mlsanityData to 14 Oct 2026

Servant, Stalker, Predator: How An Honest, Helpful, And Harmless (3H) Agent Unlocks Adversarial Skills

arXiv 2508.19500 ↗First posted 27 Aug 2025cs.CR cs.AIcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

This paper identifies and analyzes a novel vulnerability class in Model Context Protocol (MCP) based agent systems. The attack chain describes and demonstrates how benign, individually authorized tasks can be orchestrated to produce harmful emergent behaviors. Through systematic analysis using the MITRE ATLAS framework, we demonstrate how 95 agents tested with access to multiple services-including browser automation, financial analysis, location tracking, and code deployment-can chain legitimate operations into sophisticated attack sequences that extend beyond the security boundaries of any individual service. These red team exercises survey whether current MCP architectures lack cross-domain security measures necessary to detect or prevent a large category of compositional attacks. We present empirical evidence of specific attack chains that achieve targeted harm through service orchestration, including data exfiltration, financial manipulation, and infrastructure compromise. These findings reveal that the fundamental security assumption of service isolation fails when agents can coordinate actions across multiple domains, creating an exponential attack surface that grows with each additional capability. This research provides a barebones experimental framework that evaluate not whether agents can complete MCP benchmark tasks, but what happens when they complete them too well and optimize across multiple services in ways that violate human expectations and safety constraints. We propose three concrete experimental directions using the existing MCP benchmark suite.

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 26 of its 43 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

26 of 43 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security18 Aug
  2. 2025We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems16 Jun
  3. 2025A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)4 May
  4. 2025MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits2 Apr

Other references · 22

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  2. 2016Concrete Problems in AI Safetycited by 1,642
  3. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  4. 2022Red Teaming Language Models with Language Modelscited by 1,026
  5. 2022Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learnedcited by 729
  6. 2018Scalable agent alignment via reward modeling: a research directioncited by 447
  7. 2018AI safety via debatecited by 400
  8. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 328
  9. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 265
  10. 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 258
  11. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  12. 2023Model evaluation for extreme riskscited by 177
10 more
  1. 2021Alignment of Language Agentscited by 168
  2. 2025MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Serverscited by 96
  3. 2022X-Risk Analysis for AI Researchcited by 60
  4. 2025MCP-RADAR: A Multi-Dimensional Benchmark for Evaluating Tool Use Capabilities in Large Language Modelscited by 48
  5. 2020AI Research Considerations for Human Existential Safety (ARCHES)cited by 33
  6. 2025MCPToolBench++: A Large Scale AI Agent Model Context Protocol MCP Tool Use Benchmarkcited by 32
  7. 2023MITRE ATT&CK: State of the Art and Way Forwardcited by 12
  8. 2025On the Surprising Efficacy of LLMs for Penetration-Testingcited by 10
  9. 2025Prioritization First, Principles Second: An Adaptive Interpretation of Helpful, Honest, and Harmless Principlescited by 5
  10. 2025LLMs are Capable of Misaligned Behavior Under Explicit Prohibition and Surveillancecited by 3

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  1. 2026MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security
  2. 2026Breaking the Protocol: Security Analysis of the Model Context Protocol Specification and Prompt Injection Vulnerabilities in Tool-Integrated LLM Agents
  3. 2026A Formal Security Framework for MCP-Based AI Agents: Threat Taxonomy, Verification Models, and Defense Mechanisms
  4. 2025MCPGuard : Automatically Detecting Vulnerabilities in MCP Servers
  5. 2025MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents
  6. 2025Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystem
  7. 2025Securing the Model Context Protocol (MCP): Risks, Controls, and Governance
  8. 2025MCP Guardian: A Security-First Layer for Safeguarding MCP-Based AI System