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🎯 Overview

NullSec ModelAudit is a security auditing framework for machine learning models. It inspects model files for hidden payloads (pickle deserialization, Lambda layers), checks for backdoors via Neural Cleanse and Meta Neural Analysis, evaluates robustness boundaries, and generates compliance-ready audit reports covering OWASP ML Top 10 risks.

⚡ Features

Feature	Description
File Inspector	Detect pickle exploits, malicious Lambda layers, hidden ops
Backdoor Scan	Neural Cleanse, Meta Neural Analysis, fine-pruning checks
Robustness Eval	Automated adversarial boundary testing
Supply Chain	Verify model provenance and hash integrity
Fairness Audit	Bias detection across protected attributes
OWASP ML Top 10	Map findings to OWASP ML risk categories
Report Engine	HTML/PDF/JSON audit reports with severity ratings

📋 Audit Checks

Check	Category	Severity
Pickle RCE	Deserialization	Critical
Lambda Injection	Model Architecture	Critical
Backdoor Trigger	Integrity	High
Adversarial Fragility	Robustness	High
Training Data Leakage	Privacy	High
Model Watermark	Provenance	Medium
Bias / Fairness	Compliance	Medium
Dependency Vuln	Supply Chain	Variable

🚀 Quick Start

# Full security audit of a model file
nullsec-modelaudit scan --model model.pt --format pytorch --output audit-report.html

# Check for deserialization exploits in pickle files
nullsec-modelaudit inspect --model model.pkl --check deserialization

# Backdoor detection scan
nullsec-modelaudit backdoor --model model.h5 --dataset validation/ --num-classes 10

# Supply chain verification
nullsec-modelaudit verify --model model.onnx --expected-hash sha256:abc123...

🔗 Related Projects

Project	Description
nullsec-adversarial	Adversarial ML attack toolkit
nullsec-datapoisoning	Training data poisoning detection
nullsec-llmred	LLM red-teaming framework
nullsec-promptinject	Prompt injection payloads
nullsec-linux	Security Linux distro (140+ tools)

⚠️ Legal

For authorized security auditing only. Always obtain proper authorization before auditing third-party models.

📜 License

MIT License — @bad-antics

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