# Model Theft & IP Protection


📂 ai-red-teaming

## AI RED TEAMING TECHNIQUES

# Model Theft & IP Protection

Model extraction techniques and intellectual property protection testing

## Available Techniques

### Query-Based Model Extraction

Systematic querying of AI models to reverse-engineer their parameters, architecture, and decision-making logic through response analysis.

#### KEY FEATURES

- •
Strategic query generation

- •
Response pattern analysis

- •
Parameter estimation

#### PRIMARY DEFENSES

- •
Query rate limiting and throttling

- •
Response randomization and noise injection

- •
Query pattern detection

#### KEY RISKS

### Electromagnetic Side-Channel Model Extraction

Novel attack technique using electromagnetic emissions to extract AI model hyperparameters and architecture from edge devices and TPUs.

#### KEY FEATURES

- •
Electromagnetic signal monitoring

- •
Hardware-level data extraction

- •
Non-intrusive surveillance

#### PRIMARY DEFENSES

- •
Electromagnetic shielding (Faraday cages)

- •
Physical access controls

- •
Hardware security modules

#### KEY RISKS

### Membership Inference Attacks

Determining whether specific data points were used in training an AI model, potentially exposing sensitive training data and privacy violations.

#### KEY FEATURES

- •
Training data identification

- •
Statistical confidence testing

- •
Privacy boundary testing

#### PRIMARY DEFENSES

- •
Differential privacy mechanisms

- •
Data anonymization techniques

- •
Training data access controls

#### KEY RISKS

### Advanced Model Inversion Attacks

Sophisticated techniques to reconstruct private training data from model outputs, revealing sensitive information used during training.

#### KEY FEATURES

- •
Training data reconstruction

- •
Gradient-based inversion

- •
Feature space exploration

#### PRIMARY DEFENSES

- •
Gradient noise injection

- •
Secure aggregation protocols

- •
Output perturbation mechanisms

#### KEY RISKS

### API Key and Credential Extraction

Extraction of API keys, credentials, and authentication tokens from AI applications and model serving infrastructure.

#### KEY FEATURES

- •
Credential harvesting

- •
Authentication token theft

- •
API key enumeration

#### PRIMARY DEFENSES

- •
Secure credential storage (vaults, HSMs)

- •
Environment variable protection

- •
Log sanitization and filtering

#### KEY RISKS

### Ethical Guidelines for Model Theft & IP Protection

When working with model theft & ip protection techniques, always follow these ethical guidelines:

- • Only test on systems you own or have explicit written permission to test

- • Focus on building better defenses, not conducting attacks

- • Follow responsible disclosure practices for any vulnerabilities found

- • Document and report findings to improve security for everyone

- • Consider the potential impact on users and society

- • Ensure compliance with all applicable laws and regulations

FROM THE ENGINEER BEHIND THIS CATALOG

## Get your agent system red-teamed

The attacks documented here work on production agent systems every day. Have yours tested before someone else does: prompt injection, jailbreaks, tool misuse and data exfiltration, with every finding written up next to its fix.

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## AI Red Teaming

## AI Red Teaming Techniques