Must Learn AI Security Part 22: Machine Learning Attacks Against AI

Chapter 22

Oct 18, 2023

This post is part of an ongoing series to educate about new and known security vulnerabilities against AI.

The full series index (including code, queries, and detections) is located here:

The book version (pdf) of this series is located here: https://github.com/rod-trent/OpenAISecurity/tree/main/Must_Learn/Book_Version

The book will be updated when each new part in this series is released.

What is a Machine Learning attack against AI?

A Machine Learning (ML) attack against AI refers to the exploitation of vulnerabilities in artificial intelligence and machine learning systems by malicious actors. These attacks aim to manipulate, deceive, or compromise AI systems to achieve unintended outcomes or gain unauthorized access to sensitive information.

Types of Machine Learning attacks

There are several types of Machine Learning (ML) attacks against AI, each targeting different aspects of the system. Here are some common types of attacks:

Adversarial attacks: These involve creating carefully crafted input data (called adversarial examples) designed to mislead the AI system into making incorrect predictions or classifications. For example, an attacker could manipulate an image in such a way that the AI system would misidentify it.
Data poisoning: In this type of attack, an adversary injects corrupted or malicious data into the training dataset, causing the AI model to learn incorrect patterns or associations. The trained model could then produce incorrect or biased predictions when used in real-world applications.
Model inversion: This attack aims to reconstruct or infer sensitive information about the training data from the AI model's outputs, potentially leading to privacy breaches and unauthorized disclosure of confidential information.
Evasion attacks: These attacks focus on evading detection or bypassing security measures implemented by AI systems, such as spam filters, intrusion detection systems, or malware detectors. The attacker crafts input data to avoid triggering these security measures.
Model stealing: In this attack, an adversary uses the AI system's outputs to train a replica model without having direct access to the original training data or model architecture. The attacker can then use the replica model for their own purposes, potentially infringing on intellectual property rights or gaining unauthorized access to proprietary AI technology.
Membership inference attacks: In these attacks, an adversary aims to determine if a specific data point was used in the training dataset of an AI model. This can be a privacy concern, as it can reveal sensitive information about individuals who contributed to the training data.
Backdoor attacks: In this type of attack, the attacker inserts a hidden trigger or backdoor into the AI model during the training process. When the AI system encounters specific input data containing the trigger, it produces an incorrect or malicious output as desired by the attacker.

To defend against these attacks, it is essential to develop robust and secure ML algorithms and adopt best practices for data privacy and model protection.

Why it matters

Machine Learning (ML) attacks against AI can have several negative effects on AI systems, their users, and the organizations that rely on them. Some of these negative effects include:

Incorrect predictions or classifications: Adversarial attacks and data poisoning can cause AI systems to make incorrect predictions or classifications, leading to poor decision-making or unreliable outputs.
Privacy breaches: Model inversion and membership inference attacks can expose sensitive information about the training data or reveal private details about individuals who contributed to the training dataset, leading to privacy breaches and potential legal consequences.
Security breaches: Evasion attacks can bypass security measures implemented by AI systems, allowing malicious activities to go unnoticed and potentially causing significant damage to an organization's infrastructure, data, or reputation.
Intellectual property theft: Model stealing attacks can result in unauthorized access to proprietary AI technology, infringing on intellectual property rights and potentially causing financial losses or competitive disadvantages for the targeted organization.
System compromise: Backdoor attacks can enable attackers to control AI systems, causing them to produce malicious outputs or perform unauthorized actions when triggered by specific input data.
Loss of trust: The negative effects of ML attacks can erode users' and stakeholders' trust in AI systems, potentially leading to reduced adoption of AI technologies or increased skepticism about their reliability and security.
Financial and reputational damage: The consequences of ML attacks can result in financial losses, legal liabilities, and reputational damage for organizations that rely on AI systems. This can lead to loss of customers, reduced market share, or even business failure in extreme cases.

Real-world Example

One real-world example of a Machine Learning attack against AI is the adversarial attack demonstrated by researchers in the context of self-driving cars. In this example, the researchers targeted the AI-powered computer vision systems used by autonomous vehicles to recognize traffic signs.

The attack involved subtly altering a stop sign by placing stickers on it in a specific pattern. To a human observer, the altered sign still looked like a stop sign, but the AI system misidentified it as a different traffic sign, such as a speed limit sign. This kind of misclassification could have severe consequences in real-world scenarios, potentially causing accidents or traffic violations due to the self-driving car not recognizing the stop sign correctly.

This example highlights the vulnerability of AI systems to adversarial attacks and the importance of developing robust and secure algorithms to defend against such threats. It also emphasizes the need for ongoing research and collaboration between the AI community, industry, and policymakers to ensure the safe and reliable deployment of AI technologies in real-world applications.

How to Mitigate

Mitigating Machine Learning (ML) attacks against AI involves implementing various strategies and techniques to protect AI systems from potential threats. Some key approaches to mitigate ML attacks include:

Robust model training: Develop ML algorithms that are resistant to adversarial examples and data poisoning. Techniques like adversarial training, where models are trained on both original and adversarial examples, can improve the model's robustness. Regularization and data augmentation can also help in making models more resilient to attacks.
Data validation and preprocessing: Employ data validation and preprocessing techniques to filter out potential malicious inputs or outliers before they are used in training or fed into the AI system. This can help prevent data poisoning and adversarial attacks.
Model monitoring: Continuously monitor AI systems for potential vulnerabilities, unexpected behaviors, or sudden drops in performance that might indicate an attack. Implementing intrusion detection systems or anomaly detection techniques can aid in identifying and responding to potential threats.
Secure ML pipelines: Ensure that the entire ML pipeline, including data collection, storage, processing, and model deployment, is secure and follows best practices for data privacy and protection. Access control, encryption, and secure data sharing protocols can help safeguard against unauthorized access and data breaches.
Defense-in-depth: Adopt a multi-layered approach to security, combining various defense mechanisms to protect AI systems from different types of attacks. Techniques such as input validation, adversarial training, and output verification can work together to provide comprehensive protection.
Model interpretability and explainability: Develop AI models that are interpretable and explainable, making it easier to understand their decision-making processes and identify potential vulnerabilities or biases. This can help in detecting and mitigating the effects of ML attacks.
Collaboration and research: Encourage collaboration between AI researchers, industry experts, and policymakers to share knowledge, develop best practices, and establish guidelines for secure AI development and deployment. Ongoing research into ML attack detection and prevention is essential to stay ahead of potential threats.
Regular audits and updates: Conduct regular audits of AI systems to identify potential weaknesses and vulnerabilities. Keep AI models and security measures up-to-date to address newly discovered threats and ensure the system remains protected against evolving attack techniques.

By combining these approaches, organizations can significantly reduce the risk of ML attacks against AI systems and ensure the safe and reliable deployment of AI technologies in real-world applications.

How to monitor/What to capture

Detecting a Machine Learning (ML) attack against AI systems requires monitoring various aspects of the system's operation and performance. Here are some key elements to monitor for detecting potential ML attacks:

Model performance: Keep track of the AI system's performance metrics, such as accuracy, precision, recall, and F1 score. A sudden drop or unusual fluctuation in these metrics may indicate an ongoing attack or a compromised model.
Input data: Monitor the input data fed into the AI system for anomalies, outliers, or unexpected patterns that might be indicative of adversarial examples or data poisoning attempts. Implementing data validation and preprocessing techniques can aid in detecting and filtering malicious inputs.
System behavior: Observe the AI system's behavior, particularly its predictions or decisions, for any unusual or unexpected outcomes that might suggest a successful attack. This can include misclassifications, incorrect predictions, or biased decision-making.
Model outputs: Analyze the AI model's outputs, paying close attention to instances where the model produces results with low confidence or high uncertainty. These cases could indicate adversarial attacks designed to confuse the AI system.
Log files and usage patterns: Inspect system log files and usage patterns for signs of unauthorized access, data breaches, or unusual activity that could be associated with an attacker attempting to compromise the AI system or gain information about the model.
Network activity: Monitor network activity for any unusual traffic patterns or communication with suspicious IP addresses, which could indicate an attempt to exfiltrate data, inject malicious code, or perform a model-stealing attack.
Infrastructure and resource usage: Keep an eye on the AI system's infrastructure and resource usage, such as CPU, memory, and storage consumption. An unexpected spike or change in resource usage could signal an attack or a compromised component within the system.
Alerts and notifications: Set up alerts and notifications for specific events or thresholds that might indicate a potential ML attack, such as a sudden drop in model performance, a high number of anomalous inputs, or unusual network activity.

By continuously monitoring these aspects of an AI system and implementing effective detection mechanisms, organizations can proactively identify and respond to potential ML attacks, thereby reducing the risk of damage and ensuring the reliability and security of their AI applications.

[Want to discuss this further? Hit me up on Twitter or LinkedIn]
[Subscribe to the RSS feed for this blog]
[Subscribe to the Weekly Microsoft Sentinel Newsletter]
[Subscribe to the Weekly Microsoft Defender Newsletter]
[Subscribe to the Weekly Azure OpenAI Newsletter]
[Learn KQL with the Must Learn KQL series and book]
[Learn AI Security with the Must Learn AI Security series and book]