average of 40% reduction in code leakage while maintaining 75% usefulness of suggestions.
• Minimal processing overhead, adaptable across different AI code assistants.

Future Steps:

1. Enhance adaptability across programming languages.
2. Optimize performance to reduce computational overhead.
3. Integrate with IDEs for seamless usage.
4. Open-source Codec Cloak for community-driven improvements.