Adversarial AI Attacks: How Hackers Exploit Machine Learning Models

What is adversarial AI? Adversarial AI attacks are methods and techniques used to manipulate artificial intelligence (AI) and machine learning (ML) models, causing them to make incorrect predictions.

Machine learning models have experienced a 26% increase in adversarial AI attacks, primarily affecting spam filters and fraud detection.

This threat marks a new stage in cybersecurity, as it impacts AI and ML models in fundamental ways. These models scale and adapt automatically, and attacks are often unpredictable and hard to detect.

As a result, tech companies invest in robust cybersecurity measures, develop new defense strategies, and hire top-tier developers — driving up the cyber security salary for these specialists.

How Hackers Exploit Machine Learning Models

There are two major types of AI adversarial attacks. White-box attacks refer to scenarios where the model’s design, parameters, and training data are fully accessible to the attacker.

Black-box adversarial attacks on AI mean that a hacker has limited knowledge of the system and can’t access its architecture.

Evasion Attacks

Hackers can use generative adversarial networks (GANs)-based AI to manipulate data and deceive the AI systems. They generate images, videos, text, and audio that resemble authentic data.

Consequently, ML models misclassify information and make incorrect predictions. Usually, these adversarial AI and ML attacks occur after the model has been trained. 

Adversarial AI examples:

• In the automotive industry, attackers can slightly modify road signs with stickers or other means, allowing AI to misclassify a limit sign as a stop sign. Additionally, they can modify image pixels so that an obstacle is misclassified as a pedestrian.
• Adversarial attacks in AI can affect spam filtering. For instance, if these filters can recognize the word “money,” but fail to flag e-mails with the misspelled word “m0ney” as spam. Consequently, spam filters fail and phishing emails reach more users.
• Adversarial AI in cybersecurity refers to hackers modifying malware code slightly so that it appears safe to antivirus software but remains dangerous.

Poisoning Attacks

These attacks typically occur during the training phase. Hackers modify data to reduce accuracy, cause bias, and impair overall performance. These changes may be subtle so that a human can’t detect incorrect data, or more severe in case of backdoor attacks.

These adversarial AI and machine learning attacks add a harmful trigger to the model’s training data. The AI works normally most of the time, but it acts differently whenever this signal appears.

Examples of adversarial attacks on AI systems:

• These attacks have a significant impact on the e-commerce sector, as they generate fake reviews to circumvent AI filtering systems. They usually use semantic manipulation (e.g., “This laptop is potent, and the screen size and resolution are top” instead of “The best value for money), text perturbation (e.g., “Worst purch@se ever” instead of “worst purchase ever”), and manipulating ratings (e.g., Systems can generate reviews like “I absolutely love this phone! The battery life is impressive, and the quality of the speakers in top-notch” but give the product a 1-star rating).
• Attackers can manipulate data that impedes the operation of Google’s anti-spam filters.
• Cyber adversaries can create bots that generate offensive responses and statements, as happened to the Twitter chatbot.

Model Extraction Attacks

These adversarial machine learning attacks define machine learning vulnerabilities and aim to analyze the existing model’s architecture and parameters to replicate it fully. Hackers utilize the outputs of the original model to train the surrogate model. This leads to the theft of sensitive information, violates user privacy, and compromises service security.

Adversarial AI security risks examples:

• These attacks on AI in cybersecurity can evade access restrictions, such as password theft and session hijacking, which may compromise the service’s stability.
• Data extraction can lead to data leakage in medical or financial services, thereby raising significant privacy concerns.
• Hackers can attack trading models and manipulate inputs to cause financial losses.

Inference Attacks

These adversarial AI attacks target specific data used to train models, and this issue is particularly relevant for large language models (LLMs). Malicious actors use specific queries that extract sensitive and private information.

Adversarial AI examples:

• Threat actors can steal sensitive patient data from medical records or track a person’s hospital visits.
• Specific data (e.g., location, behavior, health issues) can be obtained from social media platforms.
• Smart meter data on energy use, which provides comprehensive insight into domestic activities, can be exfiltrated.

Why These Attacks Are a Serious Cybersecurity Concern

Today, AI is used in security operations centers (SOCs) to detect threats, forecast potential attacks, and take automated action. In anomaly detection, AI systems continuously monitor and analyze data streams, conducting a behavioral analysis to identify anomalies early in the process.

Additionally, AI is widely utilized in biometrics and fraud prevention, as it ensures authentication and access control, while also identifying fraudulent patterns.

However, the effectiveness of these systems is questioned by adversarial AI attacks that manipulate data, undermining their reliability and correctness, and leading to specific AI security risks.

• Unauthorized access to sensitive data and its theft by bypassing security protocols.
• The usage of machine learning vulnerabilities to break security measures and cause serious breaches.
• Manipulating biometric data to get unauthorized access to various platforms and services and steal personal, financial, or medical information.
• Bypassing control systems and malware filtering by these attacks can lead to downtime, compliance violations, reputational damage, and significant financial losses.

Hence, these attacks cannot only compromise the reliability of specific solutions but also the overall effectiveness of AI-driven cybersecurity systems.

“This is a security problem. This is a confidentiality problem. But it is much more an integrity problem. And that integrity is going to be the primary security challenge for AI systems of the future,” says Bruce Schneier, an internationally renowned security technologist.

Defense Strategies Against Adversarial AI

Adversarial attacks may collect data, steal algorithms, or alter the predictions of machine learning models. Therefore, tech companies should invest heavily in defending against AI-powered attacks and advancing the robustness of machine learning systems.

Adversarial Training

Adversarial training in cybersecurity is one of the most popular defense mechanisms for reinforcing ML models and making them less susceptible to attacks.

Tech professionals simulate the actions of attackers and create adversarial examples using standard techniques such as misclassification and perturbation.

Then, these examples are added to the training data. Models trained in this manner can effectively handle these attacks. However, this method may be expensive.

Gradient Masking

Gradient masking shields AI models from hostile attacks by concealing or obfuscating the information attackers need to trick the system.

Malicious actors typically rely on gradients to subtly modify the text or image. Noisy, unclear, and less smooth gradients make it harder for attackers to figure out what changes to make.

This strategy can be applied in computer vision systems to help AI resist trick inputs. However, according to the National Institute of Standards and Technology (NIST), this strategy can be bypassed, and it’s not a reliable defense on its own.

Data Encryption

To prevent unauthorized access and data manipulation, training data can be encrypted using homomorphic encryption. Combined with output perturbation (adding noise to the model output), it can be used to prevent leakage of sensitive data, especially in fintech and healthcare sectors.

Monitoring and Anomaly Detection

Integrating anomaly detection tools into ML models ensures constant monitoring of data flows and spotting unusual patterns, and financial institutions widely use them.

These tools send alerts when the model’s predictions or data change, immediately launching mitigation actions. However, these means should also be appropriately trained to avoid overreacting or missing real threats.

Conclusion

Thus, adversarial AI attacks pose serious threats to AI systems, causing them to make false predictions and misclassifications, which ultimately lead to significant damage to a business’s reputation and financial losses.

Companies should invest in AI security just as they do in traditional cybersecurity to ensure the integrity of AI models. Ongoing monitoring and innovation are crucial in mitigating adversarial attacks on AI.