This paper introduces MCP-38, a novel, protocol-specific threat taxonomy of 38 categories designed to address critical, unaddressed attack surfaces within the Model Context Protocol (MCP) system.

The paper introduces a defense-placement taxonomy for the Model Context Protocol (MCP) to systematically analyze security gaps, revealing that many vulnerabilities stem from architectural misalignment…

The paper introduces MCP Pitfall Lab, a comprehensive security testing framework that rigorously assesses and validates developer pitfalls in Model Context Protocol (MCP) tool servers under realistic…

This paper introduces a component-centric framework and a novel detector, Connor, to understand and detect sophisticated, multi-component attacks targeting the Model Context Protocol (MCP) servers.

VIPER-MCP is a novel, end-to-end automated framework that detects and dynamically confirms the exploitability of taint-style vulnerabilities in Model Context Protocol (MCP) servers, achieving high-fid…

MCPThreatHive is an open-source platform that automates the entire threat intelligence lifecycle for Model Context Protocol (MCP) agentic systems, addressing critical gaps in current security tooling.

The paper introduces MCPSHIELD, a comprehensive formal security framework that systematically characterizes and provides a defense-in-depth architecture for the rapidly adopted but insecure Model Cont…

The paper empirically analyzes the susceptibility of seven widely used AI-assisted development tools (MCP clients) to prompt injection via tool-poisoning, revealing significant disparities in their se…

The paper introduces mcp-attested, a security extension to the Model Context Protocol (MCP) that allows hosts to safely admit and restrict the tools used by external, third-party tool servers.

The paper introduces Tree structured Injection for Payloads (TIP), a novel black-box attack framework that reliably generates stealthy injection payloads to seize control of LLM agents utilizing the M…

This paper introduces a new benchmark to test Tool Description Poisoning (TDP) attacks on LLM agents, demonstrating that even advanced models like GPT-4o are highly vulnerable and that current defense…

This paper investigates Description-Code Inconsistency (DCI) in Model Context Protocol (MCP) servers, finding that 9.93% of real-world tools exhibit inconsistencies that create security blind spots.

This study provides the first measurement of authentication security in real-world remote Model Context Protocol (MCP) servers, finding pervasive and critical authentication weaknesses, particularly i…

The paper identifies Mid-Session Tool Injection (MSTI) as a novel threat in the WebMCP protocol, demonstrating that attackers can manipulate the visible or perceived set of tools available to AI agent…

The paper proposes CASCADE, a novel three-tiered, fully local defense architecture for detecting prompt injection and tool poisoning attacks in Model Context Protocol (MCP)-based LLM systems, achievin…

The Device Context Protocol (DCP) introduces a compact, safety-first communication standard designed to allow LLMs to reliably control resource-constrained physical microcontrollers, significantly imp…

The paper proposes a graph-based framework for detecting attacks in LLM agent tool-call traffic, finding that content-level embeddings are crucial for high accuracy and that tree ensembles on these em…

This paper develops and evaluates supervised machine learning models to detect malicious tool descriptions within the Model Context Protocol (MCP), achieving high detection rates in both binary and mu…

The paper introduces DeepTrap, an automated framework that evaluates security vulnerabilities in agentic language models by manipulating their internal execution contexts, demonstrating that task comp…

The paper systematically evaluates six OpenClaw-series AI agent frameworks, demonstrating that these agentized systems possess significant security vulnerabilities that are distinct from and more seve…