Abstract

Personal AI agents that run on the users local machine, such as OpenClaw, automate daily tasks including web search, email, and file management. Their access to computer resources, including the network, file system, and shell, exposes them to indirect prompt injection (IPI) attacks. Prior Dual LLM defenses block IPI by replacing untrusted data with symbols that the agent can reference but not read. However, they track untrusted data only inside the agents context, so when the agent saves and later rereads untrusted data, that data, possibly an attackers prompt, can return as trusted data rather than as a symbol, which we call stored IPI.