Cloud infrastructure vulnerabilities comprise security weaknesses in the cloud platforms and services that host machine learning models, which can be exploited to gain unauthorized access to model artifacts. Machine learning models deployed on cloud platforms may be vulnerable to attacks that target the underlying infrastructure rather than the model interface. These might include exploiting misconfigured access controls, leveraging multi-tenancy vulnerabilities in shared computing resources, or attacking storage systems where model checkpoints or parameters are stored. Successful attacks against cloud infrastructure can potentially yield direct access to model weights and architecture information.

Introduction To Cloud Infrastructure Vulnerabilities In AI Model Extraction

AI model extraction attacks are a growing threat in the modern cloud landscape, targeting the intellectual property and sensitive data embedded in machine learning models deployed via cloud services (Orekondy et al., 2018). In these attacks, adversaries systematically interact with cloud-hosted models-often through exposed APIs-to reconstruct the underlying model or extract proprietary information. The cloud’s scalability and accessibility, while enabling rapid AI deployment, also expand the attack surface, making cloud-based AI models especially vulnerable to sophisticated extraction techniques (Papernot et al., 2017).

Machine Learning as a Service (MLaaS) platforms, which provide model access via standardized cloud APIs, are particularly at risk. Attackers exploit these interfaces by issuing large volumes of queries, sometimes distributed across multiple accounts or service tiers, to gather enough input-output pairs to train a substitute model that closely mimics or even duplicates the target’s behavior (Tramèr et al., 2016). The risk is compounded by the fact that cloud environments often suffer from misconfigurations, insecure APIs, and insufficient access controls, all of which can facilitate unauthorized access and model theft.

Types of Cloud Infrastructure Vulnerabilities In AI Model Extraction

Understanding the specific cloud infrastructure vulnerabilities that enable model extraction is critical to developing effective defense strategies and maintaining the integrity of AI assets. Cloud infrastructure vulnerabilities adversaries popularly exploit include:

1. AI-Specific Library & Framework Vulnerabilities
2. API Security Weaknesses
3. Cloud Misconfigurations
4. Containerization & Virtualization Vulnerabilities
5. Monitoring & Detection Blind Spots
6. Platform-Level Vulnerabilities
7. Poor Coding Practices

1. AI-Specific Library & Framework Vulnerabilities

AI-Specific Library & Framework Vulnerabilities include Supply Chain Attacks and Input Validation Flaws.

Supply Chain Attacks

Malicious code in third-party libraries or compromised dependencies can introduce backdoors, enabling attackers to extract models or data from within the infrastructure. As AI systems typically rely on numerous open-source components, this attack vector is particularly concerning.

Input Validation Flaws

Poorly validated inputs in AI libraries can lead to code injection or remote code execution, potentially allowing attackers to access model internals directly. These vulnerabilities may allow attackers to bypass normal security controls by exploiting the AI framework itself.

2. API Security Weaknesses

API Security Weaknesses include Inadequate Rate Limiting, Authentication and Authorization Flaws, Broken Object-Level Authorization (BOLA), Unrestricted Query Access, and Unprotected API Endpoints.

Inadequate Rate Limiting

Cloud-hosted AI services often expose APIs that lack proper rate limiting, allowing attackers to make thousands or millions of queries to systematically map the model’s decision boundaries. Without robust rate limiting at both the API gateway and application levels, attackers can accelerate extraction attacks that would otherwise be detectable through abnormal query patterns.

Authentication & Authorization Flaws

Many cloud services implement insufficient authentication mechanisms or have overly permissive authorization models. These weaknesses enable attackers to gain unauthorized access to model inference endpoints or escalate privileges to bypass security controls designed to prevent extraction attacks.

Broken Object-Level Authorization (BOLA)

Attackers exploit insufficient checks on API endpoints to access models or data they shouldn’t, facilitating unauthorized extraction (OWASP API Security 2023 Top 10). This vulnerability allows attackers to bypass intended access restrictions by manipulating object references in API requests.

Unrestricted Query Access

APIs that do not enforce query limits or monitor for abnormal patterns enable attackers to automate extraction attacks, often without detection (Tramèr et al., 2016). This allows systematic probing of model boundaries and behavior without triggering security alerts.

Unprotected API Endpoints

Some cloud deployments expose debugging, testing, or administrative APIs without proper protection. These endpoints may provide richer information about model outputs (like confidence scores or intermediate activations) that accelerate extraction attacks by providing more detailed gradient information.

3. Cloud Misconfigurations

Cloud Misconfigurations include Exposed Storage & Model Artifacts and Over-permissive IAM Roles.

Exposed Storage & Model Artifacts

Storage buckets or repositories left publicly accessible can allow direct download or inspection of model files, bypassing the need for query-based extraction. This represents one of the most direct forms of model theft, as it may expose complete model weights and architectures.

Over-permissive IAM Roles

Excessive privileges granted to users or services can enable lateral movement within the cloud environment, increasing the risk of model theft. Attackers who compromise even low-privilege accounts may be able to escalate to roles with access to model artifacts.

4. Containerization & Virtualization Vulnerabilities

Containerization & Virtualization Vulnerabilities include Container Escape Vulnerabilities, Shared Resource Attacks, and Snapshot & Image Vulnerabilities.

Container Escape Vulnerabilities

AI models deployed in containers may be vulnerable to container escape attacks, where adversaries breach container boundaries to access the model weights directly. Cloud environments with misconfigured container security settings or outdated container runtimes present particularly high risks.

Shared Resource Attacks

Multi-tenant cloud environments hosting AI services can be vulnerable to side-channel attacks where an attacker deploys their workload on the same physical infrastructure as the target model. These attacks may leverage shared cache, memory, or processing resources to infer information about the model during inference operations.

Snapshot & Image Vulnerabilities

Cloud providers often use VM snapshots and container images for deployment. Inadequately secured snapshots or images containing model artifacts may be accessible through misconfigured access controls, allowing direct extraction without the need for query-based attacks.

5. Monitoring & Detection Blind Spots

Monitoring & Detection Blind Spots include Insufficient Logging & Monitoring, Delayed Alert Response, and Limited Visibility Into Shadow IT.

Insufficient Logging & Monitoring

Many cloud deployments lack comprehensive logging of model access and usage patterns, making it difficult to detect systematic querying indicative of extraction attacks. This is particularly problematic in distributed systems where logs must be correlated across multiple services.

Delayed Alert Response

Even with monitoring in place, alerts about unusual model access patterns may not trigger immediate responses, giving attackers sufficient time to extract significant portions of model functionality before detection.

Limited Visibility Into Shadow IT

Unofficial or undocumented AI deployments within an organization’s cloud environment may lack security controls and monitoring, creating blind spots where extraction attacks can proceed undetected.

6. Platform-Level Vulnerabilities

Platform-Level Vulnerabilities include Critical Cloud Platform Flaws and Zero-Day Attacks.

Critical Cloud Platform Flaws

Vulnerabilities in the cloud provider’s API or orchestration layer can allow attackers to access or manipulate AI models across multiple tenants. These flaws are particularly dangerous as they may impact numerous models simultaneously.

Zero-Day Attacks

Exploitation of unknown vulnerabilities before they are patched can result in undetected extraction or compromise of AI assets. Zero-day vulnerabilities in cloud platforms or AI frameworks can bypass even well-designed security controls.

7. Poor Coding Practices

Poor Coding Practices include Hardcoded Secrets and Unvetted AI-Generated Code.

Hardcoded Secrets

Credentials or API keys embedded in code can be easily extracted if code repositories are exposed, enabling unauthorized access to models (OWASP API Security 2023 Top 10). This is a common issue in rapidly developed AI systems where security practices may not keep pace with development.

Unvetted AI-Generated Code

Automatically generated code that isn’t reviewed or tested may introduce exploitable vulnerabilities, especially in the context of rapidly evolving AI pipelines (Sculley et al., (2015)). As organizations increasingly rely on AI-assisted programming, this creates new risks for model security.

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