Secure MCP Servers and Agent Skills
AI agents connect to tools, data, and systems through MCP servers and agent skills. Straiker discovers MCP servers and skills, scans them for security risks, tests how agents use them, and enforces runtime policy to stop tool poisoning, malicious skills, rug pulls, output injection, and unauthorized actions.
Problem
Every MCP server or agent skill can give an AI agent new instructions, permissions, tools, and access to data. One malicious or misconfigured component can turn that access into unauthorized actions, data theft, or compromised agents.
Solution
Straiker finds and secures MCP servers and agent skills before and during use. Discover what agents can access, scan for risk, test for exploitable behavior, and enforce policy when agents act.
Don't let MCP's strengths be its weakness
Without visibility and control, MCP-powered agent-tool workflows enable privilege escalation, unsafe tool chaining, and sensitive data exposure.
#1 Attack Vector
Tool poisoning via MCP is the top attack vector across every agent type
Straiker Agentic Risk Framework
91%
Of successful attacks on productivity agents result in silent data exfiltration — no jailbreak, no malware required
Straiker STAR Labs, March 2026
17,000+
MCP servers scanned by Straiker, expanding the attack surface every time an agent connects to a new one
The challenge of securing MCP at scale
Tool Poisoning & Rug Pulls
An MCP server your team approved yesterday can be weaponized today — silently, without anyone noticing. Attackers embed hidden instructions that your AI agent follows without question. These are CRITICAL-severity threats with no detection in traditional security stacks.
Output Injection & Privilege Escalation
When an AI agent processes a tool result, it can't tell the difference between legitimate data and an attacker's instructions. A single compromised tool call can cascade into unauthorized access, data theft, or full account takeover.
Shadow MCP Servers & No Inventory
Enterprises have no central inventory of which MCP servers are running, what tools they expose, or or what data they can access. MCP connections can operate with no hygiene checks, no authorization controls, and no audit trail.
Risk to Control for model context protocol
Stronger trust and control for AI agents
MCP security ensures authorized, monitored, auditable agent-tool interactions, building enterprise trust and enabling safe scale without data loss or policy violations.
Smaller attack surface and faster response
Visibility, least-privilege access, and runtime guardrails detect hygiene flaws and tool misuse early, isolate threats, investigate faster, and restore operations confidently.
Governance and compliance you can prove
Centralized policy, input and output validation, and end-to-end audit logs demonstrate control, meeting security and privacy requirements for AI adoption.
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Frequently Asked Questions
What is the Model Context Protocol (MCP) and why does it matter for security?
The Model Context Protocol (MCP) standardizes how AI agents connect to external tools, APIs, and data sources. Securing MCP is critical because agent-tool interactions introduce three distinct risk categories: configuration and hygiene flaws in MCP servers, runtime misuse when agents chain tools in unintended sequences, and supply chain risks from third-party MCP servers with weak authorization or unsafe defaults. Straiker has scanned more than 13,000 MCP servers and found hygiene flaws across hundreds of them, making MCP security a high-priority control for enterprises deploying AI agents.
What are the main risks in MCP implementations?
The main MCP security risks are supply chain vulnerabilities, unsafe runtime tool use, and visibility gaps. Third-party MCP servers can introduce weak authorization and unsafe defaults, while agents can misuse tools through unintended sequences or unsafe parameters. Without complete visibility into MCP servers, clients, and permissions, security teams can also miss unauthorized access paths. Straiker's Discover AI and Defend AI address all of these risks across discovery, posture, and runtime enforcement.
How does MCP security protect agent-tool interactions?
MCP security inventories servers, applies static risk scoring, and hardens configs. At runtime, Straiker's MCP security tools enforce least privilege, validate inputs and outputs, monitor tool calls, and block unsafe actions to stop misuse and prevent data exfiltration.
How do Straiker products map to MCP security?
Straiker uses a See-Test-Protect approach for MCP security. Discover AI inventories every internal and external MCP server across your agentic ecosystem and scores each one for hygiene risks before they become threats. Ascend AI continuously red-teams your MCP connections, testing for tool poisoning, rug pulls, and privilege escalation. Defend AI enforces runtime guardrails on every tool call, blocking unauthorized actions and data exfiltration at 98%+ accuracy and low latency.
What best practices should teams follow to secure MCP?
Six practices form the foundation of MCP security:
- Maintain a complete inventory of MCP servers and clients so security teams know what exists and what agents can access.
- Scan MCP servers before deployment for hygiene flaws such as weak authorization, unsafe defaults, and misconfigured permissions.
- Enforce least-privilege authorization so agents can access only the tools and data required for their task.
- Validate MCP inputs and outputs to help block prompt injection and other malicious content delivered through tool results.
- Monitor tool calls in real time to detect unsafe chaining, parameter abuse, and policy violations.
- Maintain end-to-end audit logs mapped to compliance requirements for forensics and incident response.
Together, these controls provide visibility, prevention, and runtime enforcement across the MCP attack surface.
Protect EVERY AI AGENT
As enterprises build and deploy agentic AI apps, Straiker provides a closed-loop portfolio designed for AI security from the ground up. Ascend AI delivers continuous red teaming to uncover vulnerabilities before attackers do, while Defend AI enforces runtime guardrails that keep AI agents, chatbots, and applications safe in production. Together, they secure first- and second-party AI applications against evolving threats.
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Secure the agentic frontlines
You’re building at the edge of AI. Forward-thinking teams use Straiker to secure AI agents, detect emerging attack paths, and safely scale agentic AI across their organization. With Straiker, you have the confidence to deploy fast and scale safely.
