# CoSec: Benchmarking Agent Security in Communities

arXiv: https://arxiv.org/abs/2609.34790 · HTML: https://mlsanity.com/paper/2609.34790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 22 of 22)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://mlsanity.com/paper/2403.02691/) (2024)
- [AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents](https://mlsanity.com/paper/2410.09024/) (2024)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents](https://mlsanity.com/paper/2410.02644/) (2024)
- [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023)
- [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025)
- [PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action](https://mlsanity.com/paper/2409.00138/) (2024)
- [Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents](https://mlsanity.com/paper/2509.17488/) (2025)
- [MAGPIE: A benchmark for Multi-AGent contextual PrIvacy Evaluation](https://mlsanity.com/paper/2510.15186/) (2025)
- [ConVerse: Benchmarking Contextual Safety in Agent-to-Agent Conversations](https://mlsanity.com/paper/2511.05359/) (2025)
- [OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs](https://mlsanity.com/paper/2505.19165/) (2025)
- [AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks](https://mlsanity.com/paper/2604.01487/) (2026)
- [PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints](https://mlsanity.com/paper/2604.11523/) (2026)
- [ToolPrivacyBench: Benchmarking Purpose-Bound Privacy in Tool-Using LLM Agents](https://mlsanity.com/paper/2606.28061/) (2026)
- [LITMUS: Benchmarking Behavioral Jailbreaks of LLM Agents in Real OS Environments](https://mlsanity.com/paper/2605.10779/) (2026)
- [POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents](https://mlsanity.com/paper/2605.19127/) (2026)
- [MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair](https://mlsanity.com/paper/2607.27080/) (2026)
- [REDAgentBench: Executable Red Teaming and Faithful Measurement of LLM Agent Systems](https://mlsanity.com/paper/2608.10669/) (2026)
- [MuPPET: A Benchmark for Contextual Privacy of LLM Assistants in Multi-Party Conversations](https://mlsanity.com/paper/2606.23217/) (2026)
- [Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems](https://mlsanity.com/paper/2605.27766/) (2026)

## Abstract (arXiv)

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.