miesc 5.1.5

Multi-layer Intelligent Evaluation for Smart Contracts - Open-source framework that brings enterprise-grade security analysis to every developer

MIESC

35 Analysis Modules. 9 defense layers. One command.

Enterprise-grade smart contract security, free and open to everyone.

Quick Start • Why MIESC • GitHub Action • Docker • Espanol • Docs

---

Quick Start

pip install miesc
miesc scan MyContract.sol

That's it. MIESC runs Slither + Aderyn + Solhint and gives you a unified report in seconds.

Want the full 9-layer analysis with AI correlation?

miesc audit full MyContract.sol -o results.json
miesc report results.json -t premium -f pdf --llm-interpret

See example output

=== Layer 1: Static Analysis ===
OK slither: 5 findings in 1.7s
OK aderyn: 5 findings in 3.0s
OK solhint: 0 findings in 0.7s

=== Layer 2: Dynamic Testing ===
OK echidna: 0 findings in 2.0s
OK foundry: 0 findings in 9.0s

=== Layer 3: Symbolic Execution ===
OK mythril: 2 findings in 298.0s

=== Layer 5: AI Analysis ===
OK smartllm: 4 findings in 198.9s
OK gptscan: 4 findings in 49.7s

 Full Audit Summary
+----------+-------+
| Severity | Count |
+----------+-------+
| CRITICAL |     1 |
| HIGH     |    11 |
| MEDIUM   |     1 |
| LOW      |     9 |
| TOTAL    |    22 |
+----------+-------+

Tools executed: 12/29
Report saved to results.json

---

Why MIESC

The problem: Professional smart contract audits cost $50K-$200K and take weeks. Meanwhile, $1.5B+ is lost to exploits every year. Most projects ship without any audit at all. Running Slither alone catches ~70% of vulnerabilities with 15-20% false positives. Every tool has blind spots. Auditors manually run 5-10 tools, normalize outputs, and cross-reference findings. This takes hours.

MIESC makes that workflow accessible to everyone. One command orchestrates multiple security tools across 9 complementary analysis techniques, deduplicates findings, and generates professional reports. Free, open-source, runs locally — your code never leaves your machine.

Benchmark Results

SmartBugs-curated (143 contracts, 207 ground-truth vulnerabilities):

Metric	Slither alone	Mythril alone	MIESC (static)	MIESC (all layers)
Recall	43.2%	27.4%	54.6%	80.0%
Precision	8.3%	6.1%	9.3%	22.7%
F1-Score	13.9%	10.0%	15.9%	35.4%

Real-world exploits (11 confirmed DeFi exploits, $3.3B total losses):

Vulnerability	Exploits	Detected	Recall	Examples
Reentrancy	3	3	100%	Euler $197M, Rari $80M, Platypus $8.5M
Access Control	3	3	100%	Parity $280M, Ronin $624M
Flash Loan	2	2	100%	bZx $8.1M, Compound $80M
Overall	11	9	81.8%	Cohen's Kappa: 0.773

81.8% recall on real-world exploits — MIESC would have flagged 9 of 11 multi-million dollar exploits before deployment. Full methodology | Exploit evaluation

Why recall matters more than precision for pre-audit triage: High recall means fewer missed vulnerabilities. False positives are filtered in the triage step — missed vulnerabilities become exploits in production.

The 9 Defense Layers

Layer 1  Static Analysis        Slither, Aderyn, Solhint, Semgrep
Layer 2  Dynamic Testing        Echidna, Foundry, Medusa
Layer 3  Symbolic Execution     Mythril, Halmos, Manticore
Layer 4  Formal Verification    SMTChecker, Scribble, Certora*
Layer 5  AI/LLM Analysis        SmartLLM, GPTScan, LLMSmartAudit (Ollama)
Layer 6  Pattern Detection      Gas Analyzer, Clone Detector, Threat Model
Layer 7  DeFi Security          MEV Detector, Flash Loan Analyzer, Oracle Checker
Layer 8  Exploit Validation     PoC Synthesizer (Foundry), Vulnerability Verifier
Layer 9  Consensus & Reporting  Bayesian Consensus, RAG Enrichment, PDF Reports

*Certora requires API key. All other tools are fully open-source.

What MIESC integrates

Category	Count	Examples
External security tools	13	Slither, Mythril, Echidna, Foundry, Halmos, Aderyn, Semgrep
LLM analysis modules	6	SmartLLM, GPTScan, PropertyGPT (via local Ollama)
Internal analyzers	16	MEV detector, gas analyzer, threat model, clone detector
Total analysis modules	35	Across 9 complementary techniques

vs. SmartBugs 2.0 (closest competitor)

	MIESC	SmartBugs 2.0
External tools	13	19
AI/LLM analysis	Yes (Ollama local)	No
Internal analyzers	16	No
False-positive filter	Yes (RAG + ML)	No
Professional PDF reports	Yes	No
Plugin system	Yes (PyPI)	No
GitHub Action	Yes	No
SARIF output	Yes	Yes

---

GitHub Action

Add smart contract security to any CI/CD pipeline in 30 seconds:

# .github/workflows/security.yml
name: Security
on: [push, pull_request]

permissions:
  security-events: write
  pull-requests: write

jobs:
  miesc:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: fboiero/MIESC@v5
        with:
          path: contracts/
          mode: scan
          fail-on: high
          upload-sarif: true
          comment-on-pr: true

Results appear in GitHub's Security tab and as a PR comment. See example workflow for advanced configurations.

Available Modes

Mode	Tools	Time	Use Case
scan	Slither, Aderyn, Solhint	~30s	Every push
audit-quick	4 core tools	~2min	PR checks
audit-full	All 9 layers	~10min	Pre-release
audit-profile	Configurable	Varies	DeFi, tokens, etc.

---

Installation

# Minimal CLI
pip install miesc

# With PDF reports
pip install miesc[pdf]

# Everything (PDF, LLM, RAG, web UI)
pip install miesc[full]

# Development
git clone https://github.com/fboiero/MIESC.git
cd MIESC && pip install -e .[dev]

Requirements: Python 3.12+. Slither installs automatically. Other tools are optional — MIESC uses whatever is available.

---

Docker

# Standard image (~3GB, multi-arch including Apple Silicon)
docker pull ghcr.io/fboiero/miesc:latest
docker run --rm -v $(pwd):/contracts ghcr.io/fboiero/miesc:latest scan /contracts/MyContract.sol

# Full image (~8GB, all 50 tools, amd64)
docker pull ghcr.io/fboiero/miesc:full
docker run --rm -v $(pwd):/contracts ghcr.io/fboiero/miesc:full audit full /contracts/MyContract.sol

# Check what tools are available
docker run --rm ghcr.io/fboiero/miesc:latest doctor

Docker + Host Ollama (recommended for GPU acceleration)

MIESC's LLM layers (Layer 5) use Ollama for local AI analysis. The recommended setup runs Ollama on your host machine (with GPU access) and connects the Docker container via network:

# 1. Install and start Ollama on your HOST (not inside Docker)
# Download from https://ollama.com
ollama serve &
ollama pull qwen2.5-coder:14b   # Best model for code analysis
ollama pull deepseek-coder:6.7b  # Lighter alternative

# 2. Scan with host Ollama (macOS)
docker run --rm \
  -e OLLAMA_HOST=http://host.docker.internal:11434 \
  -v $(pwd)/contracts:/contracts \
  ghcr.io/fboiero/miesc:latest \
  scan /contracts/MyContract.sol -o /contracts/results.json

# 3. Scan with host Ollama (Linux)
docker run --rm --network=host \
  -e OLLAMA_HOST=http://localhost:11434 \
  -v $(pwd)/contracts:/contracts \
  ghcr.io/fboiero/miesc:latest \
  scan /contracts/MyContract.sol

# 4. Generate AI-powered premium PDF report
docker run --rm \
  -e OLLAMA_HOST=http://host.docker.internal:11434 \
  -v $(pwd):/work \
  ghcr.io/fboiero/miesc:full \
  report /work/results.json -t premium -f pdf \
    --llm-interpret -o /work/audit_report.pdf

Why host Ollama? Docker containers don't have GPU access by default. Running Ollama on the host lets it use your GPU (Apple Silicon, NVIDIA CUDA) for 10-50x faster LLM inference.

Docker Compose (full stack with bundled Ollama)

For environments without a host Ollama, use docker-compose to run everything together:

# Start MIESC + Ollama + model initialization
docker compose --profile llm up -d

# Run analysis
docker compose exec miesc miesc scan /contracts/MyContract.sol

# Stop
docker compose down

The compose stack automatically pulls deepseek-coder:6.7b and configures inter-service networking.

ARM / Apple Silicon notes

The standard image runs natively on ARM. The full image is amd64-only in the registry (runs under QEMU emulation). Build natively with:

./scripts/build-images.sh full  # ~30-60 min, but native speed

---

CLI Reference

miesc scan contract.sol              # Quick scan (Slither + Aderyn + Solhint)
miesc scan contract.sol --ci         # CI mode: exit 1 on critical/high
miesc audit quick contract.sol       # 4-tool audit
miesc audit full contract.sol        # Full 9-layer audit
miesc audit layer 3 contract.sol     # Specific layer (e.g., symbolic execution)
miesc audit profile defi contract.sol  # Named profile (defi, token, security, etc.)
miesc report results.json -t premium -f pdf --llm-interpret  # AI-powered PDF report
miesc export results.json -f sarif   # Export as SARIF
miesc doctor                         # Check tool availability
miesc watch ./contracts              # Auto-scan on file changes
miesc benchmark ./contracts --save   # Track security posture over time

Analysis Profiles

Profile	Layers	Best For
fast	1	Quick feedback during development
balanced	1, 3	Pre-commit checks
ci	1	CI/CD pipelines
security	1, 3, 4, 5	High-value contracts
defi	1, 2, 3, 5, 8	DeFi protocols
token	1, 3, 5	ERC20/721/1155 tokens
thorough	1-9	Pre-release / comprehensive audit

---

Extend MIESC

Custom Detectors

from miesc.detectors import BaseDetector, Finding, Severity

class DangerousDelegatecall(BaseDetector):
    name = "dangerous-delegatecall"
    description = "Detects unprotected delegatecall patterns"

    def analyze(self, source_code, file_path=None):
        findings = []
        # Your detection logic here
        return findings

Register via pyproject.toml:

[project.entry-points."miesc.detectors"]
dangerous-delegatecall = "my_package:DangerousDelegatecall"

Plugin System

miesc plugins install miesc-defi-detectors   # Install from PyPI
miesc plugins create my-detector             # Scaffold a new plugin
miesc plugins list                           # List installed plugins
miesc detectors list                         # List all available detectors

Integrations

# Pre-commit hook (.pre-commit-config.yaml)
repos:
  - repo: https://github.com/fboiero/MIESC
    rev: v5.1.1
    hooks:
      - id: miesc-quick
        args: ['--ci']

# Foundry (foundry.toml)
[profile.default]
post_build_hook = "miesc audit quick ./src --ci"

// Hardhat (hardhat.config.js)
require("hardhat-miesc");
module.exports = {
  miesc: { enabled: true, runOnCompile: true, failOn: "high" }
};

MCP Server

MIESC integrates with AI agents via the Model Context Protocol:

miesc server mcp  # Start MCP WebSocket server

// Claude Desktop / MCP client config
{
  "mcpServers": {
    "miesc": {
      "command": "miesc",
      "args": ["server", "mcp"]
    }
  }
}

Python API

from miesc.api import run_tool, run_full_audit

results = run_tool("slither", "contract.sol")
report = run_full_audit("contract.sol")

Web UI

pip install miesc[web]
make webapp  # Opens at http://localhost:8501

Interactive Streamlit dashboard with upload, analysis, results visualization, and report export.

---

Multi-Chain Support

Chain	Status	Languages
EVM (Ethereum, Polygon, BSC, Arbitrum, etc.)	Production	Solidity, Vyper
Solana	Alpha	Rust/Anchor
NEAR	Alpha	Rust
Move (Sui, Aptos)	Alpha	Move
Stellar/Soroban	Alpha	Rust
Algorand	Alpha	TEAL, PyTeal
Cardano	Alpha	Plutus, Aiken

miesc scan program.rs --chain solana
miesc scan module.move --chain sui

Non-EVM support is experimental. EVM analysis (50 tools, 9 layers) is production-ready.

---

Compliance Mapping

MIESC maps every finding to international security standards:

ISO/IEC 27001:2022 | NIST CSF | OWASP Smart Contract Top 10 | CWE | SWC Registry | MITRE ATT&CK | PCI-DSS | SOC 2

---

Architecture

Contract.sol
    |
    v
 CLI / API / MCP / GitHub Action
    |
    v
 Orchestrator --> 9 Layers --> 50 Tool Adapters
    |
    v
 Finding Aggregator --> ML Pipeline --> RAG Context --> FP Filter
    |
    v
 Report Generator --> JSON / SARIF / PDF / HTML / Markdown

MIESC uses a dual-package architecture:

• miesc/ - Public API (stable, pip-installable)
• src/ - Internal implementation (35 analysis modules, ML pipeline, RAG, report generation)

See ARCHITECTURE.md for the full technical design with Mermaid diagrams.

---

Academic Validation

MIESC was developed as a Master's thesis in Cyberdefense at UNDEF-IUA (Argentina). Evaluated on the SmartBugs-curated benchmark (Durieux et al., ICSE 2020):

• 143 contracts, 207 ground-truth vulnerabilities, 10 categories
• 80% recall (184/230 vulnerabilities detected) — best-in-class for multi-tool orchestration
• 90.6% recall on reentrancy, 100% on unchecked calls and time manipulation
• 85% faster than running tools manually (~1 sec/contract)
• Full results: SMARTBUGS_SCIENTIFIC_REPORT.md

If you use MIESC in research, please cite:

@mastersthesis{boiero2025miesc,
  title={Integrated Security Assessment Framework for Smart Contracts:
         A Defense-in-Depth Approach to Cyberdefense},
  author={Boiero, Fernando},
  year={2025},
  school={Universidad de la Defensa Nacional (UNDEF) - IUA C{\'o}rdoba},
  type={Master's Thesis in Cyberdefense}
}

---

Digital Public Good

MIESC is a candidate for Digital Public Goods Alliance certification (Application ID: GID0092948), aligned with the UN Sustainable Development Goals:

• SDG 9 (Industry & Infrastructure): Strengthening blockchain infrastructure security
• SDG 16 (Peace & Strong Institutions): Protecting digital assets, reducing fraud
• SDG 17 (Partnerships): Open-source collaboration for global security standards

The project is fully compliant with all 9 DPGA indicators: open license (AGPL-3.0), clear ownership, platform independence, comprehensive documentation, data portability (SARIF/CSV/JSON), privacy-preserving (local-first), standards-based (12 international standards), and responsible use policies.

Policy	Description
DPG Compliance	Full 9-indicator compliance statement
SDG Relevance	Sustainable Development Goals mapping
Privacy Policy	Data handling and privacy statement
Responsible Use	Ethical use guidelines
Do No Harm	Risk assessment and mitigations

---

Documentation

Resource	Description
Installation Guide	Complete setup instructions
Quick Start	Get running in 5 minutes
Architecture	Technical design and layer details
Tool Reference	All 35 analysis modules and their capabilities
Report Guide	Report templates and customization
Custom Detectors	Build your own detectors
Multi-Chain	Non-EVM chain analysis
API Reference	Auto-generated from docstrings
Contributing	Development guidelines
Roadmap	What's coming next

---

Troubleshooting

Common Issues

miesc: command not found After pip install miesc, the entry point may not be on your PATH. Use:

python3 -m miesc.cli.main --help

Or add ~/.local/bin (or your venv's bin) to your PATH.

Tool 'mythril' not installed Optional tools must be installed separately. Either:

pip install miesc[full]              # All Python-based tools
brew install mythril                 # System install
docker run ghcr.io/fboiero/miesc:full  # Or use the full Docker image

Ollama API error: HTTP 404 or LLM analysis returns empty The required model isn't pulled. Run:

ollama pull qwen2.5-coder:14b   # ~9 GB, recommended
ollama pull qwen2.5-coder:32b   # ~20 GB, more accurate (needs 24+GB RAM)

Verify Ollama is running: curl http://localhost:11434/api/tags

Docker container can't reach Ollama on host (macOS)

docker run -e OLLAMA_HOST=http://host.docker.internal:11434 ...

On Linux, use --network=host and OLLAMA_HOST=http://localhost:11434.

Slither errors with solc not found or version mismatch Install solc-select:

pip install solc-select
solc-select install 0.8.20 && solc-select use 0.8.20

PDF report generation fails (weasyprint errors) WeasyPrint needs system libraries:

brew install pango cairo gdk-pixbuf libffi   # macOS
sudo apt install libpango-1.0-0 libpangoft2-1.0-0  # Linux

miesc audit full runs slowly (>10 min per contract) Heavy LLM models (32B) on small contracts. Use qwen2.5-coder:14b in ~/.miesc/config.yaml or run quick scan: miesc audit quick.

---

Contributing

git clone https://github.com/fboiero/MIESC.git
cd MIESC && pip install -e .[dev]
pytest tests/  # 4,800+ tests, 82% coverage

See CONTRIBUTING.md for guidelines. CONTRIBUTING_ES.md disponible en espanol.

---

License

AGPL-3.0 - Free for any use. Security should not be a privilege reserved for well-funded projects. If you build a service on top of MIESC, contribute your changes back.

Author

Fernando Boiero - Master's Thesis in Cyberdefense, UNDEF-IUA Argentina

Built on the shoulders of: Slither, Mythril, Echidna, Foundry, Aderyn, Halmos, and the Ethereum security community.