AI model extraction refers to an attack method where an adversary attempts to replicate the functionality of a machine learning model by systematically querying it and using its outputs to train a surrogate model. Unlike model inversion attacks, which aim to recover sensitive training data, model extraction targets the underlying logic and behavior of the model itself (Fredrikson et al., 2015).
The attacker does not need access to the model’s internal code or training data; instead, they learn how the model behaves by observing its responses to various inputs, enabling them to build a copy that mimics the original’s decision-making process (Tramèr et al., 2016; Orekondy et al., 2019). According to Oliynyk et al. (2023), “the technique of model stealing (also called ‘model extraction’) aims at obtaining training hyperparameters, the model architecture, learned parameters, or an approximation of the behaviour of a model, all of which to the detriment of the lawful model owner.”
Model Extraction: Growing Concern, Practical Threat
Model extraction attacks represent a significant, growing, and practical threat to AI systems, with demonstrated effectiveness across multiple domains and model types, often achievable at relatively low cost while employing increasingly sophisticated techniques.
While Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making even complex ML models available via pay-per-query systems, black-box access to a model does not imply a protected model (Oliynyk et al. 2023). By giving customers access to model predictions, MLaaS providers endanger their valuable intellectual property – such as sensitive training data, learned parameters, optimized hyperparameters, model architecture, or even model behavior – creating a fundamental tension between model accessibility and confidentiality (Shokri et al, 2017). The business impact of such attacks is particularly severe in sectors like finance, healthcare, and enterprise AI, where the consequences can range from financial loss to compromised patient care (Juuti et al., 2019).
These attacks are highly practical, especially when targeting black-box models accessible via APIs, and the risk of model extraction is both significant and measurable, with research demonstrating that attackers can achieve high-fidelity replicas of target models with a surprisingly small number of queries – sometimes achieving over 95% accuracy while querying as little as 5–7% of the training data (Papernot et al., 2017). For example, research demonstrates that extracted versions of BERT-based APIs perform almost identically to their originals (Krishna et al., 2019), and in some cases, attackers have created smaller models that even outperform the original on specific tasks (Orekondy et al., 2019). Further, attack techniques are becoming more efficient, reducing both the time and cost required for successful extraction, with some studies showing that effective model extraction can be achieved for as little as $7 in API costs (Tramèr et al., 2016). Finally, sophisticated attackers often evade detection by distributing their queries or mimicking normal usage patterns (Juuti et al., 2019).
AI Model Architecture & Deployment Environment Impact Extraction Attack Vulnerability
Neural networks, especially deep neural networks and large language models (LLMs), are particularly vulnerable to extraction attacks due to their complex architectures and the richness of their outputs, which provide attackers with ample information to reconstruct or closely approximate the original model simply by querying it and analyzing the responses (Tramèr et al., 2016; Orekondy et al., 2019). This risk is especially pronounced when these models are deployed via public APIs in the cloud, with cloud ML services being particularly susceptible, though edge and federated learning environments are also vulnerable (Papernot et al., 2017). The complexity and architecture of the model, as well as the deployment environment, play crucial roles in determining both the risk and the methods available to attackers.
AI Model Architecture vs. Extraction Attack Vulnerability
The architecture of the target model plays a significant role in determining its susceptibility to model extraction attacks. Let’s briefly review decision trees, ensemble methods, large language models (LLMs), linear models, neural networks, and support vector machines (SVMs)
Decision Trees
Decision Trees are predictive models that map observations to conclusions about an item’s target value through a tree-like structure of decision rules. Each internal node represents a test on an attribute, each branch represents the outcome of that test, and leaf nodes represent class labels or probability distributions. While not immune to extraction attacks, they generally require fewer queries to extract compared to neural networks because their discrete decision boundaries can be more explicitly mapped. Their straightforward structure may make them easier to defend, but this same explicitness also makes their decision rules more readily discoverable through systematic probing.
Ensemble Methods
Ensemble Methods combine multiple base models to improve overall performance and robustness. Random Forests aggregate the predictions of multiple decision trees trained on different subsets of data, while Gradient Boosting builds an ensemble of weak learners sequentially to correct errors of previous models. These approaches are more resistant to extraction attacks than individual models because they incorporate multiple decision paths and internal voting or weighting mechanisms. However, their overall behavior can still be approximated with sufficient querying, particularly if the extraction attack uses a model architecture with enough capacity to capture the ensemble’s aggregate decision boundary.
Large Language Models (LLMs)
Large Language Models (LLMs) are a type of neural network architecture specialized for natural language processing tasks, typically using transformer-based designs with billions or trillions of parameters. These models process text by encoding complex contextual relationships and can generate human-like text responses to prompts. LLMs are especially at risk for extraction attacks for several reasons. Their outputs can reveal not only the underlying model behavior but also proprietary training data or sensitive prompts, which can be highly valuable or confidential (Carlini et al, 2021). They present a particularly concerning risk profile because their information leakage potential is substantial – outputs often contain details that reveal internal knowledge representations and reasoning patterns. Attackers can use extraction attacks to reconstruct both the model’s knowledge and specific operational details, such as prompts or hyperparameters. The commercial value and unique capabilities of LLMs further increase their attractiveness as targets, with successful extraction potentially enabling downstream attacks like membership inference or data reconstruction. Additionally, many LLMs are deployed as accessible APIs, facilitating systematic querying and data collection for extraction attempts.
Linear Models
Linear Models perform classification or regression tasks by computing a weighted sum of input features and applying a simple (often linear) function to produce outputs. These include logistic regression for classification and linear regression for numeric prediction. Similar to decision trees, linear models require relatively few queries to extract because they establish straightforward decision boundaries. Their simplicity makes them both easier to extract (as fewer queries are needed to approximate their behavior) and potentially more straightforward to protect through techniques like input perturbation, which can obscure the true decision boundary without significantly affecting legitimate use.
Neural Networks
Neural Networks are computing systems inspired by biological neural networks in animal brains. They consist of interconnected nodes (“neurons”) organized in layers that process information through weighted connections. Deep neural networks contain multiple hidden layers between input and output layers, enabling them to learn complex patterns and representations. Their vulnerability stems from their high capacity and intricate behavior, which leads to significant information leakage through outputs. Despite having many parameters, they remain functionally extractable because their complex decision boundaries can often be approximated through systematic querying, especially when confidence scores are available.
Support Vector Machines (SVMs)
Support Vector Machines (SVMs) find the hyperplane that maximizes the margin between different classes in the feature space, often using kernel functions to handle non-linearly separable data. SVMs are moderately vulnerable to extraction attacks as their decision boundaries can be approximated by querying points near the boundary. Linear SVMs are particularly susceptible due to their simpler boundary structure, while kernel-based SVMs with more complex decision surfaces may require more queries but remain vulnerable to functional approximation through sufficient sampling around their decision boundaries.
AI Deployment Environment vs. Extraction Attack Vulnerability
The deployment environment significantly impacts the risk and methods of model extraction, with each environment presenting distinct vulnerabilities and attack vectors. Let’s briefly review browser-based deployment environments, cloud-based API services, edge devices, and federated learning systems.
Browser-based Deployment
Browser-based Deployment runs models directly in web browsers on client devices using JavaScript implementations or WebAssembly. This environment creates unique vulnerabilities because client-side execution necessarily exposes model weights and architecture directly in downloadable code that can be examined by users. Extraction methods in this context include code inspection to directly access model parameters, network traffic analysis to observe model loading and usage patterns, and direct access to model files in browser storage or memory. Potential mitigations include model encryption to obscure weights during transmission, progressive loading to avoid exposing the entire model at once, and serving only necessary model components for specific tasks rather than providing the complete model
Cloud-based API Services
Cloud-based API Services involve centralized model hosting with remote access through standardized interfaces. These environments are generally more vulnerable to extraction attacks because they expose powerful, centralized models through public APIs, making it feasible for attackers to automate large-scale querying and reconstruction (Tramèr et al., 2016). They enable attackers to conduct systematic probing operations over extended periods without physical access constraints. The risk is most pronounced for cloud ML services that provide detailed output information like confidence scores or probabilities. Mitigation strategies typically include rate limiting to restrict query volume, anomaly detection to identify suspicious patterns, query monitoring to track usage patterns, and pricing structures designed to make large-scale extraction economically infeasible.
Edge Devices
Edge Devices run models directly on local hardware such as smartphones, IoT devices, or specialized edge computing units. These tend to be less exposed to remote extraction, as the models are usually smaller and less complex due to hardware constraints, and large-scale automated querying is more difficult without direct network access. However, edge deployments introduce the risk of physical attacks if an adversary gains direct access to the device. These physical access scenarios enable additional attack vectors like memory analysis, side-channel attacks based on power consumption or electromagnetic emissions, and direct examination of model files. Though less vulnerable than cloud services to remote extraction, edge environments are still susceptible to extraction attacks through these alternative methods (Papernot et al., 2017).
Federated Learning Systems
Federated Learning Systems distribute the training process across multiple devices while keeping data localized, with only model updates being shared with a central server. While this approach improves data privacy, these systems remain vulnerable despite their distributed nature. They present unique attack surfaces where compromising aggregation servers or participating as a malicious node can facilitate extraction by intercepting model updates or poisoning the learning process. Effective mitigations include secure aggregation protocols that prevent individual contributions from being isolated and analyzed, and participant verification to reduce the risk of malicious participants joining the federation specifically for extraction purposes.
Final Thoughts
The rapidly expanding capabilities of AI/ML systems present both opportunities and risks – recent advancements hold potential to significantly enhance labor productivity and human health, yet this growth brings with it risks such as AI misuse and unintended consequences of deployment. Immediate action is required for AI organizations to prepare for future security needs and, given the diversity of attack vectors, defenses need to be varied and comprehensive – as achieving strong security against one category of attack does not protect an organization from others. Finally, as model extraction techniques grow more sophisticated, the AI community must continue advancing research to better understand and mitigate these threats, ensuring that valuable AI intellectual property remains protected while maintaining appropriate levels of model accessibility and utility.
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