MCP Security: Model Context Protocol for Agentic AI

a Agentic AI systems are designed to operate autonomously for an extended period. They are expanding quickly by introducing new features, but also becoming more complex. As of 2025, more than 63% of enterprise AI systems rely on durable agentic models, which often interact with external environments via numerous activities. In such systems, Model Context Protocol (MCP) has emerged as a key standard for managing shared memory, task history and long-term planning.

This blog explores the principles of MCP, its application in a agentic AI, the importance of protecting a MCP and how companies can design customized protection methods that tailored to this protocol.

What is MCP Security?

The Model Context Protocol (MCP) is a core architectural component of agentic AI systems that defines how agents manage memory, task state and decision logic over time. MCP enables the AI agents to store, retrieve and update a contextual information, allowing for the consistent reasoning, long-term context retention and the autonomous execution.

Unlike traditional AI models that operate in isolated, single-turn input–output cycles, agentic AI relies on persistent memory, multi-step planning, and contextual continuity across interactions. MCP provides a structured protocol that supports these capabilities by maintaining shared context across asynchronous sessions and agent workflows.

As a protocol-based design, MCP establishes rules for a context access, memory versioning, state updates, and security enforcement. This allows agents to avoid redundant actions, reuse prior knowledge and coordinate decisions reliably across complex environments.

Without MCP-or an equivalent context-management protocol-agentic AI systems face risks such as fragmented memory, inconsistent behavior and brittle automation workflows, limiting their scalability and operational reliability in production environments.

How Does MCP Security Improve Security for Modern AI Systems?

Traditional applications perform the predictably. When a user clicks a button, it triggers a predefined API call. These systems are the typically stateless, user-initiated and governed by explicitly defined a endpoints. Security is enforced on a per-route basis and their deterministic nature supports straight forward auditing, testing and validation.

MCP security solutions mark a shift in this paradigm. In AI-driven environments, autonomous agents act as active clients. They don’t passively await a user input; they infer intent, dynamically select APIs, and execute multi-step workflows based on the internal logic, memory, and real-time context. Each action may be influenced by a external inputs, prior interactions, or by learned behavior-resulting in non-deterministic and a context-sensitive execution.

Unlike a traditional apps, AI agents retain persistent memory-which may include user identity, access permissions, long-term goals, and a task history. If this memory is compromised (e.g via the prompt injection or context poisoning), the agent may continue making a unsafe decisions, long after the original attack.

Thus, MCP security focuses not just on endpoints, but on securing the reasoning, memory, and contextual flow that drives autonomous behavior. Traditional AppSec tools are often lack visibility into these dynamic interactions, falling short in areas like prompt injection, contextual drift, or live memory manipulation.

Why MCP Security Matters for Secure Agentic AI Systems

There are already more than a 5000 MCP servers in the use. However, many AppSec teams still focus on securing static APIs and UI-driven flows. They are not prepared for:

• Agents making API decisions in real time

• Context being a live attack surface

• Autonomous workflows executing sensitive operations

• Memory and task queues acting like privileged databases

What's Next? The next is why MCP security demands a new AppSsec mindset?

What Makes MCP Security Different from Traditional AppSec?

The MCP securities uses an agent-based design in which prompts, memory, and contextual flows control application behavior. Traditional AppSec models often prove ineffective due to their reliance on static APIs and predictable application flows.

Unique Challenges

• Context as Asset: In MCP, context acts as a stateful brain for agents. Attackers can leverage it to alter workflows, reactions, or circumvent controls.

• Memory Persistence: Context between sessions may result in loss of permission, leftover tokens, etc.

• Non-deterministic Behavior: Agents change through interactions. Because of this unpredictability, threats are more than just injection or exposure, they are also about how the agent "thinks" and "responds" to learnt context.

• No Traditional Boundary: MCP does not have traditional boundaries, like security perimeters. There is no single entry point; inter-agent communication, model chaining, and context syncs introduce risk vectors.

• Blind Spots in Current Tools: Legacy DAST/SAST tools do not grasp prompt logic, contextual memory, or LLM-agent behavior. Attack simulation demands context-aware fuzzing and memory misuse detection.

Rethinking AppSec for MCP

• Security by Design: Threat modeling to encompass memory states, rapid flows, and inter-agent interactions.

• Context Integrity Checks: Create baselines for agent memory and identify deviations in behavior or context drift.

• Prompt Protection: Secure prompt chaining, role separation in instructions, and message validation between models.

• Agent Lifecycle Monitoring: Keep track of agent actions, state changes, and communication paths for auditing purposes.

How to Secure MCP Memory and Task Protocols in AI Systems

The Model Context Protocol security focuses on how AI agents store state, complete tasks, and interact with internal communication agents.

Key Threats

• Memory Injection and Poisoning: Attackers can inject fake or a malicious data into short- or long-term memory, altering the agent behavior across sessions.

• Unauthorized Task Manipulation: Malicious actors can insert or restructure queued tasks, resulting in unauthorized actions or circumventing built-in constraints.

• Context Change and Hijacking: Context snapshots can be changed, which can interrupt execution flows and agent coordination.

• Privilege Escalation: Weak enforcement of MCP access limits can allow attackers to perform high-level or restricted actions across the protocol.

Mitigation Strategies

• Tokenized Access Control: Use scoped, expiring tokens for each agent to restrict read and write access to memory and task queues.

• Memory Integrity Verification: Verify memory blocks for cryptographic hashes or tampering before using them.

• Task Queue Management: Keep strict ordering, apply rate limits and check work queues to prevent a unauthorized manipulation.

• Audit Trails & Context Tokens: Log every interaction with memory or queue systems, including origin, context token, and result, and allow complete traceability.

• Encrypted Persistent Memory: Encrypt long-term memory at rest using agent- or session-specific keys, and enforce strict retention policies.

How Does the Model Context Protocol (MCP) Work?

The Model Context Protocol (MCP) is an open standard that defines how AI models securely interact with external tools, resources, and contextual data through a structured client–server interface.

Rather than embedding APIs directly into model logic, MCP enables AI systems to request, inspect, and invoke capabilities exposed by MCP servers. This design allows agentic AI frameworks to remain flexible, auditable, and extensible—while keeping models isolated from direct infrastructure access.

Importantly, MCP does not implement autonomy or memory itself. Instead, it provides a standardized bridge that agent frameworks use to access these capabilities safely.

Here’s how MCP works in practice:

Tool and Resource Exposure

MCP servers explicitly declare:

• Available tools (actions the model can invoke)

• Resources (readable contextual data)

• Schemas defining inputs, outputs, and constraints

The model does not discover APIs dynamically; it selects from a predefined, server-approved capability list, reducing ambiguity and attack surface.

This approach improves AI security, observability, and governance, especially in enterprise environments.

Structured Tool Invocation

Using the metadata provided by the MCP server, the model:

1. Understands what tools are available

2. Determines valid input/output formats

3. Generates structured requests aligned with the schema

This removes the need for hardcoded integrations and enables safe, policy-controlled execution of external actions.

Context Exchange and Session Awareness

MCP supports structured context exchange, which may include:

• User intent

• Environmental data

• Session metadata

• Historical references

While MCP transports this context, persistence and memory management are handled by external systems (e.g., vector databases or state stores) accessed via MCP tools.

This separation ensures clean security boundaries between models and sensitive data.

Agentic Execution (Handled Outside MCP)

Agent frameworks use MCP to:

• Retrieve context

• Invoke tools

• Store or retrieve memory

• Coordinate multi-step workflows

However, planning, task sequencing, retries, and autonomy are implemented outside MCP, making MCP a secure interface layer—not an agent runtime.

MCP Security: Why a New AppSec Model Is Required

Because MCP exposes high-context, high-privilege capabilities to models, traditional AppSec tools often fail to detect risks such as:

• Context poisoning

• Prompt injection via tools

• Unauthorized tool access

• Memory manipulation through exposed resources

Effective MCP Security focuses on:

• Tool authorization and least privilege

• Context integrity and validation

• Resource access controls

• Runtime monitoring of agent behaviorFinal Thoughts on MCP Security

Final Thoughts on Model Context Protocol (MCP) Security

MCP is more than just a protocol, it is the foundation of long-term autonomous reasoning. Without a proper MCP security solution, agentic AI systems become vulnerable to context movement, memory injection, and silent task corruption. As agentic AI continues to change how organizations scale automation, MCP security must evolve into a standard pillar of AI system design.

Akto is an Agentic AI security platform designed to secure not just APIs, but the evolving workflows of autonomous AI agents. With automatic discovery, contextual testing, and live inventory, Akto helps organizations monitor task flows, memory states, and cross-service interactions. Its agentic AI suite enables organizations to safeguard their MCP-aligned architectures from instruction misuse and memory leakage. Schedule a free demo today to see what MCP Security Solution Akto provides for your AI infrastructure.