# MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security

arXiv: https://arxiv.org/abs/2604.07551 · HTML: https://mlsanity.com/paper/2604.07551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 44 of its 68 references that are in the map.
- First posted on arXiv 8 Apr 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, 30 of 44)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks](https://mlsanity.com/paper/2302.05733/) (2023)
- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents](https://mlsanity.com/paper/2402.11208/) (2024)
- [MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers](https://mlsanity.com/paper/2508.14925/) (2025)
- [Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers](https://mlsanity.com/paper/2506.13538/) (2025)
- [MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits](https://mlsanity.com/paper/2504.03767/) (2025)
- [MCPSecBench: A Systematic Security Benchmark and Playground for Testing Model Context Protocols](https://mlsanity.com/paper/2508.13220/) (2025)
- [MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents](https://mlsanity.com/paper/2510.15994/) (2025)
- [MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use](https://mlsanity.com/paper/2509.24002/) (2025)
- [MPMA: Preference Manipulation Attack Against Model Context Protocol](https://mlsanity.com/paper/2505.11154/) (2025)
- [MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol](https://mlsanity.com/paper/2505.14590/) (2025)
- [MCP-SafetyBench: A Benchmark for Safety Evaluation of Large Language Models with Real-World MCP Servers](https://mlsanity.com/paper/2512.15163/) (2025)
- [MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools](https://mlsanity.com/paper/2509.09734/) (2025)
- [Prompt Injection Attacks on Agentic Coding Assistants: A Systematic Analysis of Vulnerabilities in Skills, Tools, and Protocol Ecosystems](https://mlsanity.com/paper/2601.17548/) (2026)
- [We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems](https://mlsanity.com/paper/2506.13666/) (2025)
- [DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on LLM-based Agent](https://mlsanity.com/paper/2502.12575/) (2025)
- [Securing the Model Context Protocol (MCP): Risks, Controls, and Governance](https://mlsanity.com/paper/2511.20920/) (2025)
- [Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary](https://mlsanity.com/paper/2506.00886/) (2025)
- [MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security](https://mlsanity.com/paper/2508.12538/) (2025)
- [MCP-Guard: A Multi-Stage Defense-in-Depth Framework for Securing Model Context Protocol in Agentic AI](https://mlsanity.com/paper/2508.10991/) (2025)
- [When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation](https://mlsanity.com/paper/2509.24272/) (2025)
- [Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem](https://mlsanity.com/paper/2509.06572/) (2025)
- [Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem](https://mlsanity.com/paper/2512.08290/) (2025)
- [Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP](https://mlsanity.com/paper/2602.11327/) (2026)
- [A Survey on Agent Workflow -- Status and Future](https://mlsanity.com/paper/2508.01186/) (2025)

## Built on by (most cited first, 2 of 2)

- [TrustShiftProbe: Characterizing, Benchmarking, and Defending Staged Trust Attacks on MCP Servers](https://mlsanity.com/paper/2608.23763/) (2026)
- [COPEX: Benchmarking LLM Robustness to Adversarial Context Across Model Context Protocol Layers](https://mlsanity.com/paper/2610.04378/) (2026)

## Abstract (arXiv)

The Model Context Protocol (MCP) enables large language models (LLMs) to dynamically discover and invoke third-party tools, significantly expanding agent capabilities while introducing a distinct security landscape. Unlike prompt-only interactions, MCP exposes pre-execution artifacts, shared context, multi-turn workflows, and third-party supply chains to adversarial influence across independently operated components. While recent work has identified MCP-specific attacks and evaluated defenses, existing studies are largely attack-centric or benchmark-driven, providing limited guidance on where mitigation responsibility should reside within the MCP architecture. This is problematic given MCP's multi-party design and distributed trust boundaries. We present a defense-placement-oriented security analysis of MCP, introducing a layer-aligned taxonomy that organizes attacks by the architectural component responsible for enforcement. Threats are mapped across six MCP layers, and primary and secondary defense points are identified to support principled defense-in-depth reasoning under adversaries controlling tools, servers, or ecosystem components. A structured mapping of existing academic and industry defenses onto this framework reveals uneven and predominantly tool-centric protection, with persistent gaps at the host orchestration, transport, and supply-chain layers. These findings suggest that many MCP security weaknesses stem from architectural misalignment rather than isolated implementation flaws.