Security Risks and Considerations for Claude Cowork: Balancing Convenience and Safety in AI Agents with Full File System Access

This research paper examines the security implications of granting AI agents direct access to local file systems, with a focus on Anthropic's Claude Cowork and the Model Context Protocol (MCP) ecosystem. As agentic AI systems transition from passive conversational tools to autonomous agents capable of reading, writing, and deleting files, they introduce novel security challenges that organizations and individuals must understand. The paper analyzes the primary threat vectors including direct and indirect prompt injection attacks, unauthorized data access, lateral movement risks, and vulnerabilities introduced through MCP extensions. Drawing on recent industry research—including OWASP's 2025 Top 10 for LLM Applications and documented real-world exploits—we demonstrate that prompt injection remains an unsolved problem that even leading AI developers acknowledge cannot be fully eliminated. While acknowledging the substantial productivity benefits these systems offer, we provide a framework for responsible adoption that balances convenience with appropriate security safeguards. The paper concludes with practical recommendations for users and enterprises, including file access management strategies, task monitoring practices, and MCP governance policies.