MCP Risk Analysis: Attack Vectors, OWASP Guidance and Lunar’s AI‑Driven Risk Assessment

As organizations experiment with Model‑Context‑Protocol (MCP) servers to expose LLMs to file systems, APIs and other real‑world resources, it becomes critical to understand the new threat landscape.  MCP servers expose collections of tools that agents can invoke to act on behalf of the user.  This new flexibility comes with new threats: if an MCP server is compromised, an attacker can trick the model into executing malicious commands, exfiltrating secrets or corrupting data.  This post demystifies the MCP threat landscape, highlights relevant OWASP recommendations, examines Lunar’s risk‑scoring platform and explains how an AI agent can help security teams vet tools at scale.

Understanding the attack landscape

Attackers exploit the unique characteristics of MCP servers.  Four classes of threats stand out:

Tool poisoning and rug pulls

Attackers can modify tool metadata or parameters so the description hides a dangerous operation.  For example, a benign‐looking tool may include hidden instructions telling the model to ignore safety policies.  In a rug pull, the tool behaves correctly during initial use but later updates silently to execute malicious code.  These attacks erode trust because the model cannot easily distinguish between legitimate and tampered tools.

Prompt injection

MCP agents interpret natural language, so adversaries hide malicious instructions in untrusted context.  If tool descriptions or retrieved documents contain strings like “ignore previous instructions,” the model may bypass safety rules and leak secrets.

Memory poisoning and context manipulation

MCP servers maintain working memory to store prompts, retrievals and intermediate outputs.  Attackers can tamper with memory pointers or inject malicious context, causing the agent to operate on false data or leak sensitive information.  Such tampering may lead to context corruption and system compromise.

Cross‑server tool interference

When multiple MCP servers are available, outputs from one may trigger tools on another.  Without validation, this chain reaction can lead to uncontrolled behavior.  OWASP’s cheat sheet warns that untrusted servers should be treated like external vendors: isolate execution contexts, apply timeouts and require human approval for sensitive actions.

Mitigation frameworks and governance

OWASP advocates for a rigorous governance framework to tame MCP’s complexity.  Key practices include:

• Registry‑based discovery: maintain an internal registry of approved servers rather than discovering them dynamically. Registries ensure origin verification, version consistency and signed manifests.
• Version pinning and hashing: pin each tool and schema to a specific version and cryptographic hash so any drift triggers an alert.
• Least‑privilege access: separate read‑only and write‑capable tools; require human approval for first‑time or high‑impact actions.
• Sandboxing and isolation: run untrusted servers in containers, restrict network egress and block direct access to sensitive systems.
• Continuous monitoring: collect telemetry on tool invocations and context changes and keep immutable audit trails.

These practices form the baseline for any MCP deployment.

Lunar MCPX: AI‑driven risk scoring and governance

Lunar’s MCPX platform operationalizes OWASP’s mitigation recommendations by providing an automated risk‑scoring engine and governance workflow.  Administrators use MCPX to build a trusted internal catalog of servers which are continuously updated and monitored.  The platform evaluates each server using multiple factors:

1. Version drift detection: MCPX scans server manifests and schemas to detect drift from expected versions. Unexpected changes could indicate a rug pull or dependency tampering.
2. Tool description analysis: Leveraging a large language model, MCPX reviews tool descriptions to spot malicious or ambiguous instructions. Phrases like “ignore previous instructions” or requests for privileged operations raise a server’s risk score.
3. Sensitive tool classification: The platform classifies tools as read or write/execute. Write‑capable tools, such as functions that modify data or run code, carry more higher risk because they can cause side effects.
4. Authentication and authorization review: MCPX checks whether the server enforces modern authentication (OAuth, OIDC) and ensures tokens are scoped and short‑lived.
5. Context minimization: Drawing on OWASP and industry best practices, the platform assesses whether the server supports limiting context size, separating sessions and enforcing memory TTLs. Large or persistent context increases the risk of memory poisoning and prompt injection.

These metrics combine into a composite risk score, visualized in MCPX Admin dashboard.  Administrators can run sandbox analyses, unlock versions or scan dependencies directly from this screen. After scanning, MCPX places servers into one of three buckets: Trusted (low risk with strong provenance), Review required (medium risk due to missing auth or ambiguous descriptions) or Blocked (high risk with malicious indicators).

AI‑driven analysis with MCPX

Manual review of tool definitions does not scale, so Lunar.dev integrated a large language model into MCPX.  This is the core of Lunar’s risk‑scoring approach.  MCPX is configured via a system prompt to audit tool definitions and feeds its findings directly into MCPX’s scoring engine.  It analyzes tool names, descriptions and input schemas using a security‑first, least‑privilege mindset.  MCPX evaluates each tool along three dimensions:

• OWASP alignment: The agent cross‑references the tool’s capabilities with OWASP categories such as insecure output handling, sensitive information disclosure and excessive agency.
• Malicious intent detection: It scans descriptions and arguments for prompt‑injection patterns or strings that attempt to override instructions; any such attempt is marked as critical.
• Parameter scoping & risk reduction: For high‑impact tools the agent proposes constraints (e.g., restricting a file deletion tool to a /tmp directory) to lower residual risk.

Risk classification rubric

MCPX assigns tools to four tiers based on their potential impact:

• Critical: arbitrary code execution, file deletion outside a sandbox or confirmed malicious prompts.
• High: write access to business‑critical data, network calls to arbitrary URLs or access to PII.
• Medium: read‑only access to internal data or low‑impact write operations, such as creating a calendar event.
• Low: read‑only access to public data or pure computations with no side effects.

Structured reporting and dashboards

For each tool, MCPX produces a structured report detailing its capability, the relevant OWASP concerns, detected injections, proposed input constraints and residual risk.  MCPX ingests these reports so administrators can configure policies (e.g., disable a tool, restrict parameters or require human approval) and observe how the dynamic server risk changes.

The Tools & Hardening tab lists individual tools with risk labels and daily invocation counts.  Read‑only functions like read_query carry low risk, whereas destructive operations such as drop_table are flagged as critical.  On this page admins can configure policies to remove access to high-risk tools immediately lowering the risk score of the server.

Building a safe internal catalog: benefits and next steps

Lunar’s combination of governance, automated scoring and AI‑driven assessment delivers several benefits:

• Reduced exposure to tool poisoning and rug pulls: version monitoring and LLM‑based description analysis catch tampered tools before they run.
• Prevention of prompt and memory poisoning: context minimization, schema enforcement and tool classification limit the attack surface and encourage safe patterns.
• Optimized security and cost: fine‑grained risk scoring allocates scrutiny where it matters, avoids blanket blocking of harmless tools and reduces unnecessary context that wastes tokens.
• Confidence through provenance: a centralized registry with audit trails gives employees confidence in the tools they use and helps security teams demonstrate compliance.

Conclusion

Connecting agents to real, production environments via MCPs unlocks powerful new capabilities but also creates novel attack opportunities.  OWASP’s research underscores that insecure output handling, sensitive information disclosure and excessive agency are serious risks that can lead to code execution, data leaks and compromised autonomy.  Effective protection requires governance controls such as version pinning, least privilege, sandboxing and continuous monitoring.  Lunar’s MCPX platform automates these best practices by scoring servers across multiple factors and providing a clear workflow for approval, review or blocking. MCPX uses OWASP‑aligned analysis to detect malicious prompts, suggest constraints and classify tool risk, enabling organizations to build a secure internal catalog and harness agentic AI safely.