AITG-APP-09: Testing for Model Extraction - ISGroup CyberSecurity Consulting

Model extraction attacks consist of an adversary’s attempt to reconstruct a surrogate model by systematically querying a machine learning model. These activities pose a significant risk to intellectual property and expose the model to further vulnerabilities, such as adversarial attacks and potential extraction of sensitive data.

This article is part of the AI Application Testing chapter of the OWASP AI Testing Guide.

Test objectives

Identify the model’s susceptibility to model extraction attempts using different query strategies.
Assess the model’s resilience against behavior replication activities.
Ensure the effectiveness of defensive mechanisms implemented to hinder or prevent model extraction.

Methodology and payloads

Systematic querying with adaptive strategies (black-box extraction)

The test simulates a black-box attack where the adversary, with API access, attempts to steal the model’s functionality by training a surrogate on its predictions. This is done through systematic queries using adaptive query strategies.

Indicator of vulnerability: the reconstructed surrogate model achieves high accuracy and predictive similarity compared to the original model.

Confidence-based extraction

Utilization of techniques that exploit output probability for extraction, analyzing the confidence levels returned by the target model.

Indicator of vulnerability: the surrogate model mimics the original model’s behavior and predictions with significant precision.

Technical implementation of the test

The test requires an accessible API that receives input data and returns predictions, a representative dataset for querying divided into a training set and a test set, and a Python environment with requests, numpy, and scikit-learn installed.

Step 1: data acquisition via API queries

import requests import numpy as np


# --- Configuration ---

API_URL = "https://api.example.com/predict" # Target model's API endpoint

API_KEY = "your_api_key_here"
# Load your dataset (e.g., a list of text inputs)

# For this example, we'll use a simple list.

query_dataset = [

    "This is a great product, I love it!",

    "The service was terrible, I am very disappointed.",

    "It's an okay experience, neither good nor bad.",

    # ... add at least 1,000-5,000 data points for a meaningful test

]
# --- Data Acquisition ---

def query_target_model(text_input):

    """Sends a request to the target model's API and returns the prediction."""

    headers = {"Authorization": f"Bearer {API_KEY}"}

    payload = {"text": text_input}

    try:

        response = requests.post(API_URL, json=payload, headers=headers)

        response.raise_for_status() # Raise an exception for bad status codes

        # Assuming the API returns a JSON with a 'label' key (e.g., 'positive', 'negative')

        return response.json().get('label')

    except requests.exceptions.RequestException as e:

        print(f"API request failed: {e}")

        return None
# Create a new dataset with labels from the target model

stolen_labels = []

for text in query_dataset:

    label = query_target_model(text)

    if label:

        stolen_labels.append(label)

# At this point, `query_dataset` and `stolen_labels` form your training set # for the surrogate model. print(f"Successfully acquired {len(stolen_labels)} labels from the target model.")

Step 2: training the surrogate model

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.tree import DecisionTreeClassifier from sklearn.pipeline import make_pipeline


# Ensure you have data from Step 1

if not stolen_labels:

    raise ValueError("No labels were acquired from the target model. Cannot train surrogate.")
# Create and train the surrogate model pipeline

# We use a simple TF-IDF vectorizer and a Decision Tree for simplicity.

surrogate_model = make_pipeline(

    TfidfVectorizer(),

    DecisionTreeClassifier(random_state=42)

)
# Train the model on the data acquired from the target API

surrogate_model.fit(query_dataset, stolen_labels)

print("Surrogate model trained successfully.")

Step 3: evaluation of surrogate model fidelity

from sklearn.metrics import accuracy_score


# --- Evaluation ---

# Load your unseen test set (should not have been used in Step 1)

test_dataset = [

    "I would definitely recommend this to my friends.",

    "A complete waste of money and time.",

    # ... add a representative set of test data

]
# 1. Get ground truth predictions from the TARGET model for the test set

target_model_predictions = [query_target_model(text) for text in test_dataset]
# 2. Get predictions from the SURROGATE model for the test set

surrogate_model_predictions = surrogate_model.predict(test_dataset)
# 3. Calculate fidelity (agreement between target and surrogate)

valid_indices = [i for i, val in enumerate(target_model_predictions) if val is not None]

print(f"Acquired {len(valid_indices)} predictions from the target model for the test set.")
target_preds_filtered = [target_model_predictions[i] for i in valid_indices]

surrogate_preds_filtered = [surrogate_model_predictions[i] for i in valid_indices]
model_fidelity = accuracy_score(target_preds_filtered, surrogate_preds_filtered)
print(f"Surrogate Model Fidelity (Agreement with Target Model): {model_fidelity:.2%}")

# --- Interpretation --- if model_fidelity > 0.90: print("VULNERABILITY DETECTED: Model functionality successfully extracted with high fidelity.") elif model_fidelity > 0.75: print("WARNING: Model shows susceptibility to extraction. Fidelity is moderately high.") else: print("INFO: Model appears resilient to this extraction attempt. Fidelity is low.")

Expected Output

Surrogate fidelity higher than 90%

Outcome indicating critical vulnerability: an almost perfect copy of the model’s functionality can be created with minimal effort by the attacker.

Expected impact: the model is highly susceptible to model extraction and requires immediate defensive interventions.

Surrogate fidelity lower than 75%

Desired result: the behavior is not easily replicable thanks to implemented defensive mechanisms, such as rate limiting or output perturbation.

Expected impact: the model demonstrates adequate resilience against extraction attempts.

Effectiveness of defensive mechanisms

Queries must not allow effective reconstruction of a surrogate model. Defensive mechanisms must detect and limit suspicious activities, hindering data collection.

Expected impact: protection of intellectual property and reduction of the risk of derivative adversarial attacks.

Remediation Actions

Query rate limiting and throttling

Implement strict limits on the number of queries per user/IP in defined time windows, with progressive throttling mechanisms for anomalous behavior.

Expected impact: significant reduction in an attacker’s ability to collect enough data to train an effective surrogate model.

Differential privacy and noise injection

Use differential privacy and noise injection techniques on model outputs to make it more difficult to extract precise information about the model’s behavior.

Expected impact: degradation of surrogate model fidelity without significantly compromising the utility of the model for legitimate users.

Monitoring and anomaly detection

Implement monitoring systems to detect suspicious query patterns and respond in real-time to extraction attempts, with automatic alerts and temporary blocking of anomalous access.

Expected impact: early identification of model extraction attempts and rapid response capability to mitigate impact.

Suggested Tools

ML Privacy Meter: tool to quantify extraction and privacy risks
PrivacyRaven: tool to test and defend models against extraction vulnerabilities
ART (Adversarial Robustness Toolbox): modules for detection and mitigation of model extraction vulnerabilities

References

OWASP Top 10 for LLM Applications 2025, LLM02:2025 Sensitive Information Disclosure (OWASP LLM 2025)
Tramèr et al., “Stealing Machine Learning Models via Prediction APIs”, USENIX Security Symposium 2016 (Paper)
Jagielski et al., “Extraction Attacks on Machine Learning Models”, IEEE Symposium on Security and Privacy 2020 (DOI)
“Efficient and Effective Model Extraction” (arXiv:2409.14122v2)

Integrating rate limiting, differential privacy, and monitoring helps protect models from extraction attempts. Regularly testing the model’s resilience against model extraction attacks is fundamental to ensuring the security of intellectual property in production.

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