MCP Security: New Risks and Best Practices to Protect Yourself - CWV Test

The rapid evolution of the Model Context Protocol (MCP) has unlocked extraordinary new capabilities for connected AI agents, but it’s also introduced a sophisticated array of security challenges that demand urgent attention. From subtle tool poisoning and privilege escalation to credential leaks across distributed cloud environments, today’s MCP ecosystem is rife with novel attack vectors that conventional controls can’t fully address.

The same data‑in‑motion challenges affect every modern enterprise using API‑driven tools. Learn how Vorlon’s SaaS and AI Ecosystem Security platform unifies oversight across both systems, so you can manage risk with a single view

In this guide, you’ll learn how best practices, like strict access governance, continuous monitoring, network segmentation, and zero-trust strategies, enable you to navigate this high-stakes landscape, empowering you to safeguard sensitive data and maintain control in an environment defined by constant change and emerging threats.

What is MCP?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024 to streamline how large language models (LLMs) interact with external systems. Designed as a universal interface, MCP allows AI models to securely access real-time data, trigger functions, and interpret context beyond their original training.

According to Gartner®, “Model Context Protocol is an emerging standard to enable communication between AI applications, AI agents, applications and data sources. Although software engineering leaders should allow teams to experiment with MCP, they must demand vigilance to mitigate security, stability and governance risks.”¹

MCP fundamentally operates as an abstraction layer that mediates interactions between large language models and downstream services, streamlining communication and enabling seamless integration across diverse, dynamic environments. So, instead of building one-off integrations for every tool or data source, developers can now use a single, consistent protocol across platforms.

Why is MCP important?

The significance of the Model Context Protocol (MCP) lies in its transformative ability to streamline and accelerate the creation of LLM-powered applications. By enabling these models to dynamically generate function calls in response to user inputs, MCP fosters seamless interoperability between AI systems and external tools.

This standardized framework not only simplifies integration but also empowers developers to swap or upgrade components with minimal code disruption, driving modularity and flexibility. The protocol’s adoption momentum surged notably following OpenAI’s endorsement of Anthropic’s open standard, underscoring MCP’s emerging role as a foundational pillar in the evolving AI ecosystem. That said, MCP introduces real security challenges.

According to Gartner®, “MCP’s rapid adoption and continuous evolution are creating security risks that product leaders focusing on securing AI must address.”²

These risks include unvetted source integrations, insufficient controls around downstream function access, and limited native support for observability or approval workflows. Without strong governance and oversight, these gaps can expose systems to unintended behaviors or unauthorized actions. Solving these concerns is essential to making MCP safe, scalable, and enterprise-ready.

As organizations adopt MCP alongside hundreds of connected SaaS applications, visibility into cross‑application data flows becomes essential. Vorlon’s SaaS Ecosystem Visibility Layer shows exactly how data moves across apps, APIs, and AI services, uncovering blind spots legacy tools miss.

How does MCP work?

The Model Context Protocol (MCP) establishes a universal interface that enables language models to interact with external systems in a structured and predictable manner. Rather than relying on ad hoc plugins or brittle integrations, MCP defines a consistent method for LLMs to query data, invoke functions, and ingest contextual input from third-party sources.

In essence, it translates fragmented ecosystems into a coherent, programmable surface that models can reason over in real time. Here's how it operates at a high level:

Architecture

MCP utilizes a client-server architecture, where the MCP client resides within the host application, typically the AI model, while the MCP server functions as an intermediary layer, facilitating secure and standardized communication between the client and external data sources and applications.

Connection management

The MCP client acts as the trusted bridge between the model and the broader MCP ecosystem, maintaining persistent, secure communication channels to MCP servers. Whether operating over the open internet or within local, on-device environments, the client negotiates and manages connectivity based on the architecture's demands.

This dual-pathway approach (cloud-based or local) ensures the system can scale across environments while maintaining control, flexibility, and resilience. It’s a key node in the architecture, adapting to diverse deployment topologies without compromising security or access fidelity.

Function calls

The AI model uses the MCP client to generate JSON-RPC function calls from user queries. These calls are sent to the MCP server, which interprets them and selects the appropriate external tool or data source for processing. This architecture separates command generation from execution, ensuring secure and modular integration across diverse API endpoints.

Consequently, the AI focuses on understanding intent and creating commands, while the MCP server handles invocation, execution context, permissions, and response delivery. This layered interaction provides a robust and scalable framework, allowing AI applications to dynamically extend capabilities, interact with real-world data, and maintain consistent performance. This client-server division enhances extensibility, adaptability, and security in complex AI systems.

Response handling

In the MCP architecture, the communication cycle ends where it matters most: with the user. It starts when the MCP client issues a request, routing it to the MCP server, which orchestrates the necessary data fetches or command executions across integrated systems.

Once the server completes these operations, it returns the output in a precise and purpose-built format. The MCP client then relays this output to a connected AI model, which doesn’t just echo back data but interprets, contextualizes, and reshapes it into a response aligned with human understanding. This layered flow (request, execution, interpretation) anchors MCP’s core strength: turning fragmented, multi-cloud complexity into actionable, intelligent answers.

Persistent context

MCP enables persistent contextual awareness across user sessions, allowing the AI model to maintain continuity by referencing prior interactions. This sustained context empowers the model to deliver responses that are not only coherent but also deeply relevant, enhancing the overall quality and fluidity of user engagement over time.

Together, these components position MCP as a strategic abstraction layer between AI systems and the services they depend on, removing integration friction, simplifying orchestration, and expanding the operational reach of AI models.

There are multiple critical security risks that warrant rigorous attention, including:

• Prompt injection: Adversarial actors embed malicious instructions into user inputs or prompt chains to hijack model behavior, often by inserting unauthorized commands or bypassing intended guardrails.
• Tool poisoning: Attackers target the metadata or schema definitions in connected tools. By embedding malicious instructions or misleading descriptions within tool manifests, attackers can deceive the AI model into making unsafe decisions, effectively weaponizing trust in the protocol’s abstraction layer.
• Privilege abuse: This represents a critical risk in which unauthorized users exploit excessive access permissions to perform unauthorized actions, thereby undermining system integrity.
• Tool shadowing and shadow MCP: Malicious actors craft rogue MCP tools designed to impersonate trusted services, opening avenues for data breaches and stealthy compromise.
• Indirect prompt injection: represents a more insidious threat vector, one that leverages the AI system’s own contextual reasoning against it. Rather than issuing direct commands, attackers embed adversarial instructions within trusted data sources, documents, or metadata that the model is trained to parse or prompted to process. These hidden cues, disguised as legitimate content, are silently absorbed into the model’s context window, subverting behavior without triggering standard security heuristics. Models then act on the manipulated assumptions. It's a silent override of model alignment, one that requires rigorous input validation, context isolation, and provenance tracking to neutralize.
• Sensitive Data Exposure & Token Theft: Inadequate compartmentalization within MCP configurations can lead to the inadvertent leakage of credentials, API keys, or user data: assets that, once exfiltrated, become footholds for broader system compromise. Poor token hygiene or overly permissive scopes further elevate this risk, turning session artifacts into an attack vector.
• Command/SQL Injection & Malicious Code Execution: If the MCP server or connected tools lack rigorous input sanitization and execution boundaries, adversaries can weaponize these endpoints. Injected payloads, whether SQL queries, shell commands, or serialized objects, can traverse the system unchecked, enabling remote code execution, data corruption, or full takeover. Without strict isolation and runtime validation, MCP’s connective power becomes an attack surface.
• Rug Pull Attacks: Trust is the first compromise. An MCP-connected tool may initially exhibit legitimate behavior, only to pivot later, silently swapping safe outputs for malicious payloads once it is integrated into critical workflows. This betrayal of trust transforms a once-safe node into a coordinated point of failure.
• Denial of Service: By strategically overwhelming the system’s computational or network capacity, adversaries can degrade or completely suspend MCP’s operational flow. Whether through input flooding, recursive tool chaining, or forced invocation loops, such attacks aim to suppress functionality, starve AI decision-making, and fracture reliability at scale.
• Authentication Bypass: When identity boundaries are poorly enforced or absent entirely, attackers can slip past the perimeter, assuming trusted roles without detection. In an MCP-driven environment, this undermines the foundational trust between clients, tools, and AI agents.

Many of these vulnerabilities mirror the same integration risks already present in SaaS environments. Vorlon’s Data‑Centric SaaS Security capabilities extend this protection to MCP connections, tracing sensitive data wherever it flows.

Before implementing controls, security teams often need to understand how MCP activity interacts with broader SaaS risk. Vorlon’s unified platform correlates these events automatically, showing policies, anomalies, and affected applications in one interface.

MCP security best practices

To safeguard MCP deployments, the following approaches represent foundational security imperatives:

1. Strict authentication and authorization: Enforce rigorous authentication standards on the MCP server, utilizing strong, regularly rotated credentials alongside multi-factor authentication (MFA). Maintain granular authorization controls to ensure that only fully verified and authorized commands are executed, thereby minimizing attack surfaces and preserving system integrity.
2. Data encryption: Encrypt everything: at rest, in motion, and across every node of the MCP stack. Transport layers must default to HTTPS with modern TLS. Stored data (especially tokens, credentials, and user context) should be shielded with strong encryption standards. In MCP’s interconnected web, unencrypted data isn’t just exposed; it’s vulnerable by design.
3. Permission management: The principle of least privilege isn’t optional; it’s an architectural requirement. MCP servers should operate within tightly scoped permission sets, capable only of performing essential tasks. Anything more becomes an opportunity for escalation. Strip away unnecessary access, or risk handing attackers the keys to the system.
4. Input/output validation: Implement stringent validation for every request received by MCP servers, permitting only responses originating from verified, trusted sources. This rigor ensures data integrity and serves as a critical defense against injection attacks and corrupted inputs.
5. Monitor and log activities: Maintain comprehensive, tamper-resistant logs capturing all system interactions. Conduct continuous, proactive analysis of these records to promptly identify and investigate anomalous behavior or potential security incidents, reinforcing system resilience and situational awareness.
6. Use trusted MCP servers: Rely exclusively on verified, trusted MCP servers, maintaining an authoritative registry to govern trust boundaries. Ensure these servers are consistently updated with the latest security patches and software versions to mitigate emerging vulnerabilities and uphold system integrity.
7. Sandbox: Enforce strict isolation by running all runtime environments within properly configured sandboxes. This containment strategy constrains potential exploits, minimizes lateral movement, and fortifies the overall security posture of MCP deployments.
8. Incident response plan: Breaches aren’t hypothetical; they’re eventual. Therefore, a robust incident response strategy must be codified, rehearsed, and ready to deploy the moment containment is critical. This includes immutable backups, rapid recovery protocols, and clear escalation paths. Because downtime isn’t just lost time; it’s lost trust.
9. Regular audits and updates: Security isn’t a set-and-forget exercise. Routine audits should interrogate every layer of the MCP stack for drift, misconfigurations, and emerging threats. Patch cycles must be proactive, not reactive, because in an evolving threat landscape, stagnation is an open door to vulnerability.
10. Employee awareness: Cultivate a culture of security mindfulness by rigorously educating personnel on critical protocols and threat vectors. Empowering staff to recognize and adhere to established security measures is essential to mitigating human error, a predominant factor in system vulnerabilities.
11. Verification of external content: Approach all external inputs, whether from users or third-party sources, with strict scrutiny. Employ comprehensive validation and sanitization processes to ensure that only clean, authenticated data integrates into the system, thereby preserving the integrity and trustworthiness of the AI environment.

No single safeguard is absolute. True resilience emerges from layered defenses that intersect, overlap, and reinforce one another. When one control falters, another stands guard.

How does Vorlon help with MCP security?

Vorlon enhances the security of Model Context Protocol (MCP) environments by giving enterprises visibility, control, and governance across the AI‑driven connections that traditional tools can’t monitor. Acting as a policy and observability layer between large language models, MCP servers, and external applications, Vorlon reduces the risks introduced by dynamic function calls, AI automations, and third‑party tool integrations.

Here’s how Vorlon strengthens MCP security:

• Policy enforcement and access governance:
  Vorlon continuously monitors API activity across MCP connections, applying enterprise security policies to machine‑driven actions. Each integration or function call is evaluated for risk and intent using context such as origin, permissions, and data sensitivity. Integrations with tools like Splunk, ServiceNow, and XSOAR make it easy to embed MCP event data directly into existing workflows—see the complete list of Vorlon integrations for details.
• AI‑aware behavioral analytics:
  Instead of parsing model prompts, Vorlon analyzes metadata and behavioral patterns to detect potential prompt injection, context manipulation, or automated data exfiltration. It correlates anomalies—such as unusual data access, authentication reuse, or high‑frequency calls—to provide explainable alerts with full context.
• Trusted tool verification:
  Vorlon automatically discovers sanctioned and unsanctioned AI tools, APIs, and plug‑ins connected through MCP. It maintains a dynamic inventory of validated integrations, surfacing unauthorized or shadow tools for review and response.
• Comprehensive audit logging:
  Every interaction among AI agents, MCP servers, and SaaS or API endpoints is captured in auditable logs. These records deliver full transparency for compliance frameworks like SOC 2, HIPAA, and GDPR, and streamline post‑incident forensics and reporting.
• Dynamic permission control:
  Vorlon enforces least‑privilege access by continuously analyzing which users, service accounts, or AI agents can reach sensitive data. It enables fast revocation or rotation of risky secrets or tokens, preventing privilege escalation and lateral movement.
• Continuous threat detection and SIEM integration:
  Real‑time analytics highlight abnormal API activity, data flows, or identity behaviors. Vorlon integrates with SIEM, SOAR, and ITSM platforms such as Splunk, ServiceNow, and XSOAR for rapid alert triage and automated remediation.

By embedding Vorlon into the MCP stack, organizations gain a unified view of how AI systems, tools, and data interact, empowering them to monitor, investigate, and govern MCP‑based operations with confidence, without disrupting workflow speed or innovation.

In conclusion

The Model Context Protocol is transforming how AI systems interact with real‑time data, but its flexibility comes with new, complex risks that traditional controls can’t contain. As enterprises adopt MCP to accelerate innovation, they must also adopt a new layer of defense that keeps pace with this dynamic ecosystem.

Protection doesn’t stop at best practices. It requires visibility into how models, tools, and APIs actually behave in production.

Vorlon delivers that visibility. Acting as a policy enforcement and observability layer for MCP environments, Vorlon inspects every connection, enforces least‑privilege execution, and provides real‑time context for every AI‑driven action. Integrations with tools like Splunk, ServiceNow, and XSOAR make it easy to embed MCP event data directly into existing workflows—see the complete list of Vorlon integrations for details.

This unified approach allows security leaders to innovate safely transforming the promise of MCP from a security risk into a controllable, resilient advantage.

¹ Innovation Insight: Model Context Protocol. 24 April, 2025. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

² Emerging Tech: Security Implications of Model Context Protocol. 18 July 2025. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.