As artificial intelligence (AI) and large language models (LLMs) become more widely adopted across industries, the methods by which these models receive, retain, and utilise context have grown in importance. A newer concept entering the discourse is the Model Context Protocol (MCP) a method for structuring and persisting information that an AI model can “remember” or reference across interactions.
While the potential benefits of MCP for enhancing personalised AI experiences and persistent workflows are considerable, the introduction of such a protocol also raises serious security risks that developers, businesses, and regulators must carefully consider. This article explores these risks, categorising them into core threat domains and offering insight into mitigation strategies.
What Is Model Context Protocol (MCP)?
The Model Context Protocol refers to a set of rules or mechanisms used to manage and structure long-term memory or persistent context for LLMs. This could include:
- Prior conversation history
- User preferences
- Project data
- Linked external documents or APIs
- Task progress tracking
Unlike ephemeral chat history, MCP allows for structured, persistent context to be stored and reloaded with each session or interaction. This aims to simulate a kind of “memory” in LLMs without fundamentally changing the underlying model architecture.
This persistent context can exist in different forms:
- Inline context passed during each interaction
- Out-of-band context stored in an external system
- Hybrid approaches combining both techniques
Why MCP Introduces Security Risk
While MCP enhances functionality, it introduces a new attack surface. These security risks are broadly categorised into:
1. Context Poisoning Attacks
One of the most prominent threats is the risk of context poisoning. This occurs when a malicious actor manipulates the information in the context memory to cause:
- Misleading responses
- Biased outputs
- Bypassing safety filters
Example: A compromised MCP data store could include injected instructions or altered facts, leading the model to generate incorrect or harmful content, even if the model itself remains intact.
Mitigation: Ensure all context sources are authenticated and validated. Employ integrity checks and sandboxing for third-party inputs.
2. Data Leakage and Confidentiality Violations
Persistent memory inherently stores user data, documents, prompts, or commands. If improperly secured, this opens the door to:
- Data leakage between users
- Accidental exposure through prompts
- Unintentional model hallucinations revealing private data
Example: A shared MCP store might leak financial summaries from one client’s context into another’s session due to a misconfigured access layer.
Mitigation: Use strict access controls, per-user context isolation, and encryption both in transit and at rest.
3. Context Injection via Prompt Manipulation
MCP relies on context passed to the model, which might include previous interactions, external documents, or system-level instructions. If attackers can influence these inputs, they can exploit prompt injection vectors to:
- Override instructions
- Trigger dangerous outputs
- Leak internal functions or memory data
Example: Embedding “ignore previous instructions” into an uploaded file that is later summarised by the AI.
Mitigation: Sanitise all user inputs, and compartmentalise context ingestion layers to limit prompt injection exposure.
4. Model Impersonation and Session Hijacking
In multi-user environments, sessions may rely on contextual data tied to user identities. Poorly implemented MCP could allow:
- Session fixation
- Hijacking another user’s memory context
- Impersonating a different user’s identity
Example: If user A’s context is incorrectly loaded into user B’s session, sensitive or personalised data could be exposed.
Mitigation: Implement strong session management, authentication, and auditing mechanisms.
5. Inference and Profiling Attacks
The accumulation of context allows for an attacker to infer patterns about user behaviour, preferences, or operations. These attacks may not exploit a direct breach but use repeated queries to model user context.
Example: An external user repeatedly queries a system to deduce decision-making logic embedded in the memory context of a company executive.
Mitigation: Limit query rates, apply differential privacy where feasible, and avoid excessive retention of sensitive behavioural traits.
6. Regulatory Compliance and GDPR Violations
Persistent contextual memory introduces compliance challenges. For systems handling data subject to GDPR, HIPAA, or other privacy laws, MCP must respect:
- Right to be forgotten
- Consent for data processing
- Data minimisation principles
Example: If the MCP stores user data without an audit trail or deletion mechanism, it may violate data protection requirements.
Mitigation: Make MCP data traceable, deletable on demand, and ensure clear user consent for context persistence.
Design Principles for Secure MCP Implementations
To responsibly integrate MCP into AI systems, the following security-by-design principles are advised:
🔐 Access Control
Use role-based access control (RBAC) or attribute-based access control (ABAC) to ensure only authorised entities can read/write to context memory.
🧱 Isolation
Isolate context per user, per session, or per tenant to avoid cross-contamination of sensitive data.
🕵️ Audit and Logging
Track every read and write operation to the MCP. Build audit trails to support incident response and compliance.
🧹 Expiry and Retention Policies
Context should have lifecycle rules. Automatically expire or archive stale context entries to reduce attack surface.
📦 Input Sanitisation
Any data entering MCP (documents, URLs, chat history) should be sanitised, scanned for malware, and normalised.
🧬 Data Integrity
Sign or hash memory context blocks to verify they haven’t been tampered with during transit or at rest.
🎯 Prompt Injection Mitigation
Apply prompt wrapping, output filtering, and instruction anchoring techniques to resist injection attacks.
The Future of MCP and Security
As AI systems gain memory and context-aware capabilities, protocols like MCP will play a central role in how systems simulate continuity, learning, and personalisation. However, as with any powerful tool, its misuse can be equally significant.
Much like cookies and session management in traditional web applications, MCP will need to evolve alongside governance frameworks, privacy legislation, and defensive engineering.
Key takeaways for organisations exploring MCP:
- Treat context as sensitive data
- Design with least privilege and zero-trust in mind
- Bake in observability, revocation, and data minimisation from the start
Conclusion
The Model Context Protocol represents a significant advancement in making language models more useful and adaptive. But with persistent context comes persistent risk. As adoption increases, attackers will explore MCP-specific vulnerabilities in the same way they exploit session hijacking, XSS, or CSRF in web applications today.
By adopting secure-by-design practices and staying ahead of the evolving threat landscape, organisations can embrace MCP’s promise without compromising on trust or safety.