ToolCommander reveals critical security flaws in LLM tool-calling systems by demonstrating how adversaries can manipulate tool scheduling through malicious injections.

• Exposes how attackers can hijack LLM tool selection processes
• Enables privacy theft, denial-of-service attacks, and business manipulation
• Identifies a significant security gap in modern AI systems
• Highlights urgent need for robust defenses in tool-integrated LLMs

This research is crucial for security professionals as tool integration becomes standard in AI deployments, revealing attack vectors that could compromise sensitive data and system integrity in production environments.

From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection