Agentic AI systems are designed to run autonomously for an extended period of time. 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 delves into the principles of MCP, its application in agentic AI, why protecting MCP is crucial, and how companies may design protection methods customized to this protocol.

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is a key layer that manages memory, tasks, and decision logic in agentic AI systems. It describes how agents store, access, and update their internal state over time. This process allows for precise reasoning and independent action. Unlike traditional models that operate on single-turn input-output cycles, agentic systems depend on persistent memory, multi-step planning, and long-horizon context. MCP provides the formal structure to support these capabilities.

Source: testcollab

At its core, MCP governs four interconnected components: short-term memory (STM) for immediate context, long-term memory (LTM) for historical knowledge, task queues for instruction sequencing, and an execution context that links decisions to actions. Each of them is managed via standardized interfaces that enable agents to monitor their own status, collaborate with other agents, and securely engage with external systems.

MCP is protocol-based, which means it establishes the rules for memory access, writing, versioning, and security. Agents use MCP to maintain a consistent state in asynchronous sessions, eliminate unnecessary actions, and reliably access previously learned information. Without MCP, agentic AI would lose continuity, leading to fragmented memory, unpredictable behavior, and weak automation workflows.

What is Model Context Protocol (MCP) in Agentic AI?

In agentic AI, the Model Context Protocol (MCP) is essential for how autonomous agents handle their memory, tasks, and decision-making over time. Agentic AI systems work continuously, so they need organized ways to remember past actions, understand the current situation, and plan future behavior. MCP provides this organization by standardizing how agents store, retrieve, and update their internal state across sessions.

Source: Medium

At its core, MCP connects four vital layers: memory (both short-term and long-term), task queues, execution context, and external APIs. This coordination allows agents to operate smoothly, staying aware of previous goals, actions, and results. For instance, when an AI assistant manages a multi-day workflow or coordinates tasks with other agents, MCP makes sure all context changes are seamless and secure.

Why MCP Security is Essential in Agentic Systems

Securing MCP is essential to protect agentic AI systems from manipulation, memory corruption, and instruction abuse.

1. Memory Injection Risks

MCP manages both short-term and long-term memory for agents, making it a high-value target. If compromised, attackers could inject false data into the memory stream. This misguides agent behavior, causing incorrect actions or dangerous automation. Memory poisoning at the protocol level spreads across sessions and linked tasks. Securing memory integrity is non-negotiable in autonomous systems.

2. Unauthorized Task Manipulation

MCP structures how agents receive and execute tasks in sequence. Without authentication and access control, attackers may manipulate task queues. This enables the silent execution of malicious instructions that appear legitimate. Manipulated agents may work with altered objectives without being detected. Strict control over instruction flow is essential to avoid internal compromise.

3. Context Drift and State Hijacking

MCP ensures context consistency across agent sessions and environments. If security is weak, malicious actors can alter stored context or trigger state resets. This may cause unpredictable behavior, redundant loops, or skipped validation steps. State manipulation also interrupts coordination in multi-agent systems. Strong session validation and context lock-in help keep everything in sync.

4. Privilege Escalation through Protocol Gaps

MCP defines agent rights and instruction scopes on the protocol level. If the enforcement is weak, it allows unauthorized users to access important tasks or settings. Attackers can take advantage of weak boundaries to gradually increase their privileges and gain access to company data. Secure MCP allows limiting access and preventing lateral movement and privilege escalation.

5. Data Leakage from Persistent Memory

Agents rely on MCP to store long-term insights, like sensitive work history and environmental information. If encryption and access controls are not implemented, this persistent memory will become a leak vector. Attackers who collect memory gain information about processes, system behavior, and credentials. Encrypting long-term memory and setting retention limits helps to prevent silent data loss.

How MCP Security Integrates with Agentic AI

MCP enables agentic AI systems to operate with continuity, coordination, and memory awareness by managing how agents interact with context and tasks.

1. Structured Memory Architecture

MCP divides memory into short and long-term layers. Short-term memory stores the current context, like recent inputs or execution state. Long-term memory contains accumulated knowledge, task history, and system feedback. This layered framework allows agents to act according to situation knowledge while retaining continuity. Each layer is indexed and retrieved using predefined context keys.

2. Task Queue Management

MCP uses task queues to manage how instructions are received, prioritized, and performed. Agents pull from these queues in a predetermined order to ensure that no tasks are ignored, repeated, or misaligned. Task metadata has dependencies, execution conditions, and priority tasks. This ordered flow allows agents to act asynchronously while maintaining order. Failed tasks are flagged for retry or audit.

3. Context Tokenization

To track an agent’s state across sessions, MCP uses context tokens. These tokens combine memory snapshots, task status, and environment variables into a unified reference point. To maintain consistency, each action or decision refers to its associated token. Tokens are regenerated in response to state changes or escalating events. This prevents context loss and allows for multistep reasoning.

4. Execution Interface Layer

Agents communicate with external systems using an MCP-defined execution interface. This layer handles API calls, file interactions, database queries, and other operational events. It ensures that all actions are within protocol-defined boundaries and security regulations. Each call generates an audit trail, which links it to the memory state and context. This ensures responsibility throughout autonomous workflows.

5. Event Handlers and Policy Gates

MCP includes event handlers that listen for changes in the environment, task outcomes, or memory updates. Policy gates evaluate whether certain actions or memory writes are allowed. These internal checks stop agents from executing unauthorized steps or corrupting their own memory. The combination of reactive and rule-based controls allows for both adaptability and governance in autonomous operations.

Core Components of MCP Security Explained

Each component of MCP security plays a distinct role in safeguarding agentic AI systems from internal compromise and external threats.

1. Access Tokens

Access tokens regulate which agents or systems can read from or write to the MCP stack. These tokens confirm the agent's identity, scope, and permission level before allowing any interaction. Without token validation, unauthorized actors could take over memory or add instructions. Tokens come with expiration dates and are connected to session-specific identities. This ensures that each access attempt is properly authorized and scoped.

2. Memory Integrity Checks

Memory integrity checks ensure unauthorized changes do not occur in temporary or permanent memory. Cryptographic hashes are added to memory blocks and verified with each access or update. If tampering is found, the system either suspends execution or starts rollback methods. These checks stop memory poisoning, replay attacks, and silent corruption. Keeping memory integrity helps track decisions across agents.

3. Instruction Audit Trails

Audit trails capture every read, write, and execution request processed through MCP. Each instruction is logged with metadata such as origin, timestamp, memory state, and execution result. This establishes a credible history of the behavior of the agents and the cause of the behavior. Security engineers can track suspicious behavior to specific agents or memory states. Audits are essential to establish accountability for autonomous systems.

4. Task Queue Governance

When governing task queues, assignment rules, sequenced, and priority need to be definitively established. These constraints prevent attackers from flooding instructions, starving themselves, or manipulating others. Queues are monitored for abnormalities such as unexpected surges, skipped dependencies, and unauthorized tasks. Governance policies also contain procedures for resolving conflicts. This makes the execution layer predictable and less susceptible to abuse or overload.

5. LTM Encryption

Long-term memory (LTM) stores sensitive agent data such as previous tasks, environmental insights, and persistent state. Encryption at rest makes sure that even if someone accesses the storage directly, the contents remain unreadable without the right keys. Key rotation, storage separation, and agent-specific encryption contexts further enhance security. LTM encryption is a security measure that can help prevent identity theft and data fraud.

MCP Security Use Cases for Enhanced Protection

MCP security protects mission-critical agentic operations by ensuring trusted memory, that commands are verified, and that tasks are protected while executing.

1. Autonomous Threat Monitoring

Persistent memory allows security agents to monitor networks, logs, and system activities continuously. MCP ensures that agents can keep track of threat patterns, event sequences, and incident correlations over time. Keeping memory secure and validating instructions are essential for preventing attackers from sending false signals or disabling detection processes. In hostile circumstances, agents must work inside a trustworthy context.  MCP security ensures that their threat models are accurate and secure.

2. API Workflow Management

Agentic AI systems are often deployed to manage complex CI/CD pipelines or production APIs. MCP structures the memory and task logic that these agents depend on for multi-step automation. If MCP security is weak, attackers may alter the sequence, inject dangerous payloads, or stall critical updates. Task queue governance and instruction audits protect workflows from silent compromise. Secure MCP ensures every API interaction follows authorized, context-aware logic.

3. Agent-on-Agent Collaboration

Multiple agents coordinating tasks, like scheduling, research, or escalation, depend on shared memory and synchronized context. MCP coordinates inter-agent communication and memory alignment. If a rogue agent tampers with the shared state, others may make incorrect conclusions. Memory integrity checks and access controls protect against corrupted collaboration. Secure MCP ensures that multi-agent networks operate with reliable coordination and information exchange.

4. Enterprise Knowledge Retrieval

LLM-based agents often pull sensitive internal data such as documents, emails, and operational history. MCP organizes this memory by linking questions to earlier queries and task results. If an attacker gets access, they could steal sensitive information or mix in false information. Encrypting long-term memory and requiring rigorous access tokens prevent data leaking. Secure MCP guarantees that agents only retrieve information that they are authorized to access.

5. Infrastructure Automation: Agents

Infrastructure agents perform tasks like provisioning servers, managing credentials, and scaling resources. These actions are based on memory states, scheduled tasks, and validated execution logic defined by MCP. If MCP is not properly secured, attackers may initiate unauthorized installations or change system states. Policy gates and instruction audits protect sensitive automation. Securing MCP ensures that only authorized and approved updates reach infrastructure components.

Final Thoughts

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

Akto is an agentic API 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 API security demo today to see how Akto secures agentic APIs and context protocols across your AI infrastructure.