AI’s Expanding Reach: Securing the Model Context

by

Author: Denis Avetisyan

As AI agents gain the ability to dynamically access external resources, a critical need emerges to address the unique security challenges posed by this expanding context.

The system establishes a distributed framework wherein a client, leveraging a designated protocol, requests services from a central server, effectively partitioning tasks and resources to optimize computational efficiency and scalability, as symbolized by the $client \leftrightarrow server$ interaction.
The system establishes a distributed framework wherein a client, leveraging a designated protocol, requests services from a central server, effectively partitioning tasks and resources to optimize computational efficiency and scalability, as symbolized by the $client \leftrightarrow server$ interaction.

This review analyzes the risks, controls, and governance frameworks for the Model Context Protocol and proposes a defense strategy against data exfiltration and supply chain attacks.

While increasingly sophisticated AI agents promise unprecedented automation, the shift towards dynamic, user-driven systems introduces novel security vulnerabilities. This paper, ‘Securing the Model Context Protocol (MCP): Risks, Controls, and Governance’, details the emerging threat landscape surrounding the Model Context Protocol and its implications for AI governance. We demonstrate that MCP’s flexibility can expand the attack surface through data exfiltration, tool poisoning, and privilege escalation, necessitating a layered defense framework encompassing authentication, provenance tracking, and sandboxing. How can organizations proactively establish robust governance for these dynamic systems and ensure the responsible deployment of increasingly autonomous AI agents?

Unveiling the Shifting Threat Landscape for Autonomous Agents

The accelerating adoption of AI agents, fueled by the capabilities of Large Language Models, is fundamentally reshaping the security landscape of critical infrastructure. These agents, designed to automate complex tasks and interact with diverse systems, are no longer confined to isolated environments; they are being woven into the fabric of essential services like finance, healthcare, and energy grids. This integration, while promising increased efficiency and innovation, simultaneously introduces novel attack vectors previously unseen in traditional cybersecurity. Unlike conventional software, AI agents operate with a degree of autonomy and rely on dynamic interactions with external tools and data sources, creating opportunities for malicious actors to exploit the agent’s decision-making processes or compromise the systems it controls. The very features that make these agents powerful – their ability to learn, adapt, and execute complex operations – also present new challenges for security professionals striving to protect against evolving threats.

Conventional security protocols struggle to defend against the novel threats presented by AI agents interacting with external resources. These systems were designed assuming static codebases and predictable interactions, but Large Language Models (LLMs) introduce dynamism; an agent can generate unique API calls, interpret varied data formats, and even adapt its behavior based on real-time feedback. This inherent flexibility circumvents signature-based detection and access control lists, as malicious actions may appear legitimate within the context of an LLM’s reasoning. Furthermore, traditional sandboxing proves ineffective when agents require access to external tools to fulfill their designated tasks, creating a tension between functionality and security. Consequently, a paradigm shift is necessary, focusing on runtime monitoring of agent behavior, intent analysis, and the implementation of robust input validation techniques tailored to the nuances of LLM-orchestrated interactions.

The integration of AI agents introduces novel security vulnerabilities, prominently through supply chain attacks and content injection. Supply chain attacks target the tools and data sources AI agents utilize, potentially compromising the agent’s operations by introducing malicious code or manipulating external resources. Simultaneously, content injection attacks exploit an agent’s reliance on external data by feeding it crafted inputs designed to bypass safeguards and elicit unintended actions, such as revealing confidential information. These attacks aren’t merely about disrupting service; successful exploitation can lead to unauthorized data exfiltration, where sensitive data processed by the agent is secretly copied and transmitted to malicious actors. The sophistication of Large Language Models further complicates defense, as subtle manipulations in input can bypass conventional security filters, demanding a re-evaluation of traditional security paradigms to protect both the agent itself and the data it handles.

Introducing the Minimum Viable Protocol for Communication (MCP)

The Minimum Viable Protocol for Communication (MCP) establishes a consistent framework for Large Language Models (LLMs) to access and utilize external resources. This standardization addresses the inherent challenges of interfacing LLMs with diverse applications and data sources, which previously required custom integration logic for each connection. By defining a common protocol, MCP facilitates interoperability and reduces the development overhead associated with building LLM-powered applications that rely on external data or tools. This allows developers to focus on application logic rather than communication protocols, and enables LLMs to function as versatile agents capable of interacting with a wide range of services.

MCP utilizes JSON-RPC 2.0 as its message encoding format to guarantee interoperability across diverse systems and streamline integration processes. JSON-RPC 2.0 is a widely adopted standard for building remote procedure calls, offering a defined structure for requests and responses. This standardization within MCP enables consistent communication patterns, reducing the complexity associated with parsing and interpreting messages. Specifically, requests are formatted as JSON objects containing a “method” key specifying the function to be called, a “params” key holding input parameters, and an “id” for request tracking. Responses follow a similar structure, including a “result” key for return values, an “error” key for reporting failures, and the corresponding “id”. By adhering to the JSON-RPC 2.0 specification, MCP minimizes the need for custom serialization or deserialization logic, facilitating seamless communication between LLMs and external components.

MCP supports two transport mechanisms to accommodate diverse deployment needs: Stdio Transport and Streamable HTTP Transport. Stdio Transport utilizes standard input and output streams for communication, suitable for local development and testing environments where direct process interaction is preferred. Streamable HTTP Transport leverages HTTP/1.1 and supports streaming responses, enabling communication over networks and facilitating integration with web-based applications. This mechanism supports both request and response streaming, improving efficiency when handling large volumes of data or long-running operations. The choice between these transports depends on factors such as network availability, scalability requirements, and the desired level of integration with existing infrastructure.

The Minimum Viable Protocol for Communication (MCP) establishes a secure interaction framework by defining roles for three core components: the Host application, the Client, and the Server. The Host manages the overall process and facilitates communication, while the Client initiates requests for data or actions. The Server processes these requests and returns responses. Security is foundational to MCP’s design; all interactions are structured to prevent unauthorized access and manipulation of data. Specifically, MCP utilizes standardized message formats and transport mechanisms, allowing for the implementation of robust authentication and encryption protocols to secure the channel between each component-Host to Client, Client to Server, and Host to Server-ensuring data integrity and confidentiality throughout the communication lifecycle.

The MCP Gateway architecture facilitates communication and data exchange between different systems or networks.
The MCP Gateway architecture facilitates communication and data exchange between different systems or networks.

Fortifying the System: Layered Defenses within the MCP Framework

Sandboxing establishes isolated execution environments for software components, limiting their access to system resources and data. This technique is particularly effective in mitigating Supply Chain Attacks by containing compromised or malicious code introduced through third-party libraries or dependencies. By restricting network access, file system operations, and API calls within the sandbox, the potential blast radius of a successful exploit is significantly reduced. Even if malicious code escapes initial security checks, the sandbox prevents it from directly interacting with the host system or sensitive data, allowing for containment and analysis. Effective sandboxing implementations utilize virtualization, containerization, or other forms of process isolation to enforce these restrictions and monitor component behavior.

Data Loss Prevention (DLP) encompasses a set of technologies and processes designed to detect and prevent sensitive data from leaving an organization’s control. These measures operate on the principle of content inspection, monitoring data in use, in motion, and at rest, and applying policies based on data classification and sensitivity. DLP systems utilize techniques such as keyword matching, regular expressions, and data fingerprinting to identify confidential information like personally identifiable information (PII), financial data, or intellectual property. Crucially, DLP functions as a secondary layer of defense; even if initial security measures are circumvented by an attacker, DLP can still block or alert on unauthorized data exfiltration attempts, mitigating the impact of a successful breach by preventing the loss of critical assets. Effective DLP implementations require ongoing policy refinement and adaptation to address evolving threat landscapes and data usage patterns.

Provenance tracking establishes a detailed, auditable record of data origins, transformations, and movements within the system. This logging includes information such as the user or process initiating an action, timestamps, data lineage – detailing each step of processing – and any modifications made to the data. The resulting audit trail allows security teams to reconstruct events, identify the root cause of incidents, and detect anomalous behavior indicative of malicious activity. Effective provenance tracking significantly reduces incident response times by providing context for investigations and facilitating containment efforts, while also supporting forensic analysis and compliance reporting.

Effective management of tools and resources within a Minimum Credibility Path (MCP) framework requires strict access controls, regular auditing, and continuous monitoring. This includes implementing the principle of least privilege, ensuring users only have access to the tools necessary for their specific roles. Automated monitoring systems should track tool usage patterns, identifying anomalies that could indicate compromise or malicious activity. Furthermore, a comprehensive inventory of all authorized tools and resources is crucial, along with procedures for patching, updating, and decommissioning them. Regular reviews of tool permissions and usage logs are necessary to detect and prevent abuse, whether intentional or accidental, and to maintain the integrity of the overall security posture.

Aligning with the Established Order: Security Standards and Risk Management

The architecture of Managed Control Planes (MCP), when integrated with robust security measures like sandboxing, Data Loss Prevention (DLP), and provenance tracking, demonstrates a clear alignment with the principles enshrined in ISO/IEC 27001, the globally recognized standard for information security management systems. This compatibility isn’t coincidental; MCP’s emphasis on controlled access, data protection, and auditability directly addresses key controls within ISO/IEC 27001, such as access control, data classification, and security logging. By establishing a framework for managing and monitoring dynamic agents, MCP facilitates adherence to these standards, enabling organizations to demonstrably improve their security posture and meet stringent compliance requirements. The proactive implementation of these combined technologies strengthens data confidentiality, integrity, and availability – core tenets of ISO/IEC 27001 – and provides a foundation for a resilient and secure AI ecosystem.

The deployment of Multi-Capability Provisioning (MCP), alongside sandboxing, data loss prevention, and provenance tracking, doesn’t operate in a vacuum; rather, these practices are intentionally designed to harmonize with established frameworks for responsible AI. Specifically, the methodology aligns with the NIST AI Risk Management Framework (AI RMF), providing a structured approach to identify, assess, and mitigate risks associated with AI systems. Simultaneously, it supports the principles of ISO/IEC 42001, the first international standard for AI management systems, which emphasizes trustworthy AI throughout its lifecycle. This congruence ensures that organizations adopting MCP can proactively address potential harms, foster transparency, and build confidence in their AI deployments, ultimately promoting the ethical and secure advancement of this rapidly evolving technology.

This research details a formalized threat model specifically designed for deployments utilizing Multi-Agent Control Planes (MCP), acknowledging the unique risks inherent in dynamic agent systems. The study moves beyond generalized cybersecurity approaches by identifying potential attack vectors targeting the interaction between agents, the control plane itself, and the underlying data flows. Building upon this threat model, a defense-in-depth control framework is proposed, layering multiple security mechanisms – including robust authentication, authorization protocols, and continuous monitoring – to mitigate identified vulnerabilities. The framework emphasizes proactive risk management, aiming to not only detect and respond to threats but also to prevent exploitation through careful system design and configuration, ultimately fostering a more secure and resilient environment for dynamic agent operations.

The modular control plane (MCP) isn’t designed as an isolated security solution, but rather as a facilitator of integration with established organizational frameworks. By deliberately aligning its proposed controls with the tenets of internationally recognized standards – including NIST AI Risk Management Framework (AI RMF), ISO/IEC 27001 for information security management, and ISO/IEC 42001 focused on AI management systems – MCP dramatically simplifies the process of adoption and compliance. This mapping allows organizations to leverage existing security infrastructure, policies, and audit procedures, reducing the overhead associated with implementing new technologies and demonstrating due diligence. Instead of requiring a complete overhaul of current systems, MCP offers a pathway to enhance security posture within a familiar and well-understood compliance landscape, fostering trust and accountability in dynamic agent deployments.

Responsible prompt engineering emerges as a powerful tool for organizations seeking to harness the capabilities of dynamic agents while bolstering security protocols. Rather than viewing prompts as mere inputs, a well-defined prompting strategy allows for the proactive shaping of agent behavior, guiding responses and limiting potential deviations from established security policies. This approach facilitates a defense-in-depth strategy, enabling organizations to anticipate and mitigate risks associated with unpredictable agent actions. By carefully crafting prompts, organizations can essentially embed security constraints and ethical guidelines directly into the agent’s operational framework, transforming prompts from potential vulnerabilities into valuable assets for maintaining control and ensuring responsible AI deployment.

The pursuit of securing the Model Context Protocol, as detailed in this analysis, reveals a fascinating truth about complex systems. It’s not enough to simply build walls; one must actively probe for weaknesses. Claude Shannon famously stated, “If you have to transmit information at a certain rate, then you must have a certain bandwidth.” This principle elegantly applies to AI agents and their contextual access. The ‘bandwidth’ isn’t merely data throughput, but the scope of permissible actions and data access. Limiting bandwidth – or, more accurately, rigorously controlling the ‘context’ – becomes crucial to prevent data exfiltration and supply chain attacks. The paper’s focus on dynamic analysis and provenance tracking isn’t about preventing communication entirely, but about understanding how information flows and ensuring its integrity – a direct echo of Shannon’s core insight.

What Lies Beyond the Protocol?

The exercise of securing the Model Context Protocol (MCP) reveals, predictably, that the most significant vulnerabilities aren’t within the protocol itself, but at its edges. Provenance tracking, while essential, functions as a rear-view mirror – documenting exfiltration rather than preventing the initial breach. The true challenge lies in anticipating the emergent behaviors of dynamic AI agents, particularly those operating within complex supply chains. It is a humbling realization that formal verification can only confirm adherence to known constraints, offering little solace against the genuinely novel attack vector.

Future work must shift from defensive perimeter building to proactive anomaly detection. Rather than meticulously cataloging threats, the field should embrace the chaos inherent in open systems, treating every agent interaction as a potential experiment in unforeseen consequences. The architecture of trust, it seems, is less about rigid control and more about resilient observation – a system designed to learn from failure, not simply prevent it.

Ultimately, the security of the MCP, and indeed all dynamic agent systems, will depend not on flawless code, but on a willingness to accept imperfection. To view every successful breach not as a failure of security, but as a valuable data point in an ongoing negotiation with complexity. The protocol is merely a scaffolding; the real system is the ever-evolving dance between agent, environment, and the inevitable entropy of information.

Original article: https://arxiv.org/pdf/2511.20920.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-28 06:41