What is Adversarial AI?

Adversarial AI refers to the practice of deliberately manipulating inputs to AI systems to cause them to make incorrect predictions or classifications. The concept emerged from the realization that AI systems don't perceive the world the same way humans do. While a human might barely notice a few altered pixels in an image, these tiny changes can completely fool an AI system into misclassifying what it sees. This disconnect between human perception and machine perception forms the foundation of adversarial attacks.

However, the threat extends beyond technical vulnerabilities. As William Dixon from the Royal United Services Institute warns in the World Economic Forum article, "Ultimately, the real danger of AI lies in how it will enable attackers." This perspective includes not just the technical manipulation of AI systems, but the strategic weaponization of AI capabilities by malicious actors.

Dixon defines adversarial AI as "the malicious development and use of advanced digital technology and systems that have intellectual processes typically associated with human behaviour." This definition captures the evolution from simple input manipulation to AI-powered attack systems that can learn, adapt, and operate autonomously.

MIT CSAIL Principal Research Scientist Una-May O'Reilly provides additional context, explaining that adversarial intelligence encompasses more than just technical manipulation. According to O'Reilly, "Think of the specialized, nefarious intelligence that these attackers marshal — that's adversarial intelligence. The attackers make very technical tools that let them hack into code, they choose the right tool for their target, and their attacks have multiple steps."

The definition of adversarial AI continues to change. According to an article published by StateTech Magazine, "What we're hearing more about now is adversaries using AI for malicious activity," representing a shift from traditional academic research to real-world criminal applications.

​

How adversarial attacks work

According to the Department of Homeland Security's analysis of adversarial AI threats, "An evasion attack occurs during inference and is a deliberate and malicious manipulation of an AI-based system's input data." These attacks involve adding small modifications to input data that cause AI models to produce incorrect outputs. These modifications, known as adversarial perturbations, are calculated specifically to exploit the mathematical properties of neural networks.

What makes these attacks concerning is their subtlety. According to DHS research, "These inputs to AI-based systems can be 'camouflaged' by very small perturbations that are imperceptible to the human eye." This capability makes detection challenging for human operators and traditional security systems.

However, as noted by Patrick Hinton from The Alan Turing Institute, there's an important distinction between laboratory research and real-world implementation: "Most research to date on the topic of adversarial camouflage has taken place in a sterile environment." This observation shows the gap between controlled academic studies and the practical challenges of deploying these attacks in real-world scenarios.

Dixon notes in the World Economic Forum article that, "Criminals are already harnessing automated reconnaissance, target exploitation and network penetration end-to-end." This represents a change from manual attack methods to fully automated assault systems.

The sophistication of modern attackers adds another layer of complexity. As noted by O'Reilly, "For the sophisticated APTs, they may strategically pick their target, and devise a slow and low-visibility plan that is so subtle that its implementation escapes our defensive shields. They can even plan deceptive evidence pointing to another hacker!"

The mathematics behind these attacks often involves gradient-based optimization techniques. Attackers use the model's own learning mechanism against it, calculating the exact changes needed to push the model's decision boundary in a desired direction. Popular attack methods include the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and the Carlini & Wagner attack, each with different levels of effectiveness.

Modern cybercriminals are leveraging these principles in new ways. As reported by StateTech, "Artificial intelligence allows adversaries to basically recode malware very quickly," demonstrating how AI acceleration is being weaponized for malicious purposes.

​

Types of adversarial attacks

Adversarial attacks can be categorized along several dimensions. As established in the research Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, white-box attacks occur when the attacker has full knowledge of the target model's architecture, parameters, and training data. This complete information allows for an effective attack but represents a worst-case scenario that may not always reflect real-world conditions.

Black-box attacks, on the other hand, assume the attacker has limited or no knowledge of the target model's internals, as noted in the same research. These attacks often rely on transferability – the property that adversarial examples crafted for one model often work against other models trained on similar tasks. This transferability makes black-box attacks particularly concerning for deployed AI systems.

The research further defines cross-technique transferability between models trained using different machine learning techniques, explaining that it measures "the proportion of adversarial samples produced to be misclassified by model i that are also misclassified by model j." This finding demonstrates that adversarial examples can successfully transfer across different AI architectures.

Targeted attacks aim to make the model output a specific incorrect classification, while untargeted attacks simply seek to cause any misclassification. Physical attacks involve creating adversarial examples that work in the real world, accounting for factors like lighting conditions, camera angles, and printing limitations.

According to Hinton's analysis, the practical requirements for effective physical attacks can be substantial. In his research, he found that "the adversary would need to print or paint adversarial patches the size of football fields to be truly deceptive," which limits the practical application of such tactics to stationary high-value targets.

​

Data poisoning attacks

Beyond evasion attacks, data poisoning represents another critical vulnerability. According to DHS analysis, "These attacks are most successful when the training data is nonstationary." This occurs when attackers inject malicious data into training datasets, particularly in systems that require continuous learning and periodic retraining. The contaminated training data can create backdoors or bias the model's decision-making process while appearing to function normally under most conditions.

The evolving threat landscape

The World Economic Forum identifies three ways adversarial AI will transform the threat landscape:

• Larger volume of attacks: By introducing scalable systems that previously required human expertise, criminals can invest resources into building and modifying new infrastructure against target sets.

• New pace and velocity: As Dixon explains, "Technology will enable criminal groups to become increasingly effective and more efficient." This allows attackers to finely tune attacks in real-time, adapt to their environment, and learn defense postures faster.

• New varieties of attacks: Most importantly, AI enables attack methodologies that were previously unfeasible, potentially bypassing entire generations of existing controls.

Defensive strategies and solutions

Adversarial training involves training models on both clean and adversarial examples, helping them learn to recognize and resist manipulated inputs. While effective, this approach comes with costs and may reduce performance on clean data.

The defensive landscape presents both challenges and reasons for optimism. As Hinton notes, "Research has found that there is no defence that cannot be overcome by a specialised attack," highlighting the ongoing nature of this security challenge. Yet he also provides a more balanced perspective, observing that "adversarial attacks are inherently brittle and appropriate pre-processing and well-designed models can effectively mitigate most effects."

As Dixon notes in the World Economic Forum article, "It is impossible to defend an organization from all attacks across all channels all the time." This reality necessitates a strategic approach to defense prioritization and resource allocation.

For organizations defending against modern AI-powered threats, experts recommend comprehensive approaches. As noted in the StateTech article, "You need AI-type systems at the network level, doing real-time analysis of everything," emphasizing the importance of AI-powered defensive solutions to counter adversarial AI attacks.

O'Reilly's research takes a unique approach to strengthening defenses by studying the attackers themselves. According to O'Reilly, "My research goal is to replicate this specific kind of offensive or attacking intelligence… I use AI and machine learning to design cyber agents and model the adversarial behavior of human attackers."

This research approach offers practical benefits for cybersecurity. As noted by O'Reilly, "Adversarially intelligent agents (like our AI cyber attackers) can be used as practice when testing network defenses… when we add machine learning to our agents, and to our defenses, they play out an arms race we can inspect, analyze, and use to anticipate what countermeasures may be used."

​

The strategic response

The World Economic Forum outlines five areas where the cybersecurity community must respond to adversarial AI threats:

• Understanding the current threat environment: Organizations must develop a deep understanding of where adapting threats will challenge current postures and where to focus limited resources.

• Investing in skills and analytics: Government, business, and education systems need to ensure they have the requisite skills and talent pipelines for advanced computing, mathematics, and orchestrated analysis.

• Investing in suppliers and third parties: Full-scale defense requires integrated services and partners who can actively manage and refine defenses in line with the changing threat landscape.

• New operational policy partnerships: AI-driven attacks will require deeper partner bases and proactive strategies with cross-sector partners, regulators, and governments.

• Integrating business processes: New generation attacks exploiting defensive gaps will pressure traditional operational silos.

FAQs

Can adversarial AI attacks target AI in non-digital physical systems, like robots or autonomous vehicles?

Yes, adversarial AI can exploit sensors and control systems in real-world robotics and autonomous vehicles to induce unsafe or unintended behaviors.

How are attackers funding or monetizing adversarial AI campaigns?

Attackers may profit via ransomware, financial fraud, industrial sabotage, or by selling exploits on the dark web.

Can adversarial AI be used for beneficial purposes?

Yes, security researchers and organizations use adversarial AI offensively in a controlled manner to test and harden AI systems.

How do attackers obtain access to model parameters for white-box attacks?

They may exploit leaks, insider threats, reverse-engineering, or poorly secured APIs to gain model knowledge.

Are all AI models equally vulnerable to adversarial attacks?

No, vulnerability varies by model architecture, training data, regularization techniques, and deployment environment.