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 agentic AI, the importance of protecting MCP, and how companies can design customized protection methods tailored to this protocol.

What is MCP Security?

The Model Context Protocol (MCP), a crucial component of agentic AI systems, describes the memory, task, and decision logic layer. It demonstrates how agents store, access, and update their internal state over time, enabling flawless reasoning and, therefore, independent action. Unlike traditional models that use single-turn input-output cycles, agentic systems rely on permanent memory, multi-step planning, and long-term context. MCP provides a formal framework to provide these functionalities.

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.

Why MCP Security is a Game Changer for Modern AI Protection

Traditional applications perform predictably. When a user clicks a button, it triggers a predefined API call. These systems are typically stateless, user-initiated, and governed by explicitly defined endpoints. Security is enforced on a per-route basis, and their deterministic nature supports straightforward 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 user input; they infer intent, dynamically select APIs, and execute multi-step workflows based on internal logic, memory, and real-time context. Each action may be influenced by external inputs, prior interactions, or learned behavior—resulting in non-deterministic and context-sensitive execution.

Unlike traditional apps, AI agents retain persistent memory—which may include user identity, access permissions, long-term goals, and task history. If this memory is compromised (e.g., via prompt injection or context poisoning), the agent may continue making 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 often lack visibility into these dynamic interactions, falling short in areas like prompt injection, contextual drift, or live memory manipulation.

Why Does It Matter?

There are already more than 5000 MCP servers in 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?

Why MCP Security Demands a New AppSec Mindset?

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 Protocol

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 malicious data into short- or long-term memory, altering 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 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 MCP work?

Unlike traditional applications, where API interactions are explicitly defined and triggered by users, Model Context Protocol security allows autonomous behavior. It transforms AI agents into self-driven clients that reason, decide, and act based on context and learned logic. It does not need step-by-step instructions. Here's how MCP works:

Dynamic API Discovery

MCP enables agents to identify APIs at runtime, rather than relying on pre-programmed endpoints. Implement this using an environment registry or tool manifest, where available APIs are documented with metadata such as function, input/output schema, authentication type, and rate restrictions. The agent analyzes this metadata to determine which APIs are relevant to its current work and how to interact with them, enabling real-time adaptation to new tools or services.

Understanding How to Use APIs

MCP-enabled agents don't just find APIs; they interpret how to use them through prompt engineering, internal documentation, or schema-based learning. For example, an OpenAPI specification or a structured YAML file can help the agent create appropriate request payloads. To make valid and proper calls, the agent relies on memory, previous usage patterns, or integrated examples. This reduces the need for hardcoded logic and allows for more flexible job execution.

Real-Time Context Exchange

Context is crucial to MCP. It includes the user's identification, present goals, environmental conditions, previous activities, permissions, and conversation history. Multiple agents or modules share this context to provide decision-making continuity. For example, if an agent is assisting a user in booking a flight, the context may include the destination, desired schedule, and budget. Any other agent who joins this workflow can immediately act on the shared context without needing to restart the process.

Autonomous Decision-Making and Execution

Once the agent has access to the required APIs and context, it can make independent judgments. It evaluates the goal, chooses the suitable tool, creates the payload, and initiates the call. Depending on the response, it can save results, modify memory, or call into subsequent APIs in a chain. This workflow is similar to how a human uses software to reach their goals, but the agent applies logic, memory, and reasoning patterns complete at speed and scale.

Integration with Akto MCP Security

MCP-based environments provide dynamic, high-context processes powered by intelligent agents. These agents rely on persistent memory, asynchronous task queues, and session-wide context chaining. Legacy security solutions, such as API scanners and firewalls, often fail to understand how agents store information, share context, or utilize chain logic. As a result, vulnerabilities including context poisoning, prompt manipulation, illegal task injection, and memory drift remain unnoticed.

Akto has purpose-built its MCP Security features to cover this evolving threat landscape. Here's how:

MCP Server & Endpoint Discovery

Akto is an MCP security platform that continuously scans environments to identify all MCP servers, their memory storage APIs, context endpoints, and agent communication flows. It automatically detects endpoints such as /context, /memory, /task, and /agent-exchange. Endpoints with poor authentication or no access control are flagged as exposed.

Context-Aware Testing Engine

Akto simulates real-world agent behavior to test how memory and context are processed and reused. Inserts altered context objects and tracks changes to the decisions made by agents. Tests prompt chains against input interferers to detect prompt leakage or unauthorized completion—Inspects memory request/response for the reflection and persistence of risky data.

Task Queue Abuse Detection

MCP agents often rely on task queues to sequence actions. Akto detects whether any queues are vulnerable to injection attacks, manipulation, or privilege escalation. It identifies unauthenticated task injection vectors and keeps tasks consistent and ordered across agent sessions. It also replicates task escalation scenarios to identify privilege misconfigurations.

Runtime Monitoring & Drift Detection

Akto doesn't just scan, it monitors. Once installed, it tracks how agents behave and how their context evolves over time. It monitors agent memory access and modification patterns. It identifies anomalies, such as unauthorized overwrites or unexpected context inflation, which signal that agent memory may have been manipulated. It connects memory changes with origin events to detect attack chains in real time.

Security Posture Dashboard for MCP

Akto presents an intuitive dashboard tailored to MCP systems, offering visibility into:

• Active agents, their memory size, and access rights.

• Insecure context handlers or endpoints.

• High-risk prompt templates or model instructions.

• Identify problems in memory and task usage.

Outcome for Security Teams

With Akto's MCP Security:

• Security engineers gain deep visibility into agent behavior, memory usage, and protocol-level risks.

• DevSecOps teams can run automated MCP-native test cases as part of CI/CD pipelines.

• Response teams are empowered to monitor and triage real-time drift or attacks against memory/state.

• AppSec teams build policy-based access controls around sensitive context and task endpoints.

Final Thoughts

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 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 demo today to see what MCP Security Solution Akto provides for your AI infrastructure.