What Is Adversarial AI in Machine Learning?

3 min. read

Artificial intelligence (AI) has made significant strides in transforming various industries in recent years. From autonomous vehicles to medical diagnostics, AI-powered systems have shown remarkable capabilities. However, as AI becomes more integrated into business tools and functions, adversarial AI has quickly emerged as a new threat.

Adversarial AI manipulates ML systems by creating inputs that misinterpret data. This challenges cybersecurity professionals who must develop advanced defense mechanisms against these threats.


Understanding Adversarial AI in Machine Learning

Adversarial AI, also known as adversarial attacks or AI attacks, is a facet of machine learning that involves malicious actors deliberately attempting to subvert the functionality of AI systems. Adversarial attacks are crafted to deceive AI systems, causing them to make incorrect or unintended predictions or decisions. Threat actors introduce attacks in the input data, altering the original data or the AI model itself by changing the parameters or architecture.

Adversarial AI has recently gained increasing attention and concern due to its potential to cause significant harm. Attackers can use adversarial AI to manipulate autonomous vehicles, medical diagnosis systems, facial recognition systems, and other AI-powered applications, leading to disastrous outcomes.

Researchers have been developing defensive mechanisms to mitigate the risk of adversarial AI attacks. These include adversarial training, where the AI model is trained with adversarial examples to increase its robustness, and defensive distillation, which involves training a secondary model to detect adversarial examples. Despite these efforts, adversarial AI remains a significant challenge, and there is a need for continued research to develop more effective solutions.


Adversarial AI Vs. Conventional Cybersecurity Threats

What sets adversarial AI apart from conventional cybersecurity threats is its intricate approach. Instead of directly attacking the system's infrastructure or exploiting known software vulnerabilities, Adversarial AI operates more abstractly. It capitalizes on the very essence of AI, which is to learn and adapt from data, by introducing subtle perturbations that appear innocuous to human observers but are specifically engineered to confound the AI's decision-making process.

How Do Adversarial AI Attacks Work?

Adversarial attacks exploit the vulnerabilities and limitations inherent in machine learning models, particularly deep neural networks. These attacks manipulate input data or the model itself to cause the AI system to produce incorrect or undesired outcomes. Adversarial AI and ML attacks typically follow a four-step pattern that involves understanding, manipulating, and exploiting the target system.

Step 1: Understanding the Target System

Cybercriminals who want to launch an adversarial AI attack must understand how the target AI system works. They do this by analyzing the system's algorithms, data processing methods, and decision-making patterns. To achieve this, they may use techniques like reverse engineering to break down the AI model, looking for weaknesses or gaps in its defenses.

Step 2: Creating Adversarial Inputs

Once attackers understand how an AI system works, they can create adversarial examples. Adversarial examples are inputs intentionally designed to be misinterpreted by the system. For example, attackers could slightly alter an image to deceive an image recognition system or modify the data fed into a natural language processing model to cause a misclassification.

Step 3: Exploitation

Attackers then deploy adversarial inputs against the target AI system. The goal is to make the system behave unpredictably or incorrectly. This could range from making incorrect predictions or classifications to bypassing security protocols.

Adversarial attacks exploit the weaknesses and sensitivities of machine learning models to cause them to make incorrect predictions or decisions. Changes to the input data can significantly affect the model's output. Attackers use gradients to understand how these changes affect the model's behavior and create adversarial examples specifically designed to deceive the model.

The examples are then used to exploit the model's weaknesses, causing it to produce incorrect results. The goal of adversarial attacks is to undermine the trustworthiness and dependability of the targeted AI system. For example, the infamous SolarWinds attack — while serious — had a manual command-and-control backend. With AI, attackers could have automated the command and control for greater impact.

Step 4: Post-Attack Actions

Depending on the context, the consequences of adversarial attacks can range from the misclassification of images or text to potentially life-threatening situations in critical applications like healthcare or autonomous vehicles.

Adversarial attacks exploit the fact that machine learning models can be susceptible to minor variations in input data, which humans might never notice. Defending against these attacks requires robust model architectures, extensive testing against adversarial examples, and ongoing research into adversarial training techniques to make AI systems more resilient.


Types of Adversarial Attacks

White-Box Vs. Black-Box Attacks

Adversarial attacks can be categorized into white-box and black-box attacks. White-box attackers have complete knowledge of the AI model's architecture, while black-box attackers have limited information. The level of knowledge significantly impacts the success of the attack.

Evasion Attacks

Evasion attacks occur when input data is manipulated to deceive AI models. For example, adding invisible changes to an image can cause an AI system to misidentify it. These attacks can seriously affect image recognition and security systems, where accurate predictions are essential.

Evasion attacks are typically categorized into two subtypes:

  • Nontargeted attacks: In nontargeted evasion attacks, the goal is to make the AI model produce any incorrect output, regardless of the output. For example, an attacker might manipulate a stop sign image so that the AI system fails to recognize it as a stop sign, potentially leading to dangerous road situations.
  • Targeted attacks: In targeted evasion attacks, the attacker aims to force the AI model to produce a specific, predefined, incorrect output. For instance, they might want the model to classify a benign object as harmful, leading to potential security breaches or false alarms.

Evasion attacks can be very tricky since they take advantage of the particular characteristics or patterns an AI model has picked up during its training. These attacks usually employ optimization techniques to identify the most efficient changes that can mislead the model while still being undetectable by human onlookers.

Poisoning Attacks

Poisoning attacks represent a more sophisticated and subtle form of Adversarial AI. In these attacks, malicious actors don't directly target the deployed machine learning model but instead manipulate the training data used to create the model. The idea behind poisoning attacks is to inject tainted data into the training dataset so that it subtly distorts the model's understanding of the underlying patterns in the data.

Transfer Attacks

Transfer attacks represent a unique challenge in the realm of Adversarial AI. Unlike other attacks that specifically target a single AI system, transfer attacks involve the creation of adversarial models for one system and their adaptation to attack other AI models.

Versatility and Risks

Once one system is compromised, adversarial attacks can take over multiple AI systems with similar functionalities. This showcases the adaptability and versatility of these techniques. Defending against Adversarial AI requires a comprehensive approach to security that considers vulnerabilities across different systems.


How to Defend Against Adversarial AI

The complex nature of Adversarial AI and ML threats requires a multifaceted, multilayered, proactive cybersecurity approach that encompasses technological solutions combined with organizational and educational strategies. The goal is to create a solid, resilient framework capable of detecting and preventing attacks and enabling teams to respond swiftly and effectively when they occur.

Related Article: Understanding AI Security Posture Management (AI-SPM)

Prevention and Detection

Prevention and detection are the front lines in the battle against adversarial AI attacks. This involves implementing advanced AI security measures that can recognize and neutralize adversarial inputs before they can affect the system. Important techniques include resilient machine learning models, which are less sensitive to adversarial manipulation, and anomaly detection systems that can identify unusual patterns or inputs.

Continuous Monitoring

Continuous monitoring of AI systems for unexpected behavior or outputs can help in the early detection of adversarial attacks. Cyber teams can use encryption and secure access to AI models and datasets to prevent unauthorized manipulation or extraction.


Education involves specific and targeted training programs for cybersecurity teams, AI developers, and all organizational staff. For cybersecurity professionals and AI developers, specialized training should focus on understanding the nature of adversarial attacks, identifying potential vulnerabilities in AI systems, and learning about the latest techniques for building robust ML models.

For the broader organizational staff, awareness programs should be about the basics of AI security, the importance of data integrity, and each individual's role in maintaining secure AI systems.

By ensuring that all levels of the organization are well-informed and vigilant, cyber teams can create a more comprehensive defense against adversarial AI and ML, making it harder for these sophisticated attacks to penetrate the organization's digital infrastructure.

Vulnerability Self-Assessments

Vulnerability self-assessments are critical for understanding and strengthening an organization's defenses against adversarial AI attacks. This involves regularly testing AI systems to identify vulnerabilities that adversarial attacks could exploit. Cyber teams can use tools and methodologies like red team exercises, penetration testing, and scenario-based assessments to simulate adversarial attacks and evaluate the resilience of AI systems.

These assessments should cover data integrity, model robustness, and system response to adversarial inputs. The insights gained from these assessments should guide the ongoing refinement and fortification of AI security strategies.

Adversarial AI poses a significant challenge in the realm of machine learning. As AI continues to evolve, so do the tactics of those seeking to exploit it for nefarious purposes. Security leaders must understand the nature of these attacks, their real-world implications, and how to defend against them.


Adversarial AI in Machine Learning FAQs

Attackers may have various motives, including financial gain, personal vendettas, or even nation-state interests. Adversarial AI provides a means to manipulate AI systems for their benefit or to disrupt critical infrastructure.
Achieving complete immunity to adversarial attacks is incredibly difficult, if not impossible. However, ongoing research and development in adversarial training and robust AI models aim to significantly reduce vulnerabilities.
Engaging in adversarial AI attacks can lead to severe legal consequences, including imprisonment and substantial fines. Laws and regulations are evolving to address this emerging threat.
Organizations can protect themselves by implementing robust security measures, staying updated on the latest research, and collaborating with cybersecurity experts to defend against attacks proactively.
Ethical hackers and security researchers play a crucial role in identifying vulnerabilities and developing countermeasures against adversarial AI. Their efforts help strengthen the security of AI systems.