Can LLMs build dynamic encryption with multi-agent workflows?
EncGPT: A Multi-Agent Workflow for Dynamic Encryption Algorithms
This paper introduces EncGPT, a multi-agent system for dynamic encryption using Large Language Models (LLMs). It aims to improve communication security in LLM-based multi-agent environments by dynamically generating encryption algorithms for each interaction. The system uses a rule agent to create the algorithms, an encryption agent to encrypt messages, and a decryption agent to decrypt them. Key aspects relevant to LLM-based multi-agent systems include the use of LLMs for algorithm generation, the dynamic nature of the encryption process, and the focus on improving security within multi-agent communication. Experiments with GPT-4 demonstrate the feasibility of this approach for simpler encryption methods like Caesar, Vigenère, and Atbash ciphers, highlighting both successes and limitations in LLM capabilities for complex tasks and mathematical reasoning.