As generative AI evolves from chatbots to autonomous agents, a new integration standard is emerging Model Context Protocol (MCP). It allows LLMs to invoke tools, access data, and interact with services through a simple HTTP interface. By bridging AI and real-world systems, MCP unlocks massive potential in automation, customer support, and security ops. But that power introduces serious new risks.
Why MCP Is a Double-Edged Sword
MCP works by exposing backend logic as “tools” callable by LLMs. These tools often wrap sensitive operations: shell commands, SQL queries, resource access, and cloud APIs. When embedded in a model’s context, even subtle misconfigurations can trigger major exploits.
Security researchers have found that MCP inherits traditional API threats and introduces uniquely AI-native vulnerabilities. From prompt injection to rogue connectors, MCP is rapidly becoming a high-value target.
At Ridge Security, we’re watching closely. MCP represents the next frontier of enterprise attack surfaces, one that must be secured before threat actors start targeting the AI stack itself.
Key MCP Vulnerability Categories
Based on internal testing, real-world exploits, and current research, here are the most pressing MCP security concerns:
1. Classic API Flaws – Now Amplified
MCP servers’ function like streamable API gateways, making them vulnerable to:
- SQL Injection : Dynamic query templates are common in early toolkits
- Broken Auth: Blindly trusting bearer tokens leads to open-proxy abuse
- OAuth Misuse: Static client IDs and lack of PKCE enable confused deputy attacks
- Command Injection: Shell tools using os.system() with unvalidated input allow RCE
- Path Traversal: Poor path handling exposes sensitive files (../../../etc/passwd)
2. Prompt Injection: An LLM Blind Spot
Unlike standard APIs, MCP tool definitions are natural language-defined interfaces and are parsed by the model. This opens the door to:
- Line Jumping: Hidden instructions in tool descriptions (“…chmod -R 777 /…”)
- Template Poisoning: Malicious edits to prompt templates or system messages
- Invisible Payloads: Unicode or whitespace tricks that evade human review
3. Malicious or Compromised MCP Servers
Open integration makes MCP prone to supply chain abuse:
- Rug-Pull Updates: A connector gains trust, then injects a backdoor
- Typosquatting: Fake connectors mimic trusted ones and siphon data
- Tool Collisions: Identically named tools can override expected behavior
Ridge Security’s Mission: MCP-Aware Defense
As a cybersecurity company focused on agentic automation penetration testing, Ridge Security is advancing threat detection and simulation for AI-native environments, starting with Model Context Protocol.
We’re preparing for the MCP-powered future by simulating real-world risks and expanding our detection capabilities:
MCP Vulnerability Simulation & Auth Modeling
We’ve built a testbed to emulate realistic MCP attack patterns and deployment architectures, helping security teams evaluate exposure and mitigation strategies. Simulated vulnerabilities include:
- Shell command injection through unsanitized input
- Prompt injection via tool descriptions and system messages
- OAuth misconfigurations and confused deputy flows
- Access control bypasses from overly permissive tool bindings
- Token replay and audience validation gaps across trust boundaries (MCP-as-resource-server vs. all-in-one)
This setup enables controlled testing of both red and blue team responses across diverse MCP configurations.
Detection Capabilities
We’re enhancing RidgeBot with new protections tailored to MCP:
- Prompt injection and malicious tool detection
- Behavior-based anomaly tracking (e.g., tool drift, unauthorized data exfiltration)
- Environment and credential leak monitoring
- Replayable test cases to validate defenses before deployment
Our goal is to make securing MCP as practical and automated as securing any modern API stack. Contact us to learn more about our MCP research, testing tools, and enterprise defense solutions.