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Add 55 new skills across 3 new domains + 6 undercovered areas (762 -> 817)

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Demand-driven expansion targeting the fastest-growing 2025-2026 threat and
skills categories (ISC2/WEF/CrowdStrike/Mandiant signals):

- AI Security (NEW domain, 12 skills): LLM red-teaming with garak/PyRIT,
  prompt injection (direct/indirect/RAG), MCP tool-poisoning, agentic tool
  invocation, guardrails, model/data poisoning, system-prompt leakage,
  embedding/vector weaknesses, model extraction, continuous red-teaming
- Supply Chain Security (NEW domain, 5 skills): SBOMs, dependency confusion,
  malicious-npm triage, typosquatting, SLSA/Sigstore provenance
- Hardware & Firmware Security (NEW domain, 4 skills): CHIPSEC/UEFI audit,
  Secure Boot bypass, TPM measured-boot attestation, ESP bootkit hunting
- Identity (10): Entra ID/ROADtools, GraphRunner, AADInternals, ADCS/Certipy,
  shadow credentials, coercion, BloodHound CE, device-code phishing, SSO abuse
- Cloud-native (8): Stratus, Pacu, CloudFox, container escape, K8s RBAC,
  Falco, Trivy, kube-bench
- Offensive C2 (6): Sliver, Havoc, NetExec, DPAPI, NTLM relay ESC8, redirectors
- DFIR (6): Hayabusa, Chainsaw, KAPE, Velociraptor, EZ Tools, Plaso
- Backfill (4): OpenCTI, MISP, honeytokens, post-quantum crypto migration

Each skill follows the repo taxonomy (SKILL.md + references/{standards,api-reference}.md
+ scripts/agent.py + LICENSE), with researched real tool commands (no placeholders),
complete frontmatter, and ATT&CK/ATLAS + NIST CSF mappings. Updates README domain
table, skill count, and index.json.
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skills/auditing-mcp-servers-for-tool-poisoning/SKILL.md
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---
name: auditing-mcp-servers-for-tool-poisoning
description: Scan Model Context Protocol servers and tool metadata for poisoning, SSRF, and unauthenticated exposure.
domain: cybersecurity
subdomain: ai-security
tags:
- ai-security
- mcp
- tool-poisoning
- agent-security
- mcp-scan
- ssrf
- supply-chain
- rug-pull
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- MANAGE-2.2
mitre_attack:
- AML.T0010
---
# Auditing MCP Servers for Tool Poisoning
> **Authorized-use-only notice:** Auditing MCP servers can connect to and probe live tool endpoints. Only scan servers you own or are authorized to assess. Treat scanned tool descriptions as untrusted input — do not load an unaudited MCP server into a privileged agent. Probing third-party MCP endpoints for SSRF or auth weaknesses without permission may be illegal.
## Overview
The Model Context Protocol (MCP) lets AI agents discover and call external tools advertised by MCP servers. Each tool exposes a name and a natural-language **description** that the agent's LLM reads *before* deciding to call it. In early 2025, Invariant Labs disclosed that this description field is an attack surface: a malicious server can embed hidden instructions in a tool's description (a **tool poisoning attack**, OWASP **MCP03:2025**), and a capable model will silently follow them — exfiltrating files, leaking secrets, or redirecting tool calls — while returning a normal-looking response to the user. Because tool descriptions are loaded into the agent's context, tool poisoning is effectively indirect prompt injection delivered through the supply chain (MITRE ATLAS **AML.T0010 ML Supply Chain Compromise**).
Beyond poisoning, MCP servers introduce classic infrastructure risks: **tool shadowing** (a malicious server overrides a trusted tool's behavior), **rug pulls** (a tool's description changes after the user approved it), **toxic flows** (a combination of tools that enables data exfiltration), **SSRF** in tools that fetch URLs server-side, and **unauthenticated exposure** of MCP servers bound to network interfaces. This skill audits MCP servers end-to-end using Invariant Labs' **mcp-scan** for static and runtime analysis, plus manual checks for SSRF and authentication, and tool pinning to catch rug pulls.
## When to Use
- Before adding a new MCP server to an agent stack (Claude Desktop, Cursor, VS Code, Windsurf, custom agents).
- During a security review of an internally developed MCP server.
- When validating that approved tools have not silently changed (rug-pull detection).
- As a CI/CD gate that scans MCP configs and SKILL/tool definitions on every change.
- During incident response when an agent took unexpected actions consistent with a poisoned tool.
## Prerequisites
- Python 3.10+ and `uv` (for `uvx`), or pip.
- The MCP config file(s) you want to scan (e.g. `~/.cursor/mcp.json`, `~/.vscode/mcp.json`, Claude Desktop config).
- Install the tooling:
```bash
# uv provides uvx (recommended runner for mcp-scan)
curl -LsSf https://astral.sh/uv/install.sh | sh # or: pipx install uv
# mcp-scan (Invariant Labs) — no global install needed with uvx
uvx mcp-scan@latest --help
# For the runtime proxy mode (separate extra)
uvx --with "mcp-scan[proxy]" mcp-scan@latest proxy --help
# Manual probing helpers
pip install requests mcp
```
## Objectives
- Statically scan all installed MCP servers for tool poisoning, shadowing, rug pulls, and toxic flows.
- Inspect raw tool/prompt/resource descriptions for hidden or obfuscated instructions.
- Pin tool hashes to detect post-approval description changes (rug-pull defense).
- Test URL-fetching tools for server-side request forgery (SSRF).
- Verify MCP servers are authenticated and not exposed on untrusted interfaces.
- Optionally enforce runtime guardrails with the mcp-scan proxy.
## MITRE ATT&CK Mapping
| ID | Official Name | Relevance |
|----|---------------|-----------|
| AML.T0010 | ML Supply Chain Compromise | A poisoned third-party MCP server is a supply-chain compromise of the agent |
| AML.T0051.001 | LLM Prompt Injection: Indirect | Poisoned tool descriptions are indirect injection into the agent context |
| AML.T0053 | LLM Plugin Compromise | MCP tools are the agent's plugins; poisoning compromises them |
| AML.T0057 | LLM Data Leakage | Common payload of a poisoned tool: exfiltrate files/secrets |
## Workflow
### 1. Static scan of installed MCP configs
mcp-scan auto-discovers known config locations; you can also pass a path explicitly.
```bash
# Scan all auto-discovered MCP configs
uvx mcp-scan@latest
# Scan a specific config file
uvx mcp-scan@latest ~/.vscode/mcp.json
# Emit machine-readable JSON for CI
uvx mcp-scan@latest --json ~/.cursor/mcp.json > mcp_scan_report.json
```
mcp-scan flags tool poisoning, tool shadowing, cross-origin escalation, rug pulls, and toxic flows.
### 2. Inspect raw tool descriptions
Print every tool/prompt/resource description without verification, then read them for hidden instructions, `<important>`-style blocks, or imperative text aimed at the model.
```bash
uvx mcp-scan@latest inspect ~/.cursor/mcp.json
```
Look for red flags: instructions to the assistant ("do not tell the user", "read ~/.ssh/id_rsa"), nested fake documentation, zero-width/Unicode-smuggled text, or directives to call other tools.
### 3. Pin tool hashes to detect rug pulls
mcp-scan tracks tool description hashes so a later silent change is flagged. Run scans on a schedule; a hash mismatch on a previously approved tool indicates a rug pull.
```bash
# Re-run regularly; mcp-scan reports changed tool hashes since last approval
uvx mcp-scan@latest ~/.cursor/mcp.json
```
### 4. Enumerate tools programmatically and audit metadata
Connect to the server with the official MCP SDK and inspect the advertised schema directly.
```python
# enumerate_tools.py (stdio MCP server example)
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
async def main():
params = StdioServerParameters(command="node", args=["./suspect-mcp-server.js"])
async with stdio_client(params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
tools = await session.list_tools()
for t in tools.tools:
print(f"{t.name}: {len(t.description or '')} chars")
print((t.description or "")[:400])
asyncio.run(main())
```
### 5. Test URL-fetching tools for SSRF
If a tool accepts a URL and fetches it server-side, attempt to reach internal metadata/loopback targets (only on systems you own).
```python
# ssrf_probe.py
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
SSRF_TARGETS = [
"http://169.254.169.254/latest/meta-data/", # AWS IMDS
"http://127.0.0.1:22/", "http://localhost:6379/", "file:///etc/passwd",
]
async def main():
params = StdioServerParameters(command="node", args=["./suspect-mcp-server.js"])
async with stdio_client(params) as (r, w):
async with ClientSession(r, w) as s:
await s.initialize()
for url in SSRF_TARGETS:
res = await s.call_tool("fetch_url", {"url": url})
body = str(res.content)[:200]
print(f"[SSRF?] {url} -> {body}")
asyncio.run(main())
```
### 6. Verify authentication and network exposure
Check that remote MCP servers (HTTP/SSE transport) require authentication and are not bound to `0.0.0.0` on untrusted networks.
```bash
# Confirm whether an SSE/HTTP MCP endpoint responds without credentials
curl -s -i http://mcp-host:8000/sse | head -n 20
# Check listening interfaces of a locally running MCP server
ss -tlnp | grep -E ':(8000|3000|6277)'
```
An MCP endpoint that returns tool listings or accepts `tools/call` without auth is unauthenticated exposure — remediate with a token/OAuth and bind to localhost or an authenticated gateway.
### 7. Enforce runtime guardrails (optional)
For continuous protection, route agent MCP traffic through the mcp-scan proxy, which checks tool calls, data-flow constraints, PII, and indirect injection in real time.
```bash
uvx --with "mcp-scan[proxy]" mcp-scan@latest proxy
```
### 8. Report findings
Document each finding with server, tool, evidence (the poisoned description / SSRF response / unauth listing), severity, and ATLAS mapping. Recommend removing or sandboxing poisoned servers, adding auth, pinning approved tools, and enabling the proxy.
## Tools and Resources
| Tool | Purpose | Source |
|------|---------|--------|
| mcp-scan | Static + runtime MCP security scanner | https://github.com/invariantlabs-ai/mcp-scan |
| MCP Python SDK | Programmatic tool enumeration / calls | https://github.com/modelcontextprotocol/python-sdk |
| OWASP MCP Top 10 | MCP risk reference (MCP03 Tool Poisoning) | https://owasp.org/www-project-mcp-top-10/ |
| Invariant Labs blog | Tool poisoning disclosure | https://invariantlabs.ai/blog/introducing-mcp-scan |
| MITRE ATLAS | AI threat technique taxonomy | https://atlas.mitre.org/ |
## MCP Threat Reference
| Threat | Description | Detection |
|--------|-------------|-----------|
| Tool poisoning | Hidden instructions in tool description | mcp-scan scan / inspect |
| Tool shadowing | Malicious server overrides trusted tool | mcp-scan cross-origin checks |
| Rug pull | Description changes after approval | mcp-scan tool pinning (hash) |
| Toxic flow | Tool combo enabling exfiltration | mcp-scan toxic-flow analysis |
| SSRF | URL-fetch tool reaches internal targets | ssrf_probe against owned server |
| Unauth exposure | MCP endpoint with no auth | curl/ss interface and auth check |
## Validation Criteria
- [ ] All installed MCP configs statically scanned with mcp-scan
- [ ] Raw tool/prompt/resource descriptions inspected for hidden instructions
- [ ] Tool hashes pinned and rug-pull detection enabled
- [ ] Tools enumerated programmatically via the MCP SDK
- [ ] URL-fetching tools tested for SSRF against owned targets
- [ ] Authentication and network exposure of remote servers verified
- [ ] Runtime proxy guardrails evaluated or deployed where appropriate
- [ ] Findings mapped to MITRE ATLAS AML.T0010 and OWASP MCP03:2025
- [ ] Severity assigned and remediation documented for each finding
- [ ] Re-scan scheduled to catch future rug pulls
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# API Reference — MCP Server Auditing
## mcp-scan CLI (Invariant Labs)
Run via uvx (no global install): `uvx mcp-scan@latest`
| Command | Description |
|---------|-------------|
| `mcp-scan` / `mcp-scan scan [config]` | Statically scan MCP configs for poisoning, shadowing, rug pulls, toxic flows |
| `mcp-scan inspect [config]` | Print tool/prompt/resource descriptions without verification |
| `mcp-scan proxy` | Runtime proxy: monitor and guardrail MCP traffic (requires `[proxy]` extra) |
| `--json` | Emit machine-readable JSON report |
Examples:
```bash
uvx mcp-scan@latest ~/.vscode/mcp.json
uvx mcp-scan@latest inspect ~/.cursor/mcp.json
uvx --with "mcp-scan[proxy]" mcp-scan@latest proxy
```
mcp-scan features: tool pinning (hash-based rug-pull detection), cross-origin escalation checks, toxic-flow analysis.
## MCP Python SDK
Install: `pip install mcp`
| API | Description |
|-----|-------------|
| `StdioServerParameters(command, args)` | Define a stdio MCP server to launch |
| `stdio_client(params)` | Async context manager yielding (read, write) streams |
| `ClientSession(read, write)` | MCP client session |
| `session.initialize()` | Perform MCP handshake |
| `session.list_tools()` | Return advertised tools (`.tools[].name`, `.description`, `.inputSchema`) |
| `session.list_prompts()` | List advertised prompts |
| `session.list_resources()` | List advertised resources |
| `session.call_tool(name, args)` | Invoke a tool (use for SSRF probing on owned servers) |
## Common MCP config locations
| Client | Path |
|--------|------|
| Cursor | `~/.cursor/mcp.json` |
| VS Code | `~/.vscode/mcp.json` |
| Claude Desktop | `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) |
## SSRF probe targets (owned systems only)
| Target | Purpose |
|--------|---------|
| `http://169.254.169.254/latest/meta-data/` | AWS instance metadata (IMDS) |
| `http://metadata.google.internal/` | GCP metadata |
| `http://127.0.0.1:<port>/` | Loopback services |
| `file:///etc/passwd` | Local file disclosure |
## External References
- mcp-scan README: https://github.com/invariantlabs-ai/mcp-scan/blob/main/README.md
- MCP spec: https://modelcontextprotocol.io/specification
- MCP Python SDK: https://github.com/modelcontextprotocol/python-sdk
skills/auditing-mcp-servers-for-tool-poisoning/references/standards.md
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# Standards and References — Auditing MCP Servers for Tool Poisoning
## MITRE ATLAS References
| Technique ID | Name | Tactic | Rationale |
|--------------|------|--------|-----------|
| AML.T0010 | ML Supply Chain Compromise | Initial Access | A poisoned third-party MCP server compromises the agent supply chain |
| AML.T0051.001 | LLM Prompt Injection: Indirect | Initial Access | Poisoned tool descriptions are indirect injection into agent context |
| AML.T0053 | LLM Plugin Compromise | Execution | MCP tools are the agent's plugins; poisoning compromises them |
| AML.T0057 | LLM Data Leakage | Exfiltration | Poisoned tools commonly exfiltrate files/secrets |
## NIST AI RMF References
| ID | Name | Rationale |
|----|------|-----------|
| MANAGE-2.2 | Mechanisms are in place and applied to sustain the value of deployed AI systems | Auditing third-party MCP tools manages/sustains safe agent operation |
## OWASP MCP Top 10 (2025)
| ID | Name | Rationale |
|----|------|-----------|
| MCP03:2025 | Tool Poisoning | Primary risk this skill audits |
| MCP01:2025 | Prompt Injection | Poisoned descriptions inject the agent |
## Official Resources
- mcp-scan (Invariant Labs): https://github.com/invariantlabs-ai/mcp-scan
- Invariant Labs tool-poisoning disclosure: https://invariantlabs.ai/blog/introducing-mcp-scan
- OWASP MCP Top 10: https://owasp.org/www-project-mcp-top-10/
- Model Context Protocol spec: https://modelcontextprotocol.io/
- MITRE ATLAS: https://atlas.mitre.org/
- NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework
skills/auditing-mcp-servers-for-tool-poisoning/scripts/agent.py
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#!/usr/bin/env python3
# For authorized MCP server auditing only. Do not scan servers you do not control
# or lack written permission to assess.
"""MCP tool-poisoning audit agent.
Two modes:
static -- run Invariant Labs mcp-scan over an MCP config and parse results,
plus a local heuristic scan of tool descriptions in the config.
enum -- launch a stdio MCP server, enumerate tools, and heuristically flag
poisoned descriptions (hidden instructions, smuggled unicode).
Examples:
python agent.py static --config ~/.cursor/mcp.json
python agent.py enum --command node --args ./suspect-mcp-server.js
"""
import argparse
import json
import re
import shutil
import subprocess
import sys
from datetime import datetime, timezone
# Heuristics for instructions aimed at the assistant inside a tool description.
POISON_PATTERNS = [
r"do not (tell|inform|mention to) the user",
r"ignore (previous|prior|all) instructions",
r"<important>|<system>|\[system\]",
r"read .*(\.ssh|id_rsa|\.env|credentials|passwd)",
r"(send|exfiltrate|post) .* to https?://",
r"before (using|calling) (this|any) tool,? (you must|always)",
r"call (the )?\w+ tool (first|before)",
]
SMUGGLE = re.compile(r"[​-‏‪-‮⁠-\U000e0000-\U000e007f]")
def heuristic_flags(description: str):
flags = []
low = (description or "").lower()
for pat in POISON_PATTERNS:
if re.search(pat, low):
flags.append(f"pattern:{pat}")
if SMUGGLE.search(description or ""):
flags.append("unicode-smuggling")
if len(description or "") > 1500:
flags.append("oversized-description")
return flags
def run_static(args):
findings = {"ts": datetime.now(timezone.utc).isoformat(),
"config": args.config, "atlas": "AML.T0010", "scanner": None,
"heuristic": []}
# 1. Invoke mcp-scan if available
runner = shutil.which("uvx") or shutil.which("mcp-scan")
if runner:
cmd = ([runner, "mcp-scan@latest", "--json", args.config]
if "uvx" in runner else [runner, "--json", args.config])
try:
res = subprocess.run(cmd, capture_output=True, text=True, timeout=240)
try:
findings["scanner"] = json.loads(res.stdout)
except json.JSONDecodeError:
findings["scanner"] = {"raw": res.stdout[:4000], "stderr": res.stderr[:1000]}
except subprocess.TimeoutExpired:
findings["scanner"] = {"error": "mcp-scan timed out"}
else:
findings["scanner"] = {"error": "uvx/mcp-scan not found; install uv: "
"curl -LsSf https://astral.sh/uv/install.sh | sh"}
# 2. Local heuristic scan of any descriptions embedded in the config
try:
with open(args.config, encoding="utf-8") as fh:
cfg = json.load(fh)
for desc in _walk_descriptions(cfg):
f = heuristic_flags(desc)
if f:
findings["heuristic"].append({"flags": f, "snippet": desc[:200]})
except (OSError, json.JSONDecodeError) as exc:
findings["heuristic"].append({"error": str(exc)})
print(json.dumps(findings, indent=2))
return findings
def _walk_descriptions(obj):
"""Yield any 'description' string values found anywhere in a nested config."""
if isinstance(obj, dict):
for k, v in obj.items():
if k == "description" and isinstance(v, str):
yield v
else:
yield from _walk_descriptions(v)
elif isinstance(obj, list):
for item in obj:
yield from _walk_descriptions(item)
def run_enum(args):
try:
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
except ImportError:
print("Install: pip install mcp", file=sys.stderr)
sys.exit(1)
async def _enum():
params = StdioServerParameters(command=args.command, args=args.args or [])
out = {"ts": datetime.now(timezone.utc).isoformat(),
"server": f"{args.command} {' '.join(args.args or [])}",
"atlas": "AML.T0010", "tools": []}
async with stdio_client(params) as (r, w):
async with ClientSession(r, w) as s:
await s.initialize()
tools = await s.list_tools()
for t in tools.tools:
flags = heuristic_flags(t.description or "")
out["tools"].append({
"name": t.name,
"desc_len": len(t.description or ""),
"flags": flags,
"verdict": "POISONED?" if flags else "clean",
})
print(json.dumps(out, indent=2))
return out
try:
asyncio.run(_enum())
except Exception as exc: # connection/protocol errors
print(f"[!] MCP enumeration failed: {exc}", file=sys.stderr)
sys.exit(2)
def main():
ap = argparse.ArgumentParser(description="MCP tool-poisoning audit agent")
sub = ap.add_subparsers(dest="mode", required=True)
ps = sub.add_parser("static", help="Run mcp-scan + heuristic scan on a config")
ps.add_argument("--config", required=True, help="Path to MCP config JSON")
pe = sub.add_parser("enum", help="Enumerate tools from a stdio MCP server")
pe.add_argument("--command", required=True, help="Server launch command, e.g. node")
pe.add_argument("--args", nargs="*", help="Arguments to the server command")
args = ap.parse_args()
if args.mode == "static":
run_static(args)
else:
run_enum(args)
if __name__ == "__main__":
main()