Predict. Simulate. Secure.
An AI‑powered red‑team simulation and attack‑path prediction engine designed for enterprise‑grade security assessment.
AAPP‑MART helps organizations anticipate attack paths and validate defenses using AI‑driven simulations.
AAPP-MART is an AI‑driven multi‑agent red team engine built to transform how organizations approach cybersecurity validation.
Unlike traditional tools that only provide reactive alerts, AAPP-MART leverages autonomous agents and attack path prediction models to simulate realistic adversarial behavior across complex infrastructures.
By combining machine learning, penetration testing methodologies, and MITRE ATT&CK alignment, AAPP-MART delivers a forward-looking security posture assessment.
It is designed for security researchers, enterprise security teams, academia, and developers who need reproducible, scalable, and AI-enhanced threat simulations.
AAPP‑MART (Autonomous Attack Path Prediction & Multi‑Agent Red Team) is an open‑source Python engine for offensive security research and automated risk assessment. It combines AI‑driven attack‑path prediction with autonomous adversarial simulation to model how attackers move through an environment and to surface actionable security insights.
Unlike traditional static vulnerability scanning, AAPP‑MART blends predictive analytics with multi‑agent red‑team behavior to provide continuous security evaluation. Its architecture helps defenders anticipate attack strategies, validate controls, and understand real‑world risk through repeatable and data‑driven simulations.
AAPP-MART is a deterministic attack path modeling and controlled adversary simulation engine designed for authorized defensive security validation.
The system combines:
- Graph-based attack path prediction
- Controlled multi-agent adversary simulation
- Risk-scored analytical reporting
It does not perform destructive exploitation.
This example reflects the intended public API design:
from aapp_mart.core.orchestrator import AAPP_MART
engine = AAPP_MART(target="192.168.1.10")
engine.run()
report = engine.get_report()
print(report)Core orchestration modules are currently under development.
See API Reference and Architecture for interface details and system structure.
AAPP-MART is intended solely for authorized security assessment, defensive threat modeling, and controlled adversary simulation within environments where explicit permission has been granted.
The system is designed for non-destructive analysis and does not support uncontrolled exploitation. Users are responsible for ensuring lawful and policy-compliant usage.
Modern infrastructures are too complex for traditional security testing. AAPP-MART combines predictive AI with autonomous adversarial simulation to continuously evaluate an environment’s real attack surface.
AAPP-MART stands out from traditional security tools in its approach:
- Traditional scanners → static, reactive, often limited to known vulnerabilities.
- BAS (Breach & Attack Simulation) tools → rely on predefined playbooks and limited scenarios.
- AAPP-MART → predictive, autonomous, and adaptive: forecasts attack paths and executes intelligent multi-agent simulations.
By combining AI-driven attack path prediction with autonomous red team simulations, AAPP-MART provides organizations with a forward-looking security posture, not just reactive alerts.
AAPP‑MART is currently in development. A full installation workflow will be made available once the platform reaches the v1.0.0 production‑ready release.
For detailed information, see the Development Guide.
AAPP-MART is designed for:
- Security researchers → to simulate adversarial behavior and validate defenses.
- Enterprise security teams → to forecast attack paths and strengthen controls.
- Academia → for studying AI-driven threat modeling and red-team automation.
- Developers → to integrate predictive security insights into custom workflows.
Detailed guides and references are also available in the repository:
- Research Foundations
- System Components
- Threat Model
- Risk Model
- Deployment Guide
- Full Installation Guide
- Module Development
- Prediction Engine Details
- Examples & Quick Starts
- Roadmap & Milestones
- Contributing Guidelines
- Changelog
- Security Policy
Copyright © 2026 secwexen.
This project is licensed under the Apache License, Version 2.0.
See the LICENSE file for full details.
- Fork the repository and create a feature or fix branch (e.g.
feature/your-feature). - Make your changes and add relevant tests.
- Ensure all tests pass (
pytest) and code style checks (e.g.make lint). - Open a pull request referencing related issues/discussion when possible.
- All PRs must pass CI checks before merging.
Contributions are welcome.
Please open an issue before submitting major changes or new features.
See CONTRIBUTING.md for detailed contribution guidelines.
AAPP-MART development is structured into strategic phases:
Phase 1 – Research & Architecture
Phase 2 – Core Implementation
Phase 3 – Ecosystem & Advanced Features
Early-stage open source project. Core implementation is still in progress.
AAPP-MART is currently under active development. This repository provides the foundational architecture, core interfaces, and initial logic of the AAPP-MART engine.
Advanced prediction models, autonomous agent behaviors, and controlled simulation capabilities are being implemented progressively.
- 💡 Check out Issues for tasks and ideas.
- 💬 Join Discussions to share feedback and proposals.
- ⭐ Found AAPP-MART useful? Give us a star and help grow the community!
- 🔧 Contribute code, documentation, or testing — see CONTRIBUTING.md for details.
If you discover a security vulnerability, please follow our responsible disclosure process.
Read SECURITY.md for instructions on reporting issues securely.