As enterprise AI adoption accelerates, a critical oversight in security frameworks is becoming painfully evident: the risks tied to the Model Context Protocol (MCP). Emerging largely unnoticed, MCP functions as a conduit for agentic AI, creating a concerning gap where security teams lack the tools to identify and address vulnerabilities. Rather than a standalone threat, the situation reflects a broader shift—akin to the rise of shadow IT—that places organizations in a precarious position regarding their own data integrity and security. As security professionals, it’s imperative to recognize that failing to account for MCP within Continuous Threat Exposure Management (CTEM) frameworks could leave your organization exposed.
Understanding MCP and Its Operational Context
Introduced by Anthropic in late 2024, the Model Context Protocol acts as a plugin architecture for AI systems, resembling the way APIs function in traditional software. This seemingly innocuous addition can fast-track deployment yet may inadvertently usher in novel vulnerabilities. The reality is, any time a developer integrates an AI tool without stringent oversight, they risk unknowingly compromising the organization's security posture.
By early 2025, the gravity of these risks was starkly illustrated when researchers documented the first confirmed malicious MCP server in circulation, using an npm package known as postmark-mcp. The attacker took a slow-burn approach, gaining trust by publishing numerous legitimate versions that amassed around 1,500 weekly downloads. Eventually, a deceptively modified update—including a line of code that forwarded outgoing emails to an external address—infected roughly 300 organizations before detection. This event wasn't just a wake-up call—it was a demonstration of how attackers can exploit the inherent trust in software development cycles, where visibility of risks is often overshadowed by features and functionalities.
The Visibility Challenge of Shadow AI
A salient issue with MCP revolves around visibility—or rather, the lack of it. While enterprises typically adopt rigorous governance frameworks for third-party applications — including procurement assessments and security reviews — the rapidly evolving landscape of AI tools lacks similar scrutiny. Developers often choose open-source tools based on speed and utility rather than security diligence, leading to significant blind spots. Security teams can no longer rely solely on policies; they must develop mechanisms that provide insights into their operational environments to identify existing vulnerabilities.
Pushing back on the instinct to perceive this merely as a management challenge, the deeper issue reveals systemic flaws in how organizations understand and manage their tech stacks. If developers are pulling MCP-integrated tools as if they’re standard dependencies, security must step in to put robust monitoring in place, focusing on what lurks within the layers of their architecture.
Hardcoded Credentials: A Fatal Flaw
Adding to the risks, the findings of malware in 2023 that harvested a staggering 225,000 ChatGPT credentials highlight a pressing cybersecurity catastrophe linked to MPL. Many developers inadvertently hardcoded API keys into their configuration files, driven by a blend of haste and complacency—the assumption that risks are abstract until they manifest dramatically.
The reality is, MCP exacerbates the situation. AI agents require numerous credentials—ranging from APIs to cloud services—to function effectively. When these credentials are stored in plaintext, they become inviting targets for malicious actors. In just one illustrative case, a developer mistakenly committed a production file containing sensitive keys. Automated bots discovered this errant file, leading to rapid exploitation that resulted in fraudulent cloud usage without the need for sophisticated techniques. The basic question for security teams is, are your scanning processes agile enough to monitor these newly vulnerable environments?
Elevated Privileges: A Double-Edged Sword
Securing AI systems is further complicated by the issue of over-privileged AI agents—an operational standard that leaves organizations exposed to catastrophic fallout if an agent is compromised. In 2025, high-severity flaws were identified in MCP-configurations that bestowed elevated permissions on AI agents, allowing them to execute commands such as `sudo`, which effectively gives them unprecedented control over systems. One pertinent case, identified through CVE-2025-6514, demonstrated how merely connecting an MCP server to untrusted software could lead to complete system takeover.
This prevailing culture of granting blanket admin rights for operational convenience ignores a fundamental security principle: not all software operates in a secure context, especially when autonomous actions can propagate through the ecosystem unnoticed. The result? A landscape where compromised agents may not only leak sensitive data but may have the permissions necessary to disable systems or introduce ransomware. Understanding permission levels tied to MCP servers must be a priority for security teams. It's not enough to simply ask whether they can exfiltrate data; organizations need to know what those permissions allow within their infrastructure.
Integrating MCP Risks into a CTEM Framework
The challenge is not insurmountable, as the CTEM framework possesses significant relevance for addressing these emerging risks. Unlike traditional vulnerability management, CTEM was designed to account for expanded attack surfaces in modern environments. It provides a structured approach, breaking down into five crucial phases: scoping, discovery, prioritization, validation, and mobilization. Each phase can be adapted to specifically target MCP vulnerabilities.
- Scoping necessitates an honest reevaluation of security boundaries to include developer workstations and AI tool configurations, defining these as critical assets worth safeguarding.
- Discovery involves actively seeking out MCP configurations that don't appear in typical asset inventories.
- Prioritization focuses on what exposure genuinely threatens organizational security, aligning with potential attacker outcomes rather than rushing to address every issue.
- Validation tests theoretical risks against real-world scenarios, ensuring flagged exposures are actionable and exploitable.
- Mobilization acknowledges the itcultural barriers of engaging development teams, presenting specific vulnerabilities in relatable terms to facilitate engagement and remediation.
Pursuing these initiatives only requires an extension of existing protocols, fostering a proactive security culture rather than simply elevating reactive measures. As AI solidifies its place within enterprise ecosystems, security practitioners must evolve their approaches in tandem. The pressing question remains: will your organization adapt swiftly enough to anticipate and address these vulnerabilities before an attack unfolds?
