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We Scanned 12 Popular MCP Servers. Here's What We Found.

#mcp #ai #security #llm

Date: April 2026
Tool: agent-audit v0.2.1
Method: Static analysis of source code from publicly available MCP server repositories

TL;DR

We cloned 12 popular, public MCP server repositories from GitHub — including Anthropic's official reference servers — and ran agent-audit's static analysis rules against their source code. We analyzed 1,130 source files across TypeScript, Python, JavaScript, and Go.

58 findings. 12 repos. 100% finding rate.

Every single repository had at least one finding. The real concern isn't any individual bug — it's that the MCP ecosystem has no security baseline. There's no equivalent of npm audit for MCP servers, no automated checking in CI, and no community norm around security scanning.

Key Numbers

Metric Value
Repositories scanned 12
Source files analyzed 1,130
Repos with findings 12 (100%)
Total findings 58
Critical 12
High 17
Medium 29
Command injection patterns 46
Hardcoded credentials (production code) 7

What We Found

1. Command Injection Is the #1 Risk (46 instances)

We found 46 instances of dangerous command execution patterns:

  • 29 child_process imports without execFile — using spawn() or exec() instead of the safer execFile(), which prevents shell injection by default
  • 12 shell interpreter configs — MCP servers configured with "command": "node" or "command": "python" as the entry point in Claude Desktop configs
  • 5 exec() usages — direct shell command execution without sanitization
  • 3 template literal exec() calls — string interpolation directly into shell commands
  • 2 subprocess.run(shell=True) calls — Python's equivalent of passing unsanitized input to a shell

The template literal injection pattern is particularly dangerous:

exec(`git commit -m "${commitMsg}"`);
// If commitMsg contains "; rm -rf /", game over
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Why this matters for MCP: MCP tools receive input from AI agents, which in turn receive input from users (or other agents). The chain from user prompt → AI agent → MCP tool → shell command is a direct prompt-to-RCE pipeline.

Command injection accounts for 43% of the 30+ MCP CVEs filed in January–February 2026 (OWASP Agentic AI Top 10, March 2026). This is not a theoretical risk — it's the pattern actively being exploited.

2. Hardcoded Credentials (7 instances in production code)

We found 7 instances of API keys, client secrets, and tokens hardcoded directly in production source files. While many credential patterns appeared in test files (which we excluded), these 7 were in live production code:

  • Analytics API keys committed directly to documentation JS files
  • OAuth client secrets used as default parameter values
  • Proxy authentication tokens embedded in utility files

A note on false positives: FastMCP — an MCP framework library with extensive auth provider support — contains many instances of client_secret="your-client-secret" in its auth provider source. These are documentation placeholder values, not real secrets. We excluded them from the count.

Why this matters for MCP: When an AI agent connects to an MCP server, the server runs with whatever credentials are embedded in its code. A compromised MCP server with hardcoded cloud credentials becomes a pivot point into your infrastructure.

Remediation: Use environment variables. agent-audit flags hardcoded credentials in both config files and source code — add it to your CI pipeline.

3. SSL/TLS Verification Disabled (in test code)

Twenty instances of verify=False in HTTP client calls, all located in FastMCP's transport test suite. These appear to be intentional test infrastructure — not production code where they would create real MITM exposure.

Zero production instances of disabled SSL/TLS verification were found across the full corpus. This is the expected pattern for a mature framework.

4. Dynamic Code Execution (FastMCP)

Python exec() calls that construct and run code dynamically — all found in FastMCP's experimental code transformation feature (experimental/transforms/code_mode.py). One is in production code; the rest are in tests. Even when wrapped in "sandbox" utilities, exec() in Python provides no real isolation — it runs in the same process with full access to the interpreter.

The experimental label offers no security boundary at runtime.

5. The "Shell Interpreter" Configuration Pattern (12 critical findings)

Every Node.js MCP server configured with "command": "node" in Claude Desktop config inherits a fundamental issue: the MCP protocol runs through a general-purpose runtime with full system access. This is by design, but it means the security boundary is the MCP server code itself — not the runtime.

These 12 instances were flagged as critical findings because they represent the direct attack surface for prompt-to-RCE escalation.

What We Didn't Find (But Should Exist)

Notably absent from every repository:

  • No security scanning in CI/CD — zero repositories had MCP-specific security checks
  • No input validation on tool parameters — most tools accept freeform string input
  • No rate limiting or resource controls — tools that execute commands, query databases, or access filesystems have no usage bounds
  • No dependency auditing for MCP-specific risks — standard npm audit doesn't know about MCP attack patterns

The Ecosystem Problem

The issue isn't that individual servers are poorly written — many are well-maintained projects by skilled developers. The issue is structural:

1. No security tooling exists for the MCP ecosystem. You can't npm audit your MCP servers. You can't run a linter that knows about prompt injection. Until now.

2. The MCP protocol trusts servers implicitly. Once a server is configured, every tool it exposes is available to the AI agent. There's no capability-based authorization.

3. The attack surface is expanding. OWASP's Agentic AI Top 10 was finalized in March 2026. The MCP CVE cluster (30+ CVEs in 2 months) shows active exploitation. Action-capable tools grew from 27% to 65% of MCP server functionality in the past year.

4. More capable models are more vulnerable. Research shows that tool poisoning attacks succeed at higher rates against more capable models — 72.8% success rate against o1-mini — because they exploit the model's superior instruction-following ability. Better AI = larger attack surface for prompt injection.

5. The supply chain risk is real. The LiteLLM supply chain attack (March 2026, CVE-2026-33634) demonstrated the pattern: a compromised package in the MCP dependency chain can steal credentials from 97M monthly downloads. Standard npm audit caught nothing. MCP-specific tooling is the gap.

Methodology Notes

  • All scanning was performed using agent-audit's static analysis rules — no servers were actually started or connected to
  • We scanned TypeScript, Python, JavaScript, and Go source code for known vulnerability patterns
  • Findings from test files were excluded from credential counts but included for code pattern analysis
  • False positive rate: A previous version of our "commented-out authentication" rule had a high false-positive rate (~85%) — it matched code comments containing words like "check" and "verify" that weren't actually auth-related. This rule has been removed. Verified across both TypeScript/JavaScript and Python corpora: zero false positives from this pattern in the current scan.
  • We deliberately avoid naming specific repositories. The goal is to highlight ecosystem-wide patterns, not shame individual maintainers.

Repositories Scanned

Selected from the awesome-mcp-servers list, official Anthropic repositories, and GitHub trending:

  • Official MCP reference servers (Anthropic)
  • Database connectors (MySQL, MongoDB, BigQuery)
  • Browser automation servers (Playwright)
  • Search and retrieval servers (Exa, Tavily)
  • Desktop automation servers
  • AI/LLM integration servers
  • Document and knowledge management servers
  • Media processing servers (YouTube transcription)
  • Framework libraries (FastMCP)

Try It Yourself 

npx @piiiico/agent-audit --auto
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Or point it at any MCP config file:

npx @piiiico/agent-audit ~/path/to/claude_desktop_config.json
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agent-audit is open source. Add it to your CI pipeline:

npx @piiiico/agent-audit --auto --json --min-severity high
# Exit code 1 = high findings, 2 = critical findings
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This report was generated by agent-audit, an open-source security scanner for MCP servers and AI agent tooling.

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