Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation

Fine-tuning large language models LLMs with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteristics in data across different organizations, the idea of collaboratively fine-tuning an LLM using data from multiple...

Safeguard-By-Development: a Privacy-Enhanced Development Paradigm for Multi-Agent Collaboration Systems

Multi-agent collaboration systems MACS, powered by large language models LLMs, solve complex problems efficiently by leveraging each agent's specialization and communication between agents. However, the inherent exchange of information between agents and their interaction with external...

Jailbreaking ChatGPT and other large language models while we can

The introduction of ChatGPT launched an arms race between tech giants. The rush to be the first to incorporate a similar large language model LLM into their own offerings read: search engines may have left a lot of opportunities to bypass the active restrictions such as bias, privacy concerns, an...