Secwexen
Rating: ★★★★★
"AAPP-MART is highly useful for security research and offensive security simulations. The predictive engine and multi-agent red team simulation provide actionable insights that traditional tools cannot."
AI-Autonomous Attack Path Prediction & Multi-Agent Red Team Simulation Engine
AAPP-MART predicts attack paths and simulates them with a multi-agent red team to identify and mitigate security risks.
Predict. Simulate. Secure your infrastructure.
Get Started on GitHubModern infrastructures are too complex for traditional security testing. AAPP-MART stands out from traditional security tools in its approach:
AAPP-MART stands out from traditional offensive security tools in its approach:
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.
(GitHub)
AAPP (Prediction Engine)
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Predicted Attack Paths
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MART (Multi-Agent Red Team)
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Autonomous Simulation
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Final Report & Defense Recommendations
Run the engine against a target after installation:
from aapp_mart.core.orchestrator import AAPP_MART
engine = AAPP_MART(target="192.168.1.10")
engine.run()
report = engine.get_report()
print(report)
Command-line interface (CLI) example:
# Run a simulation
$ aapp-mart --target 192.168.1.10 --mode full
# Generate report
$ aapp-mart --report latest
This will execute the engine and generate actionable attack path predictions and security recommendations. (GitHub)
Open-source under the Apache-2.0 license, AAPP-MART is intended for ethical security assessments, penetration testing, and defensive validation. Operate only in authorized environments. (GitHub)
Visit the repository for full documentation, installation steps, examples, and contribution guidelines: GitHub: secwexen/aapp-mart.
Step-by-step instructions to install AAPP-MART on Linux, macOS, or Windows, including dependencies and configuration. For full details, see the installation guide.
Quick start examples to run your first attack path prediction and simulation. Includes sample commands and configuration. Refer to examples for code samples and quick start scripts.
Common questions about installation, usage, troubleshooting, and limitations. Check FAQ if available.
Learn how to contribute to AAPP-MART: submitting code, reporting issues, or collaborating. See CONTRIBUTING.md for full instructions.
See the CHANGELOG for past versions, updates, and bug fixes.
Responsible disclosure and reporting security issues are documented in SECURITY.md.
AAPP-MART is designed exclusively for authorized security testing, defensive threat modeling, and red team simulations with explicit permission. It is intended to help organizations understand and reduce their attack surface by simulating adversarial behavior in a controlled and authorized manner.
The project is not intended for unauthorized access, real-world exploitation, or destructive attack activities. Users are responsible for complying with all applicable laws, regulations, and organizational policies. Its primary goal is to improve defensive posture, not to facilitate real-world attacks.
AAPP-MART is intended exclusively for ethical, legal, and authorized security research, penetration testing, and defensive security validation.
The use of this tool against systems, networks, or applications without explicit authorization from the system owner is strictly prohibited and may be illegal.
The authors and contributors of this project assume no responsibility or liability for any misuse, damage, or legal consequences resulting from the use of this software. Users are solely responsible for ensuring compliance with all applicable laws, regulations, and organizational policies.
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Join Discussions to share feedback and proposals.
For support, questions, or feature requests: Open an issue.
For ideas and discussions, use GitHub Discussions.
Rating: ★★★★★
"AAPP-MART is highly useful for security research and offensive security simulations. The predictive engine and multi-agent red team simulation provide actionable insights that traditional tools cannot."