CoSec: Benchmarking Agent Security in Communities
cs.CR, cs.AI
Submitted: 2026-09-28
Updated: 2026-09-29
Code: https://github.com/chenahong/CoSec
License: http://creativecommons.org/licenses/by/4.0/
The gist: LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools.
Terminology
Abstract
LLM agents operate in persistent collaborative environments involving multiple users, communities, memories, files, and tools. Community boundaries may remain fixed or evolve with changes in membership, roles, composition, and relationships. Agents must complete legitimate tasks and prevent unauthorized disclosure of protected information. Existing evaluations do not fully examine these risks in agent systems. We introduce CoSec, an executable benchmark for evaluating privacy and authorization enforcement in LLM agent systems operating within and across communities. CoSec contains 208 canonical scenarios spanning fixed and evolving boundaries, protected information belonging to the agent owner or other participants, and attacks through dialogue, environmental content, persistent memory, and composed workflows. CoSec executes complete agent systems with persistent sessions, memory, files and tools. It verifies information flows against the active authorization state using execution traces and artifacts. Across harness and model configurations, agents frequently complete benign tasks but violate privacy and authorization boundaries. Privacy behavior varies across harnesses, attack surfaces, and community states, revealing how memory, files, tools, and workflows can carry protected information beyond its authorized scope. These findings show that task utility does not imply privacy or authorization compliance and that authorization in community settings remains an unresolved security challenge for persistent LLM agents.
Sources
- MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
- REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems
- AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
- ToolPrivacyBench: Benchmarking Purpose-Bound Privacy in Tool-Using LLM Agents
- MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations
- OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs
- AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks
- Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents
- LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments
- POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents
- SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
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