Agentic Design

Patterns

Model Theft & IP Protection

Model extraction techniques and intellectual property protection testing

5
Techniques
3
high
high Complexity
2
medium
medium Complexity

Available Techniques

🎣

Query-Based Model Extraction

(QBE)
high

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

Loss of competitive advantageIntellectual property theftRevenue loss from model piracyExposure of training data patterns
📡

Electromagnetic Side-Channel Model Extraction

(EM-SCE)
high

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

Physical theft of model IPBypass of network security controlsExposure of edge-deployed modelsIndustrial espionage
🔍

Membership Inference Attacks

(MIA)
medium

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

Privacy violations and data exposureRegulatory compliance failuresLegal liability for data misuseReputational damage
🔄

Advanced Model Inversion Attacks

(AMIA)
high

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

Exposure of sensitive training dataPrivacy violations and identity theftRegulatory compliance failuresLoss of data confidentiality
🔑

API Key and Credential Extraction

(AKCE)
medium

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

Unauthorized API access and usage costsService abuse and quota exhaustionData access through stolen credentialsReputational damage

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

AI Red Teaming

closed

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