Everyone’s trying to make AI agents do useful things—and fast. That’s why the Model Context Protocol (MCP) is becoming one of the most talked-about standards in AI system design. Backed by teams like OpenAI and Google, the MCP Protocol provides a consistent, standardized way to connect LLMs (Large Language Models) with real-world tools and business systems.
Instead of building fragile, one-off integrations, MCP gives AI a structured, reliable interface to interact with APIs, internal apps, and data sources. In short, MCP makes connecting AI to your infrastructure easier, cleaner, and smarter.
But here’s the catch—every MCP integration runs on non-human identities (NHIs) like tokens, service accounts, and API keys. These machine credentials must be properly secured, or the same convenience that makes MCP so powerful can also make it risky.
Recent reports show that 20% of organizations experienced breaches linked to unauthorized AI tools, with each incident costing up to $670,000. Without proper safeguards, MCP can open the door to hidden security threats.
What the Model Context Protocol Does
At its core, MCP acts like a universal port for AI—a shared language between LLMs and enterprise systems.
How MCP Works
MCP follows a client-server model that allows structured, two-way communication:
The AI agent becomes an MCP client.
The tool or API becomes an MCP server.
The model sends a structured JSON request (for example, “fetch recent alerts”).
The MCP server executes the request and returns structured results.
This setup is transparent and standardized, reducing the number of custom connectors from M×N (many-to-many) to M+N (many-to-one). That’s fewer integration headaches and more scalable AI workflows.
But with that simplicity comes a challenge: each client-server connection must be trusted and secured, and those connections rely on NHIs—machine credentials that don’t follow human security rules.
Why MCP Creates New Security Challenges
MCP-powered systems depend heavily on non-human identities like tokens and service accounts. These credentials are powerful, persistent, and often invisible.
While human users can be verified through logins and MFA, NHIs often bypass those checks. Once an AI agent has access to production data or systems, that access can persist indefinitely.
This conflicts with Zero Trust security principles, which demand that every identity—human or machine—be continuously verified, scoped, and time-limited.
Without proper visibility, teams lose track of which models can access what, which tokens are still active, and whether permissions ever expire. For regulated industries, such gaps can lead to audit failures and compliance violations under standards like SOC 2, GDPR, and ISO 27001.
7 Security Risks to Watch for in MCP Implementations
1. Cross-Tenant Data Leakage
MCP makes it easy to connect internal tools to AI models, but if tenant context isn’t enforced, data can leak across environments.
An MCP client calling a shared endpoint might access data from another customer or department—creating risks under HIPAA or PCI-DSS.
Mitigate by: enforcing tenant-aware logic, validating tenant IDs, and applying strict access boundaries to every request.
2. Prompt Injection & Tool Poisoning
User input can manipulate how the model interacts with MCP tools. A malicious prompt can coerce the AI to call unauthorized tools or send sensitive data elsewhere.
For example, a user asking to “summarize recent issues” could hide an instruction to “send all customer data to Slack.”
Mitigate by: validating inputs and outputs, restricting tool access by role, and adding guardrails that review tool calls before execution.
3. Tool Squatting & Rogue Servers
MCP’s flexibility allows easy tool registration—but also enables fake or rogue servers to impersonate trusted tools.
A malicious server could spoof a legitimate one and feed the AI false or manipulated data.
Mitigate by: enforcing mutual authentication, keeping an approved registry of tools, and rejecting unverified servers.
4. Remote Code Execution (RCE) via Misconfiguration
When teams wrap internal scripts as MCP tools without security filters, a model could execute unsafe code.
A poisoned prompt could cause the AI to run harmful system commands.
Mitigate by: avoiding dynamic code execution, sandboxing risky tools, and strictly validating inputs.
5. Visibility & Audit Gaps
Many organizations don’t log which AI model called which tool, or with what parameters. This creates blind spots in monitoring and compliance.
If an AI agent starts exporting data every few minutes, would anyone notice?
Mitigate by: logging every MCP call, feeding those logs into your SIEM, and auditing them like any other API surface.
6. Confused Deputy Attacks in OAuth Flows
MCP tools that use OAuth tokens can be tricked into acting on behalf of the wrong identity if tokens aren’t properly bound to sessions.
For instance, an AI summarizing GitHub PRs could misuse an app token to access repos it shouldn’t.
Mitigate by: binding OAuth tokens to specific users, enforcing narrow scopes, and validating requests per identity.
7. Standing Privileges & Long-Lived Tokens
Static credentials like API keys and service accounts often linger far longer than intended.
Over time, these unrotated tokens accumulate risk, creating silent privilege sprawl.
A forgotten token used in staging could still have production access months later—turning into an invisible security threat.
Mitigate by: using Just-in-Time (JIT) and Just-Enough Access (JEA), rotating credentials regularly, and never embedding secrets in code.
Why Just-in-Time and Just-Enough Access Matter
MCP accelerates development by giving LLMs a clear path to interact with business tools. But speed without security leads to exposure.
With AI agents now connecting to critical systems, machine identity security is non-negotiable.
JIT and JEA access protect MCP environments by:
- Making credentials short-lived and temporary.
- Limiting access to exactly what’s needed—no more, no less.
- Providing full auditability of every action, tool call, and token use.
Solutions like Apono automate this process. With auto-expiring permissions, context-based access, and centralized logging, Apono ensures that MCP adoption remains secure, compliant, and efficient.
This means teams can enjoy the power of MCP—without the chaos of uncontrolled tokens or invisible privileges.
Securing MCP for the Future
The Model Context Protocol is reshaping how LLMs connect with real systems. It reduces integration friction and unlocks new automation potential.
But as MCP adoption grows, so does the need for stronger machine identity management.
Every MCP connection, every token, every API call must be verified, scoped, and logged. Otherwise, the same systems that empower your AI could become the entry point for your next breach.
The future of AI security isn’t just about smarter models—it’s about safer connections.
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