The Hidden Risks of the Model Context Protocol (MCP)

As AI systems become more powerful and context-aware, the Model Context Protocol (MCP) is emerging as a game-changer — allowing models to connect seamlessly with tools, APIs, and real-time data. From automating workflows to personalizing user experiences, MCP promises a new era of intelligent integration. But with this new capability comes a new class of risks — many of which are quietly overlooked. Behind the convenience of smarter AI lies a growing attack surface that spans prompt injection, token theft, supply chain compromises, and governance blind spots. While the industry races to unlock the full potential of AI agents, it’s equally urgent to understand what we might be exposing in the process.

Technical Vulnerabilities

• Prompt Injection: Maliciously crafted inputs can trick the AI into executing unintended commands or leaking data (The Security Risks of Model Context Protocol (MCP)). For example, a seemingly normal user message or email might hide instructions that cause the AI to perform unauthorized actions (e.g. forwarding confidential files to an attacker) (The Security Risks of Model Context Protocol (MCP)). Such prompt-based exploits blur the line between harmless content and executable commands, making them a stealthy threat to MCP-integrated systems.
• Data Poisoning: Compromised or malicious data fed into the model’s context or training can corrupt its outputs and behavior (Understanding the Security Implications of Using MCP | by Sebastian Buzdugan | Mar, 2025 | Medium). Attackers might inject false or biased information into the AI’s long-term context or training datasets, causing the model to embed misinformation or backdoor triggers in its responses (Understanding the Security Implications of Using MCP | by Sebastian Buzdugan | Mar, 2025 | Medium) (LLM04:2025 Data and Model Poisoning - OWASP Top 10 for LLM & Generative AI Security). Over time this “poisoned” context degrades performance or causes unexpected, harmful outputs, undermining the integrity of MCP-driven decisions.
• Model Impersonation: Adversaries may spoof AI system identities or roles to mislead users and systems. This can happen at the prompt level (attackers posing as system administrators or higher-level AIs to override policies) or via supply chain attacks. Notably, fake libraries impersonating ChatGPT and Claude were published on PyPI, tricking developers and delivering malware under the guise of official AI model integrations (PyPI Attack: ChatGPT, Claude Impersonators Deliver JarkaStealer via Python Libraries). Such impersonation exploits erode trust in MCP connectors and can lead to credential theft or code execution under false pretenses.

Security & Privacy Risks

• Token Theft (Account Takeover): MCP servers often store authentication tokens for various services. If an attacker steals one of these tokens (e.g. via a compromised MCP server or leaked credentials), they gain full access to that linked account (The Security Risks of Model Context Protocol (MCP)). In one scenario, a stolen OAuth token for Gmail let attackers read entire email histories, send emails as the victim, and even silently set up forwarders for espionage (The Security Risks of Model Context Protocol (MCP)). Worse, using a token via MCP may not trigger traditional security alerts (it looks like normal API usage) (The Security Risks of Model Context Protocol (MCP)) – making detection of the breach much harder.
• Excessive Permissions: MCP integrations often request very broad access rights to systems, far beyond the minimum needed (The Security Risks of Model Context Protocol (MCP)). This “all-access pass” approach (e.g. full read/write to a database or mailbox instead of read-only) means that if the AI or MCP server is compromised, an attacker essentially gets keys to the kingdom (The Security Risks of Model Context Protocol (MCP)). A breached MCP server could leverage its overly expansive permissions to access emails, files, databases, and more across an organization with a single exploit (The Security Risks of Model Context Protocol (MCP)). This lack of least privilege multiplies the damage from any single token or account compromise.
• Data Aggregation (Single-Point Exposure): By design, MCP bridges many data sources, which creates a honeypot of aggregated information. All your disparate data (emails, chat logs, drive files, calendars, etc.) can be pulled together in one context – and if that central conduit is breached, everything leaks at once (The Security Risks of Model Context Protocol (MCP)). Attackers able to infiltrate an MCP pipeline could correlate data from different services to uncover sensitive patterns (The Security Risks of Model Context Protocol (MCP)). For instance, combining a user’s calendar entries with their emails and documents could enable highly targeted spear-phishing or extortion campaigns (The Security Risks of Model Context Protocol (MCP)). (Even legitimate MCP service providers might be tempted to mine this trove of cross-platform data for user profiling (The Security Risks of Model Context Protocol (MCP)), raising serious privacy concerns.)
• Context/Session Leaks: (Related to aggregation) Long-lived MCP sessions that retain extensive context can unintentionally expose private data. Without careful scoping, an AI might carry over sensitive info from one query to the next. There have already been incidents of AI agents inadvertently leaking private snippets of code and data when handling rich contexts (How to mitigate the security risks of Anthropic's Model Context Protocol). This risk is heightened in MCP setups if session data isn’t properly cleared or isolated between tasks.

Governance & Compliance Challenges

• Lack of Access Control: Today’s MCP implementations lack robust role-based access controls and granular permission settings. Connectors often default to overly broad scopes (Privacy in Model Control Protocol (MCP): Risks and Protections), making it difficult to restrict an AI agent to only the data or actions appropriate for its task. Fine-grained access (e.g. per-user or per-file permissions) is not always enforced, meaning an AI might access data that a particular user wouldn’t ordinarily be allowed to see. This gap in least-privilege enforcement could violate internal data governance policies (Privacy in Model Control Protocol (MCP): Risks and Protections) and amplify the impact of any compromise.
• Poor Auditability: It is hard to monitor and audit AI-driven actions through MCP with the current tooling. Many MCP servers/connectors are community-developed and deployed ad-hoc, without a centralized auditing framework or rigorous security review ([2503.23278] Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions). This decentralization means inconsistent logging practices – some actions might not be logged at all, and those that are logged could be scattered across systems. Even when logs exist, they may contain sensitive details (queries, file paths, user IDs) that create new privacy liabilities if not protected (Privacy in Model Control Protocol (MCP): Risks and Protections). The net result is limited oversight: unusual or unauthorized usage of MCP may go unnoticed due to insufficient monitoring and anomaly detection (a need the industry has already highlighted) (The Security Risks of Model Context Protocol (MCP)).
• Regulatory Exposure: Using MCP to connect and process personal or sensitive data can quickly lead to compliance violations if not done carefully. Organizations must ensure MCP-driven AI services adhere to data protection laws like GDPR and HIPAA (Privacy in Model Control Protocol (MCP): Risks and Protections) – for example, by limiting data use to stated purposes, obtaining user consent, and honoring data deletion requests (Privacy in Model Control Protocol (MCP): Risks and Protections). Without such controls, an MCP integration might inadvertently break data minimization or retention rules. Moreover, emerging AI-specific regulations are raising the stakes: the EU AI Act, for instance, will impose hefty fines for AI systems that mishandle data or lack transparency (). In a recent C-suite survey, 85% of executives voiced concern about AI-related litigation and regulatory action (). The broad access and opaque decision-making of MCP-powered AI could put organizations squarely in the crosshairs of regulators if misuse leads to privacy breaches, bias, or other harms (i.e. significant legal and reputational risks).

As MCP adoption grows, technical leaders and CISOs should proactively address these issues – implementing least‑privilege access, rigorous auditing, and compliance checks – to safely harness MCP’s benefits without exposing their organizations to hidden threats.