Artificial intelligence now powers critical enterprise workflows, and with the rapid adoption of the Model Context Protocol (MCP), organizations are introducing new automation layers, new trust boundaries, and, along with it, their attack surfaces are expanding. The OWASP MCP Top 10 reveals the most important risks emerging from this AI-driven ecosystem, providing security leaders with a structured way to understand where AI systems can be exploited. As MCP continues to influence how agents interact with tools and data, these MCP vulnerabilities become essential to address for anyone responsible for safeguarding enterprises from AI security risks.

The OWASP MCP Top 10: Key MCP Vulnerabilities and Their Impact

Model context protocol introduces dynamic interactions between AI agents, tools, and servers. These interactions behave differently from traditional software components, which means the risks can spread faster, manifest more unpredictably, and lead to consequences that extend far beyond a single misconfiguration.

01 – Token Mismanagement & Secret Exposure

Token mismanagement and secret exposure refer to the exposure of hard-coded credentials, long-lived tokens, and secrets stored in model memory or protocol logs. Because AI agents often process or hold these values temporarily, attackers may retrieve sensitive credentials through prompt manipulation, command injection, debugging traces, or misconfigured server endpoints. The MCP vulnerability only becomes more sensitive based on how workflows intermingle sensitive secrets with operational context, and without strict handling controls, this information becomes unintentionally accessible.

Impact:
Exposure compromises authentication boundaries across connected systems. Cyber adversaries may impersonate internal tools, access privileged APIs, escalate to system-wide compromise, or pivot deeper into infrastructure using exposed credentials. At scale, this significantly amplifies AI security risks by enabling a single leaked secret to cascade across interconnected AI-driven workflows and systems.

02 – Privilege Escalation & Scope Creep

The major AI security risks arise when Model Control Protocol servers grant agents temporary or loosely defined permissions that exceed their intended scope. Over time, access privileges escalate, allowing attackers to perform unauthorized actions like executing system commands, modifying files, or accessing restricted data.

Impact:
Once privilege escalates, attackers can assume greater control than intended, leading to unauthorized system modifications, data exfiltration, or persistent access. This creates long-term MCP vulnerabilities, particularly when automated agents retain expanded permissions.

03 – Tool Poisoning

Tool poisoning occurs when adversaries compromise tools, plugins, or their outputs by injecting malicious, misleading, or biased context that alters an AI model’s behavior and answers. Because MCP agents rely on tools as trusted sources of information or execution, a poisoned tool can create an MCP vulnerability where it distorts workflows, corrupts decisions, or introduces harmful behaviors.

Impact:
A single poisoned tool can create a chain of reaction across AI-powered workflows, producing unreliable outputs, facilitating data tampering, or creating invisible pathways for command injection and lateral manipulation. Over time, this increases AI security risks considerably by undermining trust in automated decision-making and allowing manipulated context to grow unchecked across interconnected systems.

04 – Software Supply Chain Attacks & Dependency Tampering

This MCP vulnerability occurs when compromised dependencies, libraries, runtimes, plugins, or external services alter agent behavior or introduce execution-level backdoors. Model Context Protocol ecosystems depend heavily on external dependencies, and any tampering within that chain directly affects the model’s ability to act safely.

Impact:
Unmonitored MCP vulnerability introduced via supply chain risks can modify agent logic, introduce malicious automation, exfiltrate data, or provide remote access to attackers. Because dependencies travel across multiple AI systems, the impact of these security risks scales quickly and silently unless discovered early in the process. 

05 – Command Injection & Unsafe Execution

Command injection becomes possible when agents construct or execute system commands, shell scripts, API calls, or code snippets using uncontrolled malicious inputs, whether from prompts, tool outputs, or third-party integrations. In MCP architectures, where models act as interpreters, attackers can manipulate inputs that lead to unintended command execution.

Impact:
Successful command injection gives attackers direct execution capability inside the environment. This may result in file manipulation, remote command execution, data deletion, infrastructure compromise, or takeover of downstream systems.

06 – Prompt Injection via Contextual Payloads

Prompts injection via contextual payloads expands classic prompt injection by exploiting contextual payloads that become part of the model’s memory or interpreted content. Because models follow natural-language instructions, adversaries can embed harmful directives into files, images (via OCR), or metadata that later influence agent behavior unexpectedly.

Impact:
Contextual prompt injection allows attackers to manipulate AI workflows, alter outputs, and trigger unauthorized actions without direct access to the MCP server. These attacks are stealthy, persistent, and often difficult to detect, resulting in higher AI security risks. When successful, they can significantly alter the AI model’s behaviour, outputs, and accuracy. 

07 – Insufficient Authentication & Authorization

Inadequate authentication and authorization occur when MCP tools or agents fail to enforce identity validation across interacting systems. This MCP vulnerability manifests through weak or missing authentication, a lack of identity validation for tools, and improper access controls between agents or MCP servers.

Impact:
Weak authentication frameworks allow unauthorized agents or external actors to access internal tools, execute commands, or retrieve sensitive data. This exposure enables impersonation attacks, unauthorized execution, and privileged misuse, significantly increasing AI security risks by allowing malicious actors to operate within AI-powered systems by impersonating trusted identities.

08 – Lack of Audits & Telemetry

Limited telemetry from Model Context Protocol systems makes investigation and monitoring extremely difficult. Without logs of tool invocations, context changes, or agent actions, security teams lack visibility into how MCP workflows behave under normal or malicious conditions, creating confusion during audit times.

Impact:
Without meaningful observability, suspicious behavior blends into normal MCP activity, allowing threats to persist unnoticed. This visibility gap makes it difficult for security teams to detect anomalies, reconstruct incidents, or understand how an attack unfolded. Adversaries can exploit the absence of telemetry to operate quietly, maintain durable access, and expand their foothold without triggering alerts or leaving a reliable forensic trail.

09 – Shadow MCP Servers

Shadow MCP servers emerge when development teams deploy unauthorized or unsupervised instances of MCP in cloud environments, sandboxes, or experimental systems. These installations bypass formal security governance and often run with permissive defaults.

Impact:
Shadow MCP servers introduce significant blind spots because they operate outside formal security governance. Without proper visibility or oversight, these instances often stay hidden with weak security controls or misconfiguration. Attackers actively scan for such overlooked systems and routinely find them long before internal teams do. Once discovered, these ungoverned endpoints offer direct access into AI workflows, creating exploitable pathways that bypass monitoring, hardening, and established security controls.

10 – Context Leakage & Over-Sharing

In MCP workflows, “context” refers to the accumulated memory, prompts, intermediate outputs, and operational data that AI agents carry across tasks or interactions. When this context is not tightly scoped, properly isolated, or efficiently expired, it can bleed into subsequent requests or sessions. This oversharing allows sensitive information, such as credentials, internal logic, proprietary datasets, or workflow details, to surface unintentionally in places it was never meant to appear.

Impact:
Context leakage exposes confidential data and operational intelligence that attackers can readily weaponize. Beyond creating compliance and privacy concerns, this oversharing gives adversaries detailed insight into how systems behave, enabling more precise manipulation, tailored exploits, and escalated compromise across MCP-driven environments.

How RiskProfiler Addresses External MCP Vulnerability with Agentic Threat Intelligence

Many of the most critical MCP vulnerabilities become exploitable when they are exposed externally, through servers, leaked secrets, compromised dependencies, or unmonitored installations that attackers can discover before the organization does. RiskProfiler focuses precisely on these internet-facing MCP exposures. Through continuous external attack surface monitoring and agentic threat intelligence, the platform identifies MCP vulnerability and subsequent AI security risks that inadvertently leak credentials or configuration data, uncovers suspicious or compromised third-party components influencing agent behavior, and reveals unauthenticated or unknown MCP servers deployed across fragmented cloud environments. As organizations continue to integrate AI into their workflows rapidly, RiskProfiler restores visibility by surfacing and mapping every externally accessible AI endpoint and correlating it with real threat actor reconnaissance patterns.

By discovering these exposures early, RiskProfiler enables enterprises to proactively eliminate the MCP vulnerabilities that attackers would exploit first. Instead of reacting to downstream AI security risks, security teams gain the ability to control their AI footprint at the perimeter, before an adversary gains the upper hand.

Conclusion

The OWASP MCP Top 10 underscores a new chapter in cybersecurity, one shaped by AI autonomy, distributed orchestration, and expanding external attack surfaces. Organizations adopting Model Context Protocol must remain vigilant, ensuring that both internal and external exposures are continuously monitored and addressed. As AI becomes foundational to business operations, securing MCP-enabled environments becomes essential to maintaining trust and resilience.

RiskProfiler equips enterprises with the threat intelligence needed to stay ahead of external AI security risks and MCP vulnerabilities.

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