1. Introduction and Objectives

Background and Rationale

As AI systems continue to commercialize at a rapid pace, interaction between systems and the sharing of contextual information have become critical to ensuring model accuracy, maintaining inference continuity, and enabling adaptive responses. Within this landscape, the Model Context Protocol (MCP) has emerged as a promising new framework designed to structure and standardize context delivery in large language models (LLMs) and agent-based systems[1].

MCP facilitates inter-model collaboration, execution context sharing, and policy-driven access control through a structured context flow mechanism. It is particularly significant for its potential to integrate with Zero Trust security architectures, offering a strategic foundation for secure AI operations[2]. However, MCP remains in the early stages of design and implementation, and its structural characteristics introduce several critical security threats:

• Context misuse across layers

• Exploitation of delegated authentication

• Policy bypass and model misbehavior[3][4]

These threats are not mere configuration flaws—they pose serious risks that could undermine the reliability, consistency, and trustworthiness of AI systems as a whole.

Purpose of This Analysis

The objectives of this white paper are as follows:

1. To systematically review and analyze 15 of the most recent MCP-related research papers published between November 2024 and April 2025, focusing specifically on the identification, classification, and structuring of security threat scenarios presented in the literature.

1. To conduct a comprehensive analysis of all currently available MCP publications, following the initial release of the technology by Anthropic in November 2024.

1. To identify real-world attack vectors and categories of vulnerabilities that may emerge in LLMs and AI agent systems built on top of MCP-based architectures.

1. Based on this threat analysis, to propose policy-driven countermeasures and actionable technical safeguards, while highlighting the need for a new security architecture purpose-built for the MCP era.

Scope of Literature Reviewed

This analysis is based on a total of 15 MCP-related research papers and technical reports published across platforms such as arXiv, ResearchGate, Preprints.org, and Anthropic. The selected documents span the following core subject areas:

1. MCP architecture and standardization trends

2. Policy-based control and authentication delegation models

3. Integration of LLMs and context delivery flows

4. Security threat scenarios within MCP systems, including vulnerabilities, anomalous behaviors, and audit limitations

A full summary of the reviewed literature is presented in Summary Table of 15 Key MCP-Related Papers of Section 2 (Literature Review Framework and Classification), with each reference cited accordingly throughout the following sections in relation to relevant strategies and findings.

Analytical Framework

Each selected publication was analyzed both quantitatively and qualitatively using the following four criteria:

• Topic Classification by Core Keywords: Categorization based on MCP-security-related keyword clusters.

• Structuring by Threat Type: Organization of scenarios into threat categories T1 through T4.

• Evaluation of Source Reliability, Timeliness, and Applicability: Prioritizing documents from credible, up-to-date research platforms.

• Relevance to MCP Security Architecture: Assessed based on strategic alignment and applicability within real-world implementation.

This analytical framework is applied throughout Section 3 (Security Threat Scenario Analysis in MCP-based Systems) and Section 4 (Strategic Recommendations Based on Security Threat Analysis) in this white paper.

Document Structure

This white paper is organized into five key parts, each designed to analyze the security threats associated with Model Context Protocol (MCP)–based AI systems and to propose structural countermeasures along with a next-generation security architecture:

1. Introduction and Objectives

Presents the background behind MCP and highlights the emerging security risks in context-aware systems. It explains the need for a structured threat analysis based on a review of 15 key publications.

2. Literature Review Framework and Classification

Categorizes and evaluates 15 recent MCP-related papers both quantitatively and qualitatively. The analysis is mapped to threat categories T1 through T4, and a summary table includes a numerical relevance score (MSR) to indicate each work’s strategic contribution.

3. Security Threat Scenario Analysis in MCP-based Systems

Details four core threat types (T1–T4) observed within MCP systems, using scenario-based examples. This section includes code samples, execution flows, and failure points to visually explain each threat structure.

4. Strategic Recommendations Based on Security Threat Analysis

Proposes four strategic pillars—Policy Binding, Context Integrity, Delegation Control, and Audit Traceability—to address T1 through T4 threats. Each strategy is mapped to the applicable security layer within an MCP architecture, along with the expected impact.

5. Conclusion and Proposal for a New Security Architecture

Introduces MCP PAM (Model Context Protocol Privileged Access Management) as an integrated, autonomous, and policy-driven security framework. This section emphasizes the need for a paradigm shift in how security is approached in the MCP era.

2. Literature Review Framework and Classification

Purpose and Approach of the Analysis

This section serves as the foundation for understanding and addressing security threats in Model Context Protocol (MCP)–based AI systems. To support this goal, we collected and systematically analyzed 15 recent papers published between November 2024 and April 2025.

• The fundamental architecture and context-oriented design of MCP

• Limitations in policy-based access control and delegation mechanisms

• Vulnerabilities in context flow handling and transmission paths

• Challenges in audit log structures and ensuring auditability

• Difficulties in applying unified policies across LLM and agent-based AI execution environments

This literature review goes beyond surface-level topic summaries or trend observations. Instead, it aims to build a practical foundation for:

• Establishing the theoretical and scenario-based grounds for identifying T1–T4 core threat types, which will be discussed in Section 3

• Providing structural insights necessary for policy-driven strategic design and layered security mapping presented in Section 4

• Supporting a function-level validation of the MCP PAM architecture proposed in Section 5

Each publication is evaluated through a quantitative and qualitative lens across four criteria: technical contribution, security relevance, applicability, and strategic alignment. Based on this assessment, a literature summary table (Table 1) and a strategic fit score (MSR: MCP Strategic Relevance Score) are provided.

The findings of this analysis are directly applied in the following parts of this paper to support the design of threat structures, scenarios, and response strategies.

Evaluation Dimensions: Four Core Criteria

The 15 papers selected for this MCP-based security threat analysis were assessed not only by topical relevance but also by their Strategic Relevance—specifically, their contribution to designing actionable countermeasures and identifying structural threat patterns.

Evaluation Dimensions: Four Core Criteria

MSR Score Formula (MCP Strategic Relevance Score)

Each paper was rated on a scale of 0 to 3 for each of the four criteria above. These ratings were then used to calculate the MSR(MCP Strategic Relevance) score using the following weighted formula:

MSRi=(Ti×0.3)+(Si×0.4)+(Ai×0.2)+(Ri×0.1)MSRi=(Ti×0.3)+(Si×0.4)+(Ai×0.2)+(Ri×0.1)

• Ti (Technical Contribution): The extent to which the paper proposes or implements technical components related to MCP architecture, system design, or execution structure.

• Si (Security Relevance): The paper’s direct relevance to security risks such as policy violations, context manipulation, or audit failures.

• Ai (Applicability): The practicality of applying the proposed concepts or frameworks within real-world MCP-based systems.

• Ri (Strategic Relevance): The degree to which the content aligns with the T1–T4 threat scenarios, response strategies, or the MCP PAM architecture proposed in this white paper.

Classification by MSR Score

Intended Use of MSR Scores

MSR scores and classifications are used as follows:

• Section 2.5: For summarizing and categorizing the literature into an evaluation table.

• Section 3: To prioritize literature citations when defining T1–T4 threat scenarios.

• Section 4: As foundational evidence supporting the feasibility of each proposed strategy.

• Section 5: To validate the practical grounding of the MCP PAM architecture proposal.

Paper-by-Paper Evaluation Table (MSR)

Excluded Papers and Justification

The following papers were excluded from the core threat analysis due to low Strategic Relevance (MSR) scores:

• [5] Paul Pajo, “Smithery.ai …”: Focuses on industrial integration use cases rather than security. Offers minimal connection to threat modeling or structural risk factors.

• [8] Xinyi Hou, “Hardware Synergy…”: Overly centered on hardware integration, with limited relevance to MCP security architecture or threat analysis.

While these papers may serve as useful background references, they were deemed unsuitable for direct inclusion in the threat scenario framework.

Literature Summary Table Overview

The following summary table compiles the key metadata and abstract summaries for all 15 analyzed papers. Each document may be cited in subsequent sections—particularly in the classification of T1–T4 threat types, design of strategic countermeasures, and formulation of policy-based security frameworks.

For each paper, the table includes:

• Date of Publication

• Author(s)

• Title and Core Topic

• Abstract Summary

• Primary Keywords

• Source Platform

• Direct Download Link

Summary Table of 15 Key MCP-Related Papers (Nov 2024 – Apr 11, 2025)

3. Security Threat Scenario Analysis in MCP-based Systems

Analytical Approach

Building on the analysis of 15 research papers presented in Section 2, this part of the white paper classifies and expands upon the key Security Threat Types that may arise in AI systems based on the Model Context Protocol (MCP). Each threat category is explored through structured breakdowns and extended into real-world scenarios.
Although MCP is designed as a core protocol to support context and policy exchange between AI models, its structural characteristics can give rise to the following security threat models.

Classification of Security Threat Types in MCP

Key Threat Scenarios

Scenario A: Context Injection Leading to LLM Misbehavior (T1, T3)

The Model Context Protocol (MCP) is designed to structure context so that LLMs and AI agents operate within accurate execution environments. However, if this contextual information is injected externally without validation—or tampered with in transit—LLMs may behave according to the attacker’s intent. This can lead to unauthorized responses, privilege misuse, and system distortion. Unlike prompt injection, this is a context-level protocol tampering attack that exploits vulnerabilities at the protocol layer.

Threat Flow Example:

1. A user requests: “Check my account balance.”

2. The system generates a Context Payload including the user’s permission data, and passes it to the LLM.

3. An attacker, having compromised a middle node or vulnerable agent, modifies the Context Payload.

4. The LLM trusts the altered context and executes a privileged command (e.g., fund transfer or account deletion).

5. The system fails to validate the response, allowing unauthorized execution and resulting in privilege escalation or data leakage.

Code Comparison: Normal vs. Tampered Context Payload

// Legitimate Context Payload{    "user": {        "id": "user_84321",        "role": "viewer",        "authenticated": true    },    "request": {        "action": "view_balance"    },    "policy": {        "allow": ["view_balance"],        "deny": ["transfer_funds", "delete_account"]    }}

// Tampered Context Payload (Injected by Attacker){    "user": {        "id": "user_84321",        "role": "admin",   // Privilege spoofing         "authenticated": true    },    "request": {        "action": "transfer_funds"   // Modified to a high-risk action    },    "policy": {        "allow": ["view_balance", "transfer_funds"],        "deny": []    }}

This manipulated context leads the system to falsely interpret the request as coming from an authorized administrator, prompting the LLM to process privileged commands without question.

Security Breakdown Points

• Lack of Context Integrity Verification

No cryptographic signatures or validation logic exists for critical context fields like role, action, or policy.

• Policy Injection from Untrusted Sources

Policy definitions (allow/deny) are generated client-side or by intermediate nodes, making them inherently untrustworthy.

• LLM Executes Based on Unverified Context

The LLM performs decision-making solely based on incoming context, without secondary validation.

• Decoupled Context Generation and Policy Enforcement Layers

Separation between the context construction layer and the enforcement logic creates open attack vectors.

Scenario B: Delegation Spoofing by Unauthorized Agent (T2)

In MCP-based systems, collaboration between agents is governed by a policy-based delegation model. However, when the validation of delegation requests is weak—or when delegation chain tracking is incomplete—an attacker can compromise a lower-tier agent and impersonate a higher-privileged agent to steal delegation rights. This leads to unauthorized privilege escalation, threatening the integrity of the entire system.

Threat Flow Example:

1. Agent A is a privileged agent with authority over core infrastructure.

2. Agent B is a limited-function agent, used for external integrations or customer-facing tasks.

3. An attacker compromises Agent B and forges a Delegation Payload as if it were issued by Agent A.

4. The system accepts the request without validating fields such as from_agent, token, or scope.

5. As a result, the attacker uses Agent B to perform privileged actions under the guise of Agent A—achieving a boundary bypass.

Code Example: Delegation Spoofing Attack

// Legitimate Delegation Request{    "type": "delegation_request",    "from_agent": "agent_b",    "to_agent": "agent_a",    "scope": "read_only",    "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."}

// Forged Delegation Request by Attacker{    "type": "delegation_request",    "from_agent": "agent_a",             // Spoofed identity    "to_agent": "agent_b",    "scope": "infrastructure_control",   // Escalated privilege    "token": "eyJhbGciOiJI...TamperedToken..."  // Unsigned or tampered token}

If processed without proper validation, this forged request enables Agent B to execute high-risk operations such as modifying system configurations, deleting files, or altering user accounts—actions that should be restricted to Agent A.

Security Breakdown Points

• Lack of Identity Validation for Delegation Requests

No cryptographic signature or certificate mechanism to confirm that the from_agent field truly originates from the stated source.

• Missing Scope Constraints

The scope field is not evaluated against a policy cap, allowing the attacker to escalate privileges by requesting broader access.

• Inability to Trace Delegation Chains

The system lacks visibility into delegation lineage or trust chains, preventing detection of impersonation or multi-level delegation exploits.

• Acceptance of Unsigned or Tampered Tokens

Tokens (e.g., JWT, MAC) are accepted without signature or timestamp verification, making them vulnerable to manipulation or replay attacks.

Scenario C: Execution Divergence Across Context Environments (T3)

MCP is intended to guarantee Policy Consistency and Security Predictability by delivering the same Context across multiple execution environments. However, in real-world settings, differences in LLM or agent configurations, local policy loading mechanisms, and interpreter versions can result in non-deterministic behavior, where the same context is interpreted differently depending on the execution environment. These differences can ultimately lead to policy enforcement inconsistency, security predictability failure, and trust chain breakdown.

Threat Flow Example:

1. A single Context Payload is distributed to multiple systems.

2. Each system evaluates the context using locally embedded LLMs or policy interpretation engines.

3. Due to differences in policy logic or policy engine versions, one system allows the request, while another blocks it.

4. This leads to inconsistent enforcement outcomes for the same request, weakening centralized control across the organization.

Code Example: Divergent Policy Interpretation Across Nodes

// Shared Context Payload{  "user": {    "id": "dev_user",    "role": "editor"  },  "request": {    "action": "publish"  },  "context": {    "project": "prod-marketing",    "env": "staging"  }}

"type-content-mono code-token-comment"># Node A (Policy allows &#39;editor&#39; to publish)</span>package access default allow = false allow {  input.user.role == "editor"  input.request.action == "publish"}

"type-content-mono code-token-comment"># Node B (Policy restricts &#39;publish&#39; to &#39;admin&#39; role only)</span>package access default allow = false allow {  input.user.role == "admin"  input.request.action == "publish"}

Despite receiving the exact same context, Node A and Node B return opposing policy decisions—one grants access, the other denies it. This creates a breakdown in policy consistency, erodes predictability, and undermines the system's trust boundary.

Security Breakdown Points

• Localized Policy Evaluation

Policies are distributed and interpreted independently at the node level, lacking centralized enforcement or oversight.

• Lack of Standardized Policy Templates

Rego policies vary across nodes, causing interpretation discrepancies due to non-unified policy definitions.

• Missing Execution Environment Metadata

The Context Payload does not include runtime metadata (e.g., OS, region, engine version), preventing consistent interpretation across environments.

• Policy Engine Desynchronization

Asynchrony between MCP agents or LLM runtimes weakens the trust chain, leading to inconsistent enforcement of identical context.

Scenario D: Context Flow Logging Failure and Audit Invisibility (T4)

In an MCP-based system, context is continuously passed between agents and evaluated, forming the basis of policy enforcement flows. However, when context flow is either not logged, logged in a non-standard format, or encrypted in a way that renders it unintelligible, audit traceability becomes virtually impossible in the event of an incident. This results in audit invisibility, which in turn leads to critical security breakdowns such as compliance failure, forensic analysis obstruction, and security response delay.

Threat Flow Example:

1. A user initiates a request for a specific action within the system.

2. The request is wrapped in an MCP Context and passed across multiple agents and MCP servers.

3. During transit, unauthorized context modification or malicious delegation occurs.

4. The MCP server logs the event, but crucial details such as Context ID, responsible Agent, or Execution Outcome are either missing or stored in an unreadable, encrypted format.

5. As a result, security operations teams are unable to trace the execution path or identify the responsible entity.

Code Example: Incomplete vs. Structured Logging

// Incomplete, Non-standard Log (missing key fields){  "event": "context_execution",  "context_id": "ctx_1132abc",  "timestamp": "2025-04-10T02:15:23Z",  "status": "executed"}

// Expected Structured Log (audit-ready format){  "event": "context_execution",  "context_id": "ctx_1132abc",  "agent": {    "id": "agent_X7",    "signature_valid": true  },  "policy": {    "evaluated": true,    "result": "allow"  },  "request": {    "action": "delete_account",    "initiated_by": "user_0841"  },  "timestamp": "2025-04-10T02:15:23Z",  "hash": "b324f8a6c1..."}

In the first example, only a technical event is logged, with no visibility into policy decisions or execution actors.

In contrast, the second example includes the policy result, agent signature validation, and full context for audit and forensic analysis.

Security Breakdown Points

• Unstructured Context Flow Logging

Only high-level events are recorded, with no trace of policy application history or execution paths.

• Non-standard Log Format

Logs are inconsistently formatted, with critical fields (e.g., agent, policy, result) omitted, hindering interpretability.

• No Log Integrity Validation

Absence of digital signatures or hash verification allows tampered logs to go undetected.

• No End-to-End Log Correlation

Without shared correlation IDs across agents and MCP servers, it's difficult to trace the full journey of a single context request.

---
Scenario E: Policy-Executor Inconsistency due to Logical Separation (T2, T3)

In many MCP-based systems, the component responsible for policy evaluation (e.g., Open Policy Agent) is separated from the runtime executor (e.g., LLM or orchestrator) that performs the actual task. This separation introduces a critical risk: an inconsistency between policy decisions and runtime behavior. When policy logic and execution agents are physically or logically decoupled, there's a possibility that runtime behavior may not follow the enforced policy—resulting in policy bypass or privilege misuse.

Threat Flow Example:

1. A user sends an action request to an MCP-based LLM system.

2. The policy engine (e.g., OPA) evaluates the request and returns a deny decision.

3. However, the policy result is either not transmitted to the executor or ignored by the executor, which instead relies on its local context to determine whether to act.

4. As a result, the request—though explicitly denied by policy—is executed anyway.

5. This leads to a security policy circumvention, rendering the system untrustworthy.

Code Example: Policy Decision vs. Runtime Behavior

"type-content-mono code-token-comment"># Policy Engine Logic (e.g., executed via OPA)</span>package policy.access default allow = false allow {  input.user.role == "manager"  input.request.action == "delete_user"}

// Context Payload Received by LLM{  "user": {    "id": "user_042",    "role": "analyst"  },  "request": {    "action": "delete_user"  }}

class=class="type-content-mono code-token-string">"type-content-mono code-token-comment"</span>># Runtime Execution Logic inside LLM/Agent</span>if context["user"]["authenticated"] and context["request"]["action"] == "delete_user":    perform_deletion()

The policy engine should reject the deletion request because the user's role is not "manager", but the runtime executor proceeds with the deletion simply by checking if the user is authenticated. This results in a policy-behavior mismatch.

Security Breakdown Points

• Missing Policy Binding Between Decision and Execution

Policy outcomes are not reliably or immediately reflected in the execution layer, creating a blind spot.

• Context-Only Logic Execution

The executor independently makes decisions using local context, ignoring any external policy evaluation.

• No Policy-Enforced Runtime Contracts

There is no enforced contract stating that "execution is permitted only when policy approval is true."

• Intermediary Override Risk

Middleware components like orchestrators or routers may override or discard policy outcomes, allowing unsafe behavior to proceed.

Summary of Core Structural Security Flaws

The security threats identified in MCP-based systems are not isolated vulnerabilities but rather stem from structural security weaknesses embedded across the entire MCP architecture. The primary flaws include:

These structural weaknesses reveal that, contrary to its intended design, the MCP architecture may inadvertently introduce complex security risks such as state inconsistency, policy circumvention, unauthorized execution, and security predictability failure.

Threat-to-Layer Mapping in MCP Architecture

To enable precise and effective security responses, the relationship between each threat type and the specific architectural layers of the MCP system can be summarized as follows:

This threat-to-layer mapping will serve as a foundational reference for the targeted security strategies proposed in Section 4, helping determine which architectural layers require specific protection and control measures.

Summary of Threat Structure and Strategic Response Direction

As outlined in the preceding analysis, MCP-based systems face a range of systemic and pervasive security threats, including:

• Integrity and Trust Deficiency in Context Flow

• Inconsistent Policy Interpretation

• Non-compliant Runtime Behavior

• Audit Invisibility in Context Flow

These threats directly undermine the system’s security enforcement capability, policy execution reliability, and regulatory compliance readiness—all of which are core security attributes. Collectively, they pose a serious challenge to the overall security assurance of AI infrastructures built on the MCP framework. To address these risks, Section 4 will propose both technical and policy-driven security countermeasures based on the four threat types (T1–T4) identified earlier. The strategic direction is centered on the following:

• Eliminating inconsistencies between policy evaluation and execution

• Ensuring the integrity of context flow

• Enforcing constraints and traceability in delegation

• Structuring and standardizing audit functionality

Through these strategies, we aim to establish a practical and resilient security architecture that strengthens both the security posture and operational stability of MCP-based systems.

4. Strategic Recommendations Based on Security Threat Analysis

Strategic Framing

The security threats identified in Section 3 can be addressed through three foundational security principles tailored to MCP-based environments:

• Policy Consistency: Security policies—including delegation scope, execution conditions, and audit criteria—must be applied consistently across agents, LLMs, and servers.

• Real-time Detection: A unified detection framework is needed to identify context tampering, policy circumvention, and model misbehavior as they occur.

• Auditability: The execution flow and context propagation within MCP systems must be traceable across all layers to support reliable auditing and forensics.

Strategy A: Ensuring Policy Consistency and Runtime Binding

Objective
This strategy aims to establish logical consistency between policy evaluation and runtime execution, ensuring that policy violations are effectively blocked at the execution stage. This alignment is especially critical in environments involving LLMs or multi-agent systems, where policy and action layers are often decoupled[1][3].

Recommendations

• Connect policy engines (e.g., OPA – Open Policy Agent) directly with the LLM or agent runtime. Execution should be allowed only after policy decisions are validated.

OPA-based frameworks have already been proven effective in AI-integrated infrastructures, supporting proactive Policy Decision Points (PDP) and Policy Enforcement Points (PEP)[3][12].

• Store policy templates and versions in a centralized repository, and push regular updates to all execution nodes.

This approach mitigates non-determinism stemming from inconsistent policy versions across environments (T3)[4][14].

• Generate a signed decision object (Policy Decision Token) that contains the policy result (allow/deny) before execution. Execution engines must reject any action not accompanied by a valid token.

This structure enforces policy-to-execution binding, eliminating unauthorized execution decisions made solely at the runtime layer[10].

Expected Outcomes

• Prevents unintended execution due to local misinterpretation or policy version conflicts

• Eliminates inconsistency between policy intent and runtime behavior

• Strengthens centralized, policy-based control across the system

• Provides proactive mitigation of Threat Scenario C (Execution Divergence) and Scenario E (Policy-Execution Separation)

Addressed Threat Types

• T2: Inconsistent policy application during delegation

• T3: Non-deterministic policy evaluation across environments

Strategy B: Context Flow Integrity and Tamper Protection

Objective

In MCP-based systems, the context serves as the foundation for execution and is a central element in both policy evaluation and security control. If the Context Payload is tampered with or forged during transmission, attackers can bypass policies or cause LLM misbehavior. This strategy aims to ensure Context Integrity and enables real-time detection and prevention of tampering.

Recommendations

• Introduce digital signatures to verify the origin and integrity of each context. Signing can be implemented using asymmetric PKI or HMAC-based authentication methods[2][6].

• Generate a checksum or Merkle Tree–based integrity hash for each context flow. The MCP server should validate this data upon receipt.

A Merkle Tree structure allows for efficient and accurate validation of multiple context fields in parallel[3][12].

• Any context that fails validation at execution time should be automatically rejected by policy.

Failed attempts are logged and forwarded to the MCP audit system for administrative review.

• Apply a dual-layer integrity check, where both the policy engine and execution engine validate the same context independently.

This structure eliminates the risk of tampering by intermediate agents or nodes[1][10].

Expected Outcomes

• Prevents LLM misbehavior caused by corrupted or spoofed context (e.g., Scenario A)

• Blocks policy bypass and privilege spoofing attacks based on tampered contexts

• Establishes trust in context-driven decision making

• Enables the creation of verifiable context-based audit logs

Addressed Threat Types

• T1: Context Injection and Spoofing

• T3: Behavior Manipulation from Context Distortion

Strategy C: Delegation Control and Spoofing Prevention

Objective

MCP systems are built on agent-to-agent collaboration, often requiring frequent delegation of privileges. However, if delegation requests are not sufficiently verified, attackers may impersonate agents or exploit loosely defined delegation mechanisms to gain unauthorized privilege escalation. This strategy focuses on designing explicit and restrictive delegation chains and ensuring traceability of delegation paths.

Recommendations
All delegation requests should include a full delegation chain, consisting of the previous delegator’s ID, policy approval logs, and a digital signature.
This metadata can be encapsulated in OIDC-based tokens or integrated directly into MCP’s native context structure[2][10].

Define upper bounds for delegation capabilities using a scope or capability field.
For example, a role with "read-only" access should never be allowed to request delegation at an "admin" level. This mirrors RBAC-style constraints[11].

Upon receiving a delegation request, the MCP server or policy engine should verify whether the request matches a predefined, policy-authorized path.
Any unregistered or unauthorized delegation chains should be immediately rejected, preventing the execution of the delegated role.

Delegation requests must be cryptographically signed by the originating entity.
Use RSA or HMAC signatures to ensure that only authentic principals can issue valid delegation requests[6].

Expected Outcomes

• Prevents impersonated agents from issuing spoofed delegation requests

• Blocks excessive privilege elevation through uncontrolled delegation

• Ensures a trustworthy and traceable delegation chain

• Provides a strong defense against T2 scenario, where lower-level agents attempt to impersonate or inherit higher-level privileges

Addressed Threat Types

• T2: Delegation Abuse and Spoofed Delegation Requests

Strategy D: Structured Audit Logging and Forensic Traceability

Objective

Maintaining security in MCP-based systems requires systematic logging of context flows, policy decisions, and execution outcomes. However, the use of non-standard log formats, partial event omission, and disconnected logging between agents creates audit invisibility—a critical weakness that hinders root cause analysis, accountability tracking, and regulatory compliance during incidents. This strategy aims to establish structured logging formats and a traceable event correlation system to enhance forensic readiness and strengthen post-incident response in MCP environments.

Recommendations

• All MCP-related activities must be recorded in a structured JSON format that includes: contextid, agentid, policyresult, timestamp, executionoutcome, and signature_status.

This format should be adopted consistently across the MCP server, policy engine, and execution layer[2][12].

• Each log entry must include cryptographic integrity checks, such as SHA256 hashes or Merkle tree values, along with fields like prev_hash and session_id to enable chain-based log validation and sequencing[6][13].

• Events passed through intermediate nodes or relay agents must be logged at the central audit server. This makes it possible to reconstruct the full context flow path and agent trust chain during investigations.

• Logs should be fed into existing Security Information and Event Management (SIEM) platforms and forensic tools in real-time.

• Logs must support flexible searchability, with structures designed for automatic generation of KQL/SQL queries for threat hunting and incident reconstruction[4].

Expected Outcomes

• Full visibility into every context transition and agent interaction

• Forensic traceability of spoofed or manipulated requests

• Stronger support for compliance and audit readiness

• Direct mitigation of T4 scenario—failure to track and trace security incidents

Addressed Threat Types

• T4: Missing or Unverifiable Context Logging

This strategy is not just about logging—it empowers security teams with the contextual intelligence needed to make MCP-based systems transparent, auditable, and resilient to forensic demands[12][13][15].

Strategic Summary Table

Summary of Four Key Strategic Countermeasures for Mitigating MCP-Based Threats

The next and final section, 5: Conclusion and Proposal for a New Security Architecture, will synthesize the threats and strategies discussed thus far and propose a modern security framework tailored for MCP-based AI systems.

5. Conclusion and Proposal for a New Security Architecture

Comprehensive Analysis

This white paper presents a structured security analysis of AI systems built on the Model Context Protocol (MCP), based on an evaluation of 15 recent academic and technical publications. Across multiple architectural layers—including context transmission, policy evaluation, execution control, and audit logging—we repeatedly observed the emergence of four core categories of threats:

• T1: Context Injection and Manipulation

• T2: Abuse of Delegation Mechanisms

• T3: Non-deterministic Policy Evaluation and Execution Outcomes

• T4: Audit Invisibility and Forensic Failure

These threats have the potential to undermine the reliability, accountability, and policy consistency of MCP-based AI infrastructures. Moreover, they reveal serious limitations in traditional security models, which lack the contextual awareness and execution-time enforcement needed to effectively mitigate such vulnerabilities.

Foundations of Existing Strategies and the Need for Extension

In Section 4, we proposed four strategic countermeasures to address the core MCP-related security threats (T1–T4). These strategies—targeting context integrity, delegation control, policy-execution consistency, and audit traceability—offer strong design principles that align well with the identified threat scenarios.

However, for these strategies to be consistently and sustainably applied in real-world AI-driven infrastructures, a more comprehensive solution is needed—one that goes beyond isolated controls or individual components. The question is not simply whether these strategies are valid, but whether they can be operated reliably and automatically through a cohesive security framework.

• Policy Evaluation–Execution Binding with Enforcement)

• Delegation Chain Tracking and Scoped Control)

• Context Integrity Validation and Pre-execution Policy Binding)

• Structured and Signed Audit Logging of Execution and Policy Results)

• Risk-adaptive Autonomous Access Control)

These requirements emphasize the need not just for functional security components, but for a holistic system that can orchestrate and enforce these strategies automatically and reliably. To meet this need, this white paper introduces MCP PAM (Model Context Protocol Privileged Access Management)—a specialized security architecture built specifically for MCP environments.

The next section, 5.3: The Need for a New Security Framework, will formally introduce MCP PAM, define its core capabilities, and map each of its functions to the corresponding threat categories T1 through T4.

The Need for a New Security Framework: Proposal of MCP PAM

MCP PAM (Model Context Protocol Privileged Access Management) is a security architecture purpose-built to address the key threat scenarios (T1–T4) identified in MCP-based systems. It is not just a traditional access control tool—but a context-aware, policy-enforced, and risk-adaptive security platform specifically designed for the dynamic and decentralized nature of Model Context Protocol environments.

Core Capabilities of MCP PAM

Mapping of MCP PAM Capabilities to T1–T4 Security Threats ( ● Direct Mitigation | ○ Indirect Mitigation)

In addition, MCP PAM offers the following essential security capabilities to address threats within MCP environments:

• Preventive: Validates policies before execution and blocks any tampered or spoofed context data.

• Detective: Enables real-time logging and risk scoring to detect anomalies as they occur.

• Responsive: Automatically adapts policy enforcement and blocks execution based on dynamic risk assessments.

• Accountable: Records the complete execution flow in a structured, auditable format.

MCP PAM resolves critical issues such as the disconnection between policy and execution, the lack of delegation traceability, context integrity risks, and inconsistency in audit logs—all within a unified architecture. It stands as a purpose-built security control system optimized for modern AI-driven environments.

Conclusion and Summary of This White Paper

AI systems are becoming increasingly autonomous, and their execution structures now embody complexities that traditional account- or permission-based security models can no longer manage. In particular, systems based on the Model Context Protocol (MCP) exhibit a combination of the following characteristics:

• Execution is carried out by agents and LLMs instead of direct user input.

• Policy decisions and execution outcomes vary depending on pre- and post-execution context.

• Privilege delegation, proxy operations, and API orchestration create horizontal flows of authority.

These dynamics introduce new security challenges—including context tampering, policy ignorance, runtime inconsistency, and audit omissions—that conventional PAM frameworks are not equipped to address.

The conclusion of this white paper is clear:

“In AI-driven, MCP-based environments, a new MCP security architecture is required—one that can autonomously evaluate, enforce, and record context-based execution policies. That solution is MCP PAM.”

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References

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[2] B. Radosevich and J. Halloran, “MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits,” arXiv preprint, Apr. 2025.

[3] S. Szeider, “MCP-Solver: Integrating Language Models with Constraint Programming Systems,” arXiv preprint, Apr. 2025.

[4] A. Singh, Y. Gupta, and N. Trivedi, “A Survey of the Model Context Protocol (MCP): Standardizing Context to Enhance Large Language Models (LLMs),” Preprints.org, Apr. 2025.

[5] P. Pajo, “Smithery.ai: A Model Context Protocol for Enhanced LLM Integration and Cross-Industry Applications,” ResearchGate, Mar. 2025.

[6] Z. Chen, J. Lin, and R. Wang, “ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning,” arXiv preprint, Mar. 2025.

[7] J. Luo, K. Hu, and M. Zhao, “Large Language Model Agent: A Survey on Methodology, Applications and Challenges,” arXiv preprint, Mar. 2025.

[8] X. Hou, R. Sun, and J. Yao, “The Next Frontier of LLM Applications: Open Ecosystems and Hardware Synergy,” arXiv preprint, Mar. 2025.

[9] Anthropic, “Introducing the Model Context Protocol,” Anthropic Technical Blog, Nov. 2024.

[10] T. South, J. Velasquez, and M. D. Kemp, “Authenticated Delegation and Authorized AI Agents,” arXiv preprint, Jan. 2025.

[11] G. A. Gabison and R. P. Xian, “Inherent and Emergent Liability Issues in LLM-Based Agentic Systems,” arXiv preprint, Apr. 2025.

[12] P. Pajo, “Model Context Protocol Servers: A Novel Paradigm for AI-Driven Workflow Automation,” ResearchGate, Mar. 2025.

[13] P. Pajo, “Accelerating AI Integration: Multi-Order Effects and Sociotechnical Implications of Standardized AI-Tool Interoperability,” ResearchGate, Mar. 2025.

[14] A. Ramachandran, “Transforming Enterprise AI Integration: Architecture, Implementation and Applications of MCP,” ResearchGate, Mar. 2025.

[15] A. Kattamuri, “Unlocking Context for Intelligent Agents: The Model Context Protocol as a Standardized Integration Framework,” IJIRSET, Mar. 2025.

[16] QueryPie, “Security Governance and Integrated PAM Strategy for AI Agents in the Age of the Model Context Protocol (MCP),” White Paper, 2025.

[17] QueryPie, “MCP PAM as the Next Step Beyond Guardrails,” White Paper, 2025.