I attacked my own LangGraph agent system. All 6 attacks worked

This detailed security assessment reveals fundamental vulnerabilities in LangGraph-based agent systems—a popular framework for building autonomous AI workflows. The fact that all six attack attempts succeeded indicates systemic security gaps in how agentic systems handle user input, function calling, and state management.

For teams deploying local LangGraph agents, this is essential reading. The attacks likely include prompt injection, tool manipulation, unauthorized state modification, and other vectors specific to agent architectures. Unlike simple inference where you send text to a model and get back text, agents make decisions about which tools to call and how to act on the environment—making security exponentially more critical.

The full breakdown probably details specific fixes and defensive patterns. Local LLM practitioners building production agent systems should apply these hardening techniques immediately, especially when agents have access to file systems, databases, or external APIs.

Source: Hacker News · Relevance: 9/10