modelpoison - AI Model Poisoning Detector

Detect and defend against data poisoning attacks on machine learning models.

Protect your ML training pipelines from malicious data injection and model manipulation.

🚀 Features

• Multi-Vector Detection: Detect backdoor, label flip, gradient, and feature poisoning
• Training Data Analysis: Analyze datasets for suspicious samples
• Defense Mechanisms: Apply multiple defense strategies
• Risk Scoring: Calculate poisoning risk scores
• Real-time Protection: Fast detection for production use

📦 Installation

Build from Source

git clone https://github.com/hallucinaut/modelpoison.git
cd modelpoison
go build -o modelpoison ./cmd/modelpoison
sudo mv modelpoison /usr/local/bin/

Install via Go

go install github.com/hallucinaut/modelpoison/cmd/modelpoison@latest

🎯 Usage

Detect Poisoning

# Detect poisoning in training data
modelpoison detect training_data.csv

# Analyze security
modelpoison analyze

Apply Defenses

# Defend model against poisoning
modelpoison defend training_data.csv

# Get recommendations
modelpoison recommend

Programmatic Usage

package main

import (
    "fmt"
    "github.com/hallucinaut/modelpoison/pkg/detect"
    "github.com/hallucinaut/modelpoison/pkg/defend"
)

func main() {
    // Create detector
    detector := detect.NewDetector()
    
    // Detect poisoning
    result := detector.Detect(samples)
    
    fmt.Printf("Poisoned samples: %d\n", result.PoisonedCount)
    fmt.Printf("Risk Score: %.0f%%\n", result.RiskScore*100)
    
    // Apply defense
    defender := defend.NewDefender()
    defense := defender.Defend(result.RiskScore, "Data Cleaning")
    
    fmt.Printf("Defense Success: %v\n", defense.Success)
    fmt.Printf("Risk Reduction: %.0f%%\n", defense.RiskReduction*100)
}

🔍 Attack Types Detected

Backdoor Attacks

Inject malicious triggers:

• Visual patterns in images
• Specific words in text
• Trigger sequences in time series

Label Flipping

Corrupt training labels:

• Random label noise
• Targeted label changes
• Consistent mislabeling

Gradient Poisoning

Manipulate training gradients:

• Byzantine attacks
• Coordinate poisoning
• Gradient compression attacks

Feature Poisoning

Corrupt input features:

• Feature manipulation
• Statistical outliers
• Distribution shifts

Data Poisoning

Inject malicious data:

• Malicious samples
• Distribution poisoning
• Concept drift attacks

🛡️ Defense Strategies

Strategy	Effectiveness	Overhead	Use Case
Adversarial Training	85%	40%	High-security training
Ensemble Defense	90%	50%	Critical applications
Robust Aggregation	80%	15%	Distributed training
Data Cleaning	75%	20%	General use
Input Filtering	70%	10%	Real-time protection
Outlier Detection	65%	12%	Quick defense

📊 Risk Levels

Score	Level	Action
0-10%	MINIMAL	Monitor
10-30%	LOW	Review data
30-50%	MEDIUM	Clean data
50-70%	HIGH	Investigate
70-100%	CRITICAL	Block training

🧪 Testing

# Run all tests
go test ./...

# Run with coverage
go test -cover ./...

# Run specific test
go test -v ./pkg/detect -run TestDetectPoisoning

📋 Example Output

Detecting poisoning in: training_data.csv

=== Model Poisoning Detection Report ===

Total Samples: 1000
Poisoned Samples: 15
Risk Score: 15%
Method: ensemble_detection

Detected Poisoned Samples:
[1] backdoor
    ID: sample_001
    Type: backdoor
    Score: 78%
    Description: Potential backdoor trigger detected
    Evidence: Unusual feature pattern

⚠️  POISONING DETECTED
Recommendation: Clean training data before training

🔒 Security Use Cases

• ML Pipeline Security: Protect training data from poisoning
• Model Integrity: Ensure trained models are clean
• Data Quality Assurance: Validate training datasets
• AI Supply Chain Security: Secure ML data pipelines
• Compliance: Meet AI security requirements

🛡️ Best Practices

1. Validate training data before training
2. Monitor for poisoning during training
3. Use multiple defenses for critical systems
4. Test models for backdoor behavior
5. Regular security audits of ML pipelines
6. Implement data versioning for reproducibility

🏗️ Architecture

modelpoison/
├── cmd/
│   └── modelpoison/
│       └── main.go          # CLI entry point
├── pkg/
│   ├── detect/
│   │   ├── detect.go       # Detection logic
│   │   └── detect_test.go  # Unit tests
│   └── defend/
│       ├── defend.go       # Defense mechanisms
│       └── defend_test.go  # Unit tests
└── README.md

📄 License

MIT License

🙏 Acknowledgments

• Machine learning security research community
• Adversarial machine learning researchers
• AI safety practitioners

🔗 Resources

• Adversarial Machine Learning
• ML Security Best Practices
• AI Red Teaming

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Built with GPU by hallucinaut