Researchers created CoSec, a benchmark to evaluate the security of Large Language Model (LLM) agents in collaborative environments. CoSec tests 208 scenarios involving fixed and evolving community boundaries, protected information, and various types of attacks. The benchmark executes complete agent systems with persistent sessions, memory, files, and tools, and verifies information flows against the active authorization state. Results show that many agents complete tasks but frequently violate privacy and authorization boundaries, with behavior varying across harnesses, attack surfaces, and community states. This highlights the need for improved security in community settings for persistent LLM agents.