As enterprises increasingly embed AI into their workflows, securing the underlying communication between models and their contextual environments has become critical. The Model Context Protocol (MCP)—an emerging abstraction for how models interface with external context like user identity, prompt history, and real-time task constraints—is pivotal in this shift. But with this flexibility comes risk. In this post, we unpack the security and vulnerabilities model for MCP and offer insight into how organizations can mitigate emerging threats.
🔍 What is the Model Context Protocol (MCP)?
The Model Context Protocol defines how large language models (LLMs) or multimodal models receive contextual data beyond a single prompt. This may include:
- User-specific memory or history
- Execution environment details (e.g., agent role, access scope)
- Real-time variables like time, location, device type
- Embedded embeddings or stored vector context
In short, MCP governs how models act in stateful, multi-turn, agent-like scenarios by injecting structured context—without directly modifying the model’s weights.
⚠️ Why is MCP a Security Concern?
Context injection becomes a new attack surface. Because context determines what a model can see and how it behaves, malicious or misconfigured context can:
- Exfiltrate sensitive data (prompt leakage or latent embedding leaks)
- Escalate privileges (e.g., accessing another user’s memory context)
- Introduce shadow prompts or indirect jailbreaking
- Trigger unauthorized agent actions (via task injection)
- Poison downstream embeddings or memory stores
These attacks don’t necessarily modify the model—but they exploit injected context vectors or serialized protocol messages, bypassing traditional model audits.
🧠 Vulnerability Model for MCP
Here’s a simplified breakdown of how vulnerabilities can be modeled in MCP:
| Layer | Example Threats | Mitigation Strategies |
|---|---|---|
| Context Retrieval | Memory spoofing, outdated context replay | Authenticated and timestamped retrieval |
| Serialization Layer | Schema injection, malformed context objects | Strict schema validation, signed context blobs |
| Execution Scope | Cross-user leakage, escalation of function access | Per-request scope enforcement, access tokens |
| Embedding Storage | Poisoning, vector inversion attacks | Store isolation, differential privacy, rotation |
| Audit & Replay | No forensic trail for context abuse | Immutable logs, context provenance tracking |
🛡 How to Secure MCP in Practice
- Context Isolation: Segregate contexts per user-session-application. Use per-user embedding stores to avoid cross-contamination.
- Signed Context Blobs: All context messages should be cryptographically signed and versioned.
- Dynamic Policy Enforcement: Implement runtime guards that check access scopes before allowing a model to use sensitive context.
- Audit and Alerting: Instrument MCP with logs that trace who injected or accessed what context—useful for detection and forensics.
- Sandbox Context Agents: Run agents with restricted, ephemeral contexts to reduce the blast radius of potential misuse.
🚨 The Road Ahead
As LLMs and autonomous agents continue to evolve, MCP will become a foundation layer for secure orchestration. It deserves the same scrutiny we apply to network protocols, container runtimes, or system calls. Standardizing context boundaries, trust assumptions, and signing mechanisms will be critical.
We believe the future of AI security is not just about model weights, but about how context is structured, authenticated, and enforced. MCP is where the next generation of AI supply chain attacks will emerge—and where proactive defenses must start.