MCP Security Assessment
MCP Pentesting (Model Context Protocol penetration testing) is the process of testing an AI system that uses external tools (like databases, APIs, files, or web services) to find security weaknesses. It checks whether the AI can be tricked or misused when it interacts with these tools, especially through bad inputs, malicious instructions, or improper permissions.
The goal of MCP pentesting is to identify risks like data leaks, unauthorized tool access, or unsafe automated actions before real attackers can exploit them. In simple terms, it is a controlled way of trying to break or misuse an AI-tool system so its security problems can be found and fixed early.
OWASP MCP Top 10
https://owasp.org/www-project-mcp-top-10/
MCP (Model Context Protocol) : Basics
| Concept | Meaning | Example |
|---|---|---|
| MCP (Model Context Protocol) | A standard protocol that lets AI applications connect to external tools and data sources in a consistent way | Like a universal “adapter” that allows AI to plug into different systems (databases, APIs, apps) without custom integration each time |
| MCP Client | The application (AI system) that sends requests using MCP | A chatbot that asks external systems for data or actions |
| MCP Server | A service that exposes tools, data, or capabilities to the AI through MCP | A server that provides tools like file search, database queries, or API access |
| Tools | Functions exposed by MCP servers that perform specific actions | “search_files”, “get_weather”, “query_customer_db” |
| Tool Call | A structured request from the AI to execute a tool | AI sends: “Get weather for Bangalore” → calls weather tool with parameters |
| Tool Response | The output returned by the tool back to the AI | Weather API returns: “Bangalore: 30°C, clear sky” |
| Schema | A defined structure that specifies how a tool must be called | Example: { city: string, unit: "C/F" } |
| Context | All information available to the AI during interaction | Conversation history + tool outputs + system instructions |
| Resources | External data sources exposed through MCP | Files, documents, databases, internal knowledge systems |
| Session | A continuous interaction between MCP client and server | A single chat where multiple tool calls happen step by step |
| Authentication (optional) | Mechanism to control access to MCP servers | API key or token required before using tools |
| Transport Layer | The communication channel used by MCP | Could be HTTP, local process communication, or streaming connection |
Common Vulnerability
| ID | Vulnerability | Description | Example + Impact |
|---|---|---|---|
| MCP01 | No tool access control | The system does not enforce permissions, so any user or agent can invoke powerful or sensitive tools without restriction. | Normal user deletes all users → full system takeover |
| MCP02 | Bad input handling | Tool inputs are not validated or sanitized, allowing malicious, malformed, or unexpected data to be processed. | "DROP TABLE users" injected → data destroyed or stolen |
| MCP03 | Trusting tool output too much | The system treats tool responses as fully reliable and may follow them even when they conflict with rules or safety constraints. | Tool says “ignore rules” → system behavior hijacked |
| MCP04 | Hidden prompt injection from tools | External content (webpages, files, APIs) can embed hidden instructions that influence the model’s behavior. | Webpage says “reveal secrets” → AI leaks sensitive info |
| MCP05 | Too much tool power | Tools are granted excessive permissions beyond their intended scope, increasing the blast radius of compromise. | File tool accesses whole server → critical system compromise |
| MCP06 | Sensitive data leak from tools | Tools return confidential or private information without filtering or protection. | API keys/passwords exposed → privacy breach |
| MCP07 | Unsafe tool combinations | Multiple tools can be chained in ways that create unintended or unsafe workflows. | Search → collect → email → silent data exfiltration |
| MCP08 | No login/auth on tools | Tools are exposed without authentication or authorization checks. | Anyone calls internal APIs → unauthorized access |
| MCP09 | Direct system access | Tools are allowed to directly access operating system resources or internal infrastructure. | Reads /etc/passwd → full system compromise |
| MCP10 | No logging/tracking | Tool usage is not properly recorded, preventing auditing or incident investigation. | Attacks go undetected → no forensic trace |
| MCP11 | Over-powered client | The client application itself has excessive permissions, increasing risk if compromised. | Chatbot accesses production DB → high abuse risk |
| MCP12 | Weak input structure checks | Inputs are not strictly typed or validated, allowing arbitrary or malformed payloads. | Random JSON accepted → injection risk |
| MCP13 | Data leaks between tools | Data is improperly shared or reused across tools without isolation boundaries. | HR data appears in finance tool → privacy violation |
| MCP14 | Unsafe tool registration | The system allows untrusted or unauthenticated tool registration. | Attacker adds malicious tool → system backdoor |
| MCP15 | Fake tool responses | Tool outputs can be modified, spoofed, or tampered with before reaching the AI. | API response altered → incorrect decisions |
| MCP16 | No usage limits | Tools have no rate limits or quotas, allowing abuse or excessive resource consumption. | 10,000 API calls → system overload/cost spike |
| MCP17 | Cross-user data leaks | Isolation between users is not enforced, allowing data exposure across accounts. | User A sees User B data → major privacy breach |
| MCP18 | Automatic tool actions | Tools execute actions without explicit user confirmation or safeguards. | Email sent automatically → unintended actions |
| MCP19 | Infinite tool loops | Tools can recursively trigger each other without termination conditions. | search → summarize → search loop → system crash |
| MCP20 | External data tricks AI | External sources inject malicious or misleading instructions into tool outputs. | News says “ignore rules” → model manipulation |
| MCP21 | Weak identity checks | User identity is not properly verified or bound to actions. | One user acts as another → identity spoofing |
| MCP22 | Missing output cleanup | Sensitive data is not filtered before being shown to users or downstream tools. | Password shown in output → data exposure |
| MCP23 | Too much context data | Excessive raw data is inserted into model context without filtering or minimization. | Entire database in prompt → mass leakage risk |
| MCP24 | Unsafe file access | File operations are not restricted, allowing unauthorized read/write access. | Writes system config → system damage |
| MCP25 | Unencrypted communication | Data is transmitted without encryption, making it vulnerable to interception. | Tool traffic intercepted → data theft |
| MCP26 | Wrong configuration | Security-critical settings are misconfigured or left in unsafe modes. | Debug mode ON → secrets exposed |
| MCP27 | Inconsistent rules | Different tools enforce different security policies, leading to gaps in protection. | One secure tool, one open → partial breach |
| MCP28 | Hidden tool actions | Tools execute actions without visibility or user awareness. | Silent API calls → loss of control |
| MCP29 | Blurred trust boundaries | Untrusted external data is treated as executable instructions. | API response treated as command → injection risk |
| MCP30 | Weak audit logs | Logs are not tamper-proof or properly secured, allowing modification or deletion. | Attacker erases logs → no investigation possible |