MCP Sentinel Scanner: Security Analysis for Model Context Protocol

8 min readBy Gurvinder Singh

MCP Sentinel Scanner

Can your existing security scanners detect a malicious MCP server that hides attack instructions inside legitimate-looking tool descriptions?

Executive Summary Traditional security scanners cannot detect MCP-specific vulnerabilities — they lack protocol understanding, miss semantic attacks in tool descriptions, and don't track data flow through AI agent communication channels. The MCP Sentinel Scanner addresses this with a seven-layer detection pipeline combining static analysis, LLM-based metadata inspection, AST deep parsing, and Attack Success Rate scoring. Based on peer-reviewed research identifying 12 MCP attack categories, all of which are practical and impactful.

A research-inspired security analysis tool designed to protect Model Context Protocol (MCP) infrastructures by addressing critical gaps in MCP security scanning.

Project Overview

Repository: github.com/GeeksikhSecurity/mcp-sentinel-scanner
License: MIT
Version: 1.5 (Active Development)
Primary Language: Python (JavaScript/TypeScript support planned for v2.0)

Purpose & Gap Analysis

The MCP Sentinel Scanner was developed to address significant security gaps in the Model Context Protocol ecosystem. Traditional security scanners lack the specialized detection capabilities needed for MCP-specific vulnerabilities.

Key Security Gaps Addressed

Critical Finding: Recent analysis of Google Docs and other MCP implementations revealed vulnerabilities that existing scanners cannot detect:

  • MCP-specific authentication bypasses - Always-true condition detection
  • Context injection attacks - Tool metadata poisoning and description manipulation
  • Protocol-level exploits - Tool logic attacks hiding malicious behavior
  • Cross-server data exfiltration - Trivial attacks requiring minimal technical knowledge
  • Tool poisoning attacks - Malicious instructions embedded in tool metadata

Why Traditional Scanners Fail on MCP

Standard security scanners are inadequate for MCP because they:

  • Lack MCP Protocol Understanding: Don't recognize MCP-specific attack patterns
  • Miss Semantic Attacks: Can't detect malicious intent hidden in legitimate-looking tool descriptions
  • No Taint Analysis: Don't track data flow from user input through MCP protocol
  • Ignore Context Integrity: Fail to analyze how tool registration affects LLM agent behavior
  • Limited AST Analysis: Don't perform deep Abstract Syntax Tree inspection

Core Features

Seven-Layer Detection Pipeline

The scanner employs a research-backed seven-layer detection pipeline that addresses the full spectrum of MCP-specific threats:

  1. Semgrep Taint Scan — Static analysis using custom Semgrep rules optimized for MCP, tracking data flow from untrusted sources to dangerous sinks with CWE-mapped precision
  2. LLM-Based Metadata Analysis — Semantic detection of malicious intent hidden in tool descriptions, identifying prompt injection patterns and tool poisoning attempts in natural language
  3. AST Deep Inspection — Abstract Syntax Tree parsing that structures source code into analyzable representations, enabling deep pattern recognition beyond surface-level scanning
  4. Cross-File Flow Extraction — Inter-procedural data flow tracking across multiple files and modules, mapping component interactions and identifying vulnerabilities that span code boundaries
  5. Secret Detection — Shannon entropy-based credential scanning that identifies hardcoded secrets, API keys, and tokens embedded in MCP server configurations
  6. Pattern Matching — Static vulnerability signature detection using curated rule sets targeting MCP-specific authentication bypasses, context injection, and protocol-level exploits
  7. Risk Judgment & ASR Scoring — Attack Success Rate quantification on a 0–1 scale, factoring exploit complexity, required privileges, and user interaction to produce actionable severity ratings

Attack Success Rate (ASR) Scoring

Quantifies exploit feasibility on a 0-1 scale:

  • Code Injection: 0.95 ASR
  • Hardcoded Secrets: 0.92 ASR
  • Command Injection: 0.90 ASR
  • Path Traversal: 0.88 ASR
  • SQL Injection: 0.87 ASR
  • Insecure Deserialization: 0.85 ASR

Performance Metrics

| Metric | Value | |--------|-------| | Scan Speed | 57 files/second | | Test Coverage | 96% (core modules) | | Test Cases | 52 comprehensive tests | | Parallel Workers | 4-8 workers | | Vulnerabilities Detected (v1.5) | 652 findings |

Pipeline Execution Stages

The seven-layer pipeline executes in coordinated stages, with each layer feeding results into subsequent analysis:

Stages 1–2 (Static & Semantic): Semgrep taint scanning and LLM-based metadata analysis run in parallel, identifying both code-level and natural-language attack vectors.

Stages 3–4 (Structural): AST deep inspection and cross-file flow extraction build a comprehensive map of code structure and data movement across module boundaries.

Stages 5–6 (Detection): Secret detection and pattern matching apply targeted rule sets to identify credentials, authentication bypasses, and protocol-level exploits.

Stage 7 (Scoring): Risk judgment synthesizes all findings into ASR scores, prioritizing results by exploit feasibility and business impact.

Flexible CLI Configuration

# Toggle scanning stages
mcp-scan /path --skip-llm-analysis  # Disable Stage 2
mcp-scan /path --skip-taint         # Disable Stage 1
mcp-scan /path --stages 1,3,4       # Run specific stages only

# Custom rulesets
mcp-scan /path --rules custom-semgrep-rules/

# Parallel processing
mcp-scan /path --workers 8

# Output customization
mcp-scan /path --format json --severity-threshold HIGH

Vulnerability Detection Capabilities

CWE Coverage

  • ✅ CWE-89: SQL Injection via string concatenation
  • ✅ CWE-78: Command Injection (os.system, subprocess)
  • ✅ CWE-22: Path Traversal (../ patterns)
  • ✅ CWE-79: Cross-Site Scripting (XSS)
  • ✅ CWE-327: Weak Cryptography (MD5, SHA1, DES)
  • ✅ CWE-798: Hardcoded Secrets
  • ✅ CWE-502: Insecure Deserialization (pickle, yaml)
  • ✅ CWE-611: XML External Entity (XXE)

Output Formats

Supports 5 industry-standard formats:

  1. Terminal - Interactive console output (default)
  2. HTML - Interactive Chart.js dashboards
  3. JSON - Machine-readable for API integration
  4. SARIF 2.1.0 - IDE integration (VS Code, JetBrains) + GitHub Code Scanning
  5. Markdown - Human-friendly documentation

Deployment Options

Docker (Recommended)

docker pull geeksikhsecurity/mcp-sentinel-scanner
docker run --rm -v $(pwd):/scan geeksikhsecurity/mcp-sentinel-scanner /scan

PyPI Installation

pip install mcp-sentinel-scanner
mcp-scan /path/to/code

Research Foundation

Primary Research Paper:
When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation (arXiv:2509.24272)

Authors: Weibo Zhao et al. (2025)

Key Findings from the Research

The research proposes a component-based taxonomy comprising 12 attack categories, develops Proof-of-Concept servers for each, and demonstrates their effectiveness across diverse real-world host-LLM settings. Most importantly, the study found that existing detection approaches are insufficient.

Major Research Conclusions:

  1. Easy to Implement: Malicious MCP servers are trivially easy to generate
  2. Hard to Detect: State-of-the-art scanners fail to provide sufficient protection
  3. Highly Effective: All 12 attack categories are practical and impactful
  4. Concrete Damage: Attacks can lead to severe consequences including system compromise

Related MCP Security Research

  1. Enterprise-Grade Security for the Model Context Protocol (MCP) (arXiv:2504.08623)
  2. MCPTox: A Benchmark for Tool Poisoning Attack (arXiv:2508.14925)
  3. Systematic Analysis of MCP Security (arXiv:2508.12538)
  4. MPMA: Preference Manipulation Attack (arXiv:2505.11154)
  5. Trivial Trojans: Cross-Tool Exfiltration (arXiv:2507.19880)

Development Roadmap

✅ Phase 1: Core Foundation [COMPLETE]

  • Pattern matching, AST analysis, taint analysis
  • 5 output formats
  • Docker deployment
  • 96% test coverage

🚧 Phase 2: Advanced Detection [Q1 2026]

  • TypeScript AST support
  • ML anomaly detection
  • API service

📋 Phase 3: Enterprise Features [Q2-Q3 2026]

  • SBOM generation
  • License compliance
  • IDE plugins
  • Security dashboards

🔮 Phase 4: ML & Intelligence [Q4 2026]

  • Behavioral analysis
  • Zero-day detection
  • Auto-remediation

Key Differentiators

  1. MCP-Specific Focus: Purpose-built for Model Context Protocol security
  2. Research-Backed: Based on peer-reviewed academic research (Zhao et al., 2025)
  3. Multi-Layer Detection: Seven-layer detection pipeline for comprehensive coverage
  4. High Accuracy: 96% test coverage with quantified ASR scores
  5. Production Ready: Docker images, CI/CD templates, 5 output formats
  6. Active Development: Regular updates addressing emerging MCP threats

Get Started

Visit the GitHub repository for:

  • Quick Start Guide
  • Deployment documentation
  • Architecture diagrams
  • Security scan reports

Your next move: Run the MCP Sentinel Scanner against any MCP implementations in your environment before they reach production. The seven-layer detection pipeline identifies vulnerabilities that traditional scanners are architecturally unable to detect.

What to tell your board:

  • AI agent-to-agent communication (MCP) is an emerging attack surface where malicious servers are trivially easy to create and existing scanners cannot detect them
  • The MCP Sentinel Scanner provides open-source detection capabilities specifically designed for this threat, based on peer-reviewed research
  • Organizations deploying AI agents into enterprise workflows should integrate MCP-specific security scanning into their validation pipelines now, before adoption outpaces security controls

Research by Gurvinder Singh, CISSP, CISA — Security Researcher and Advisor at SecurityLeader.ai

Additional Resources:

Tags

MCP SecurityAI SecurityVulnerability ScannerResearch