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# Security in Model Context Protocol: An Analysis of the OWASP MCP Top 10

Standardizing AI integrations with the Model Context Protocol introduces a virtually infinite attack surface. Master the OWASP MCP Top 10 to mitigate indirect prompt injections and secure your deployments.

- **Speakers:** [Jose Manuel Ortega](https://www.wearedevelopers.com/@jose-manuel-ortega)
- **Event:** World Congress 2026 Europe - Virtual Stage
- **Published:** July 2, 2026
- **Duration:** 48:46
- **URL:** https://www.wearedevelopers.com/videos/1957-security-in-model-context-protocol-an-analysis-of-the-owasp-mcp-top-10

## Summary

The Model Context Protocol (MCP), introduced by Anthropic as a universal standard for AI tool integrations, solves the fragmentation of how large language models (LLMs) connect to external data sources. However, standardizing this architecture introduces a fundamentally new, virtually infinite attack surface where every tool description, resource payload, and prompt template becomes a potential injection vector. By operating over JSON RPC without natively mandating authentication frameworks, MCP radically shifts traditional trust boundaries and enables privilege escalation up to ten times greater than traditional REST APIs. To address these vulnerabilities, the OWASP MCP Top 10 catalogs the most critical risks, emphasizing that indirect prompt injection is the number one threat. When an LLM autonomously processes trojanized tool descriptions or malicious external resources, attackers can execute insecure tools, exfiltrate sensitive data, or exploit broken object level authorization and server-side request forgery. Further compounding the danger is the invisible vulnerability of logging failures, alongside the growing risk of AI supply chain attacks via compromised community-published MCP servers. Securing MCP deployments requires treating every tool invocation like an untrusted HTTP request and applying defense-in-depth methodologies such as the STRIDE threat model. Organizations must implement a strict three-zone gateway architecture, enforcing a five-layer security stack that includes mutual TLS (mTLS) for transport, OAuth bearer tokens for protocol access, and role-based access control with per-object ACLs. Bolstered by static analysis tools like Semgrep, strict JSON schema validation, and comprehensive anomaly monitoring, security engineers can successfully mitigate these novel risks and build resilient AI ecosystems.

**Keywords:** model context protocol, OWASP MCP top 10, indirect prompt injection, AI tool integrations, insecure tool execution, LLM privilege escalation, MCP security architecture, JSON RPC trust boundaries, mTLS authentication, OAuth bearer tokens, broken object level authorization, server-side request forgery, AI supply chain vulnerabilities, STRIDE threat modeling, Semgrep static analysis

## Chapters

1. **Introduction to the Model Context Protocol security agenda** (00:09) — An overview of securing the new attack surface introduced by standardizing AI tool integrations.
1. **Fundamentals and architecture of the Model Context Protocol** (01:53) — How standardizing AI connections through JSON RPC introduces trust boundaries at every component interaction.
1. **Understanding the four core security primitives of MCP** (04:35) — Analyzing the distinct security profiles of tools, resources, prompts, and sampling in AI integrations.
1. **Overview of the OWASP MCP top ten vulnerabilities** (08:13) — A catalog of the most critical risk categories affecting standard AI tool deployments.
1. **Mitigating indirect prompt injection in language models** (09:21) — How malicious instructions embedded in tool descriptions can hijack autonomous model execution without user interaction.
1. **Preventing insecure tool execution through strict schema validation** (12:13) — Why trusting models with arbitrary parameters requires treating every tool call like an untrusted web request.
1. **Addressing data exfiltration and insufficient authorization in MCP** (14:21) — How attackers exploit absent protocol-level authentication to turn compromised models into sensitive data extraction tools.
1. **Securing object level authorization and preventing data exposure** (17:36) — Preventing unauthorized access to sensitive database records and API keys inadvertently exposed in resource payloads.
1. **Preventing server-side request forgery and insecure deserialization** (20:04) — Blocking prompt-injected models from attacking internal infrastructure or executing arbitrary code via unverified payload inputs.
1. **Mitigating logging failures and supply chain vulnerabilities** (21:53) — Why comprehensive tool invocation logs and strict dependency versioning are essential for detecting hidden package backdoors.
1. **Adapting the STRIDE threat model for MCP deployments** (25:33) — Mapping spoofing, tampering, repudiation, information disclosure, and privilege escalation to AI connector architecture.
1. **Implementing a layered authentication and authorization stack** (28:31) — Securing connections using mutual TLS, short-lived OAuth tokens, and strict per-capability role-based access controls.
1. **Enforcing strict input validation and payload security rules** (30:33) — Defending against protocol abuse by rejecting unknown fields and enforcing strict JSON schemas over client-supplied parameters.
1. **Selecting security tooling for static analysis and monitoring** (33:03) — Utilizing specialized code scanners, dynamic proxies, and runtime tracing to identify misconfigurations across the deployment lifecycle.
1. **Designing a reference architecture with network trust zones** (35:21) — Isolating backend AI servers by routing all untrusted tool interactions through a stateless security gateway.
1. **Integrating security controls into the software development lifecycle** (37:26) — Automating vulnerability scanning, dependency checking, and fuzz testing during continuous integration and deployment pipelines.
1. **Implementing a practical security checklist for production environments** (39:28) — Verifying authentication, least privilege access, data sanitization, and observability requirements before deploying AI integrations.
1. **Analyzing a real supply chain attack scenario** (42:46) — Tracing how a trojanized dependency exfiltrates data by silently embedding malicious instructions into tool description metadata.
1. **Community resources and essential security takeaways for MCP** (45:11) — Leveraging community standards to build resilient, defense-in-depth AI applications that anticipate evolving attack vectors.

## Related Moments

- [Core concepts and advantages of the Model Context Protocol](https://www.wearedevelopers.com/videos/100305-api-mcp-or-mcp-app-choosing-the-right-surface-for-ai-agents) (from "API, MCP or MCP App? Choosing the right surface for AI agents")
- [Origin and purpose of the Model Context Protocol](https://www.wearedevelopers.com/videos/100132-the-agent-interface-layer-protocols-tools-and-trust-boundaries) (from "The Agent Interface Layer: Protocols, Tools and Trust Boundaries")
- [Integrating Model Context Protocol for non-technical users](https://www.wearedevelopers.com/videos/100091-3-ways-to-rebuild-the-data-stack-for-agents) (from "3 Ways to Rebuild the Data Stack for Agents")
- [Securing AI agents against malicious MCP servers](https://www.wearedevelopers.com/videos/2004-are-your-apis-ready-for-ai-agents) (from "Are Your APIs Ready for AI Agents")
- [Utilizing industry threat models for AI security](https://www.wearedevelopers.com/videos/715-a-hundred-ways-to-wreck-your-ai-the-in-security-of-machine-learning-systems) (from "A hundred ways to wreck your AI - the (in)security of machine learning systems")
- [Standardizing agent interactions with the Web MCP proposal](https://www.wearedevelopers.com/videos/1805-wearedevelopers-live-modern-devops-for-iot-devices-and-more) (from "WeAreDevelopers LIVE - Modern DevOps for IoT Devices and More")

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