Borrowing it
Nothing to install: this file belongs to lewing/helix.mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/lewing/helix.mcp/main/.copilot/skills/mcp-threat-modeling/SKILL.mdgit clone --depth 1 https://github.com/lewing/helix.mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lewing/helix.mcp/mcp-threat-modeling)<a href="https://agentmods.dev/skills/lewing/helix.mcp/mcp-threat-modeling"><img src="https://agentmods.dev/badge/skills/lewing/helix.mcp/mcp-threat-modeling/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lewing/helix.mcp/mcp-threat-modeling"><img src="https://agentmods.dev/badge/skills/lewing/helix.mcp/mcp-threat-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.00850 |
| Opus 5 | $0.00008 | $0.00425 |
| Sonnet 5 | $0.00003 | $0.00170 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
mcp-threat-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
MCP (Model Context Protocol) servers have a distinctive threat model compared to typical APIs. They are invoked by AI agents which may be prompt-injected, run in two transport modes (stdio and HTTP) with very different trust boundaries, and often handle CI/infrastructure data that may contain secrets.
Patterns
MCP-Specific Trust Boundaries
Always identify these trust boundaries for any MCP server:
- MCP Client → Server — Stdio is process-local (trusted). HTTP is network-accessible (untrusted). Treat these as fundamentally different security postures.
- AI Agent → MCP Tool Parameters — Parameters come from an AI agent that may be prompt-injected. Treat all MCP tool inputs as untrusted, even in stdio mode.
- Server → External APIs — Outbound API calls use credentials. SSRF risk if the server fetches arbitrary URLs based on agent input.
- Server → Local Filesystem — Downloads and caches write to disk. Path components derived from external data require sanitization.
Stdio vs HTTP Security Divergence
Stdio MCP servers inherit the host process's security context — authentication is implicit (the user who launched the process). HTTP MCP servers are network services that need explicit authentication. A common mistake is building a tool as stdio-first, then adding HTTP transport without adding auth middleware.
AI Agent as Untrusted Input Source
MCP tool parameters originate from an AI agent, not directly from a human. An agent can be prompt-injected to:
- Pass malicious URLs to download tools (SSRF)
- Request unbounded batch operations (DoS)
- Construct path-traversal payloads in file/work item names Always validate and sanitize MCP tool inputs as if they were user input from the internet.
Cache Security for MCP Servers
MCP stdio servers are often ephemeral processes. Cross-process caches (SQLite, file-based) persist data beyond session lifetime. Key concerns:
- Auth context isolation — different tokens should get separate cache namespaces
- Cached data may contain secrets (CI logs, build artifacts)
- Cache location should be user-profile-scoped (not world-readable)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 64 lines · 15 tokens per session scan A 93ee3f5a9f40
mcp-threat-modeling is a skill published in the GitHub repository lewing/helix.mcp (4 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 850 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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