What Is Adversarial AI?

With artificial intelligence (AI) and machine learning (ML) increasingly embedded in organizations’ critical systems. Adversarial AI, which seeks to sabotage its decisions, is an increasing challenge. We’ve looked at Dark AI, which uses AI in malicious ways, but in this article, we’ll dig into the distinct concept of Adversarial AI, where AI systems are the target of attacks. Here, too, AI is often behind attackers’ methods, successes, and the keys to identifying and thwarting their efforts. 

What is Adversarial AI/ML?

Adversarial AI includes the techniques used to manipulate and deceive AI systems by exploiting their vulnerabilities. Attackers can feed AI models their own incorrect data designed to elicit off-the-mark predictions, impacting an organization’s critical AI-based applications. Types of Adversarial AI attacks include:

• Evasion attacks: Altering the input data to deceive the AI system without noticeable changes for humans to make model answers incorrect.
• Poisoning attacks: Injecting faulty data into the training dataset to ensure the AI’s learning process is problematic from the start, and its future predictions incorrect.
• Backdoor attacks: Planting a hidden access portal into an AI model to control its output in specific situations.
• Transfer attacks: Creating adversarial examples on a source model, then using them to deceive a different AI model, impacting another company entirely.
• Model inversion attacks: Reverse engineering or extracting sensitive information from a trained AI model seeking its previous predictions and inputs from users.
• Membership inference attacks: Attempting to determine which data was used in the AI’s original training set, aiming to gain sensitive information back out of the model.
• Denial-of-Service (DoS) attacks: Flooding the AI system with data to make it unreliable or to cause an outright crash.

Types of Adversarial AI Attacks

AI models aren’t inherently more vulnerable than other organizational assets, but they may be more crucial for overall success. After all, innovating in AI can be the key to organizational progress, and attacks on AI can set back innovation disproportionately compared to attacks on less dynamic and technologically advanced assets. Perhaps that’s why AI breaches are common.

Almost three-quarters of organizations saw their AI systems breached in 2024, and attacks on AI are increasing yearly.

With so many breaches of innovative company technology, AI breaches can have an outsized impact. AI needs protecting; a considerable 89% of companies say the AI models in production are crucial to their success. So what does it take to safeguard them? It starts with knowing common attack patterns.

Here’s a deeper look at examples of each, with tools and techniques used to combat them.

Evasion Attacks

In an evasion attack, problematic inputs may be undetectable to humans. They trick machine learning models, for instance, by altering a text or sensor data so that the model identifies it incorrectly. What does that look like? For an organization with an AI fraud detection system built into payment processing, an attacker could manipulate transaction amounts or locations in such a way as to trick the model into not flagging the transaction. While a human would notice there’s an irregularity, the AI model no longer can. 

What can teams do about evasion attacks? AI-powered behavioral analysis tools can monitor and flag unusual patterns — including unusual patterns in inputs — before AI begins malfunctioning.

Poisoning Attacks

When an AI model’s entire learning process is sabotaged, all its outputs are compromised. For example, a company’s machine learning for customer segmentation could be made to miscategorize all customers, leading to poor marketing targeting and wasted advertising spend.

ML-powered anomaly detection helps subvert poisoning attacks, checking for unusual patterns in training data. But during the training phase, they’ll often come from different tools like data anomaly detection tools, outlier detection algorithms, and integrity monitoring tools.

Backdoor Attacks

AI models attacked by backdoor actors may act normally in most circumstances, but when a specific trigger is activated, like a facial expression, the system misidentifies the intruder and allows access.

Regular model audits, using AI-based interpretability tools to analyze decision-making, can help uncover backdoors or unexpected dependencies. And AI-enhanced sanitization systems can filter suspicious inputs before they interact with the model. That means less chance that hidden triggers will ever activate a backdoor.

Transfer Attacks

Attackers can create malicious data for one AI model, then use it to trick another model. Because many AI models use similar architectures and data, what works on one may similarly fool the other. The approach can completely upend the second model’s outputs, and the similarity between models means that transfer attacks are a way for cyber attackers to spread their malicious activity across companies and systems easily.

To guard against it, try using different AI models for different tasks, to make it harder to exploit a weakness in one of the models and apply it to the next. And use “adversarial training,” a technique in which AI models are trained to recognize and defend against malicious examples like the ones used in transfer attacks.

Model Inversion Attacks

When companies train AI on sensitive data, attackers are likely to try to extract that data by fooling AI models into handing it over. So in the case of a company using AI to predict patient outcomes, attackers may exploit the model to infer sensitive details about patients’ health conditions. 

Implement AI-based privacy-preserving tools so individual data points can’t be extracted from the model. Use AI-based access management to limit who can interact with the model, and set up AI-based query monitoring, which can flag suspicious queries that indicate attempts at model inversion.

Membership Inference Attacks

Attackers can query models to reveal other kinds of sensitive data from their training materials. The goal is to expose private or proprietary information, including customer data.

Employ privacy audits to test your system and see how well it protects personal data. Restrict access with AI-powered access controls to detect suspicious attempts to query the model, and use AI-powered data privacy-preserving models with techniques like federated learning or secure multi-party computation (SMPC).

Denial-of-Service Attacks (DoS)

DoS attacks flood the system until it gives nonsensical answers or crashes. So for example, a company using AI for inventory management may find a flooded system interrupts supply chain operations.

To counter the onslaught, use AI-enhanced traffic filtering to find and block unusual spikes in input data or requests, lowering the chances a DoS attack can permeate the system with requests. Load balancing can also distribute incoming traffic to prevent overload on any single component of the AI infrastructure. Finally, real-time monitoring and anomaly detection tools can observe system behavior. That makes for faster identification and mitigation, before the system collapses entirely.

Understanding Adversarial AI Attack Methods

These attack types define the general nature of an Adversarial attack. But to guard against them, teams also need to look at the actual techniques used to carry them out. These methods help point to specific vulnerabilities in AI systems that could be exploited and, hence, how teams can take action against each.

Attack Method	Description	Target	Characteristics
Limited-Memory BFGS (L-BFGS)	Optimizes minimal perturbations to mislead the model.	Evasion attack	Subtle, optimization-based attack
FastGradient Sign Method (FGSM)	Perturbs input using the model’s gradient to cause misclassification.	Evasion attack	Fast, strong, targeted adversarial examples
Jacobian-based Saliency Map Attack (JSMA)	Targets influential features based on a saliency map.	Evasion attack	Focuses on specific features to mislead the model
Deepfool Attack	Finds the smallest perturbation to cross the decision boundary.	Evasion attack	Iterative, minimal perturbations
Carlini & Wagner Attack (C&W)	Minimizes perturbations while ensuring misclassification.	Evasion attack	Powerful, hard to defend against
Generative Adversarial Networks (GAN)	Generates adversarial examples using a second neural network.	Poisoning and evasion attacks	Uses generative approach to craft adversarial inputs
Zeroth-order Optimization Attack (ZOO)	Optimizes inputs without needing gradient access, often in black-box scenarios.	Evasion attack	Works when model gradients aren’t available, effective in black-box settings

Most methods are evasion attacks because they directly alter inputs to fool a model during inference, after it’s trained.  But GAN is a poisoning attack as well; it generates adversarial inputs (evasion), but also generates malicious code that could be used to corrupt training sets (poisoning).

Ultimately, the methods used in Adversarial AI are not created equal. And teams will need to adjust strategies and tools based on the attack methods and techniques most relevant to their organizations, based on the AI systems they’re using, from image recognition to fraud detection or recommendation systems. 

Evasion attacks are particularly relevant for models that make decisions in real time, like fraud detection. Those models benefit from runtime behavior analysis that can spot anomalous activity as it happens and shut down an attack in progress.

Neural network-based models might be more susceptible to methods like FGSM and Deepfool. With adversarial training to help the model learn to recognize and resist adversarially perturbed images, organizations can guard against attacks that take advantage of this model’s high dimensionality and tendency to overfit.

And for black-box scenarios, where attackers have a limited view into the AI system they’re targeting, methods like ZOO are more relevant. If organizational models aren’t fully exposed, those kinds of attacks could be more of a concern.

How do these specific techniques impact the tips and tools we’ve already covered? They allow teams to update security strategies and focus on implementing defenses against their most likely adversaries first.  

Implementing a Defense Strategy Against Adversarial AI: A Checklist for Teams

Protecting AI models is always going to require a multi-layered strategy that is tailored to defend against the unique threats of both Adversarial AI and an organization’s own AI assets. Organizations must first assess their risks based on the AI systems they deploy, accounting for the attack methods that are most likely to impact them. Regardless of prioritizing specific defenses, here is the step-by-step path all teams will take:

Checklist for Defending Against Adversarial AI:

1. Conduct regular risk assessments to identify the most likely attack methods.
   Commit to keeping security measures aligned with evolving threats and model usage, since the ideal Adversarial AI strategy is constantly evolving with an organization’s tech stack.
2. Implement adversarial training to teach models to recognize malicious inputs, so models learn to distinguish between normal and adversarial data.
3. Use behavioral analysis tools for real-time detection of abnormal input patterns. Make sure that deviations from baseline behavior lead to immediate alerts when potential attacks are detected.
4. Apply data anomaly detection and outlier detection when training datasets.
   Identify outliers and suspicious data so poisoning attacks can’t corrupt the model’s learning.
5. Employ strong model validation techniques, especially for organizations at high risk of transfer attacks.
6. Limit access to training data so only trusted users can modify it. This step is particularly important for organizations where training manipulation is a core concern.
7. Monitor for unexpected model behavior with continuous runtime monitoring so that sudden performance degradation or misbehavior is caught before damage spreads.
8. Apply model interpretability tools to identify hidden vulnerabilities before they’re exploited.
9. Perform regular model audits to detect existing backdoors and verify that the model is resistant to backdoor attacks.
10. Sanitize inputs before they reach the model, filtering out potential threats before they can affect a model’s behavior.
11. Use ensemble models to increase resistance to adversarial examples. They combine predictions from multiple models, so it’s harder for adversarial examples to succeed across all systems.
12. Enable real-time traffic filtering to prevent DoS attacks. Filtering tools block excessive or suspicious input before it overloads the system.
13. Utilize access control and query monitoring to make sure that only authorized users can query sensitive models.
14. Stay updated on emerging AI defense technologies and research.
    New AI defense techniques emerge at the speed of AI. Teams will want to incorporate evolving strategies as attackers change their own.

The Future of Adversarial AI Attacks

What’s in the cards for Adversarial AI? 

In the future, adversarial self-supervised learning will enhance the strength of models. They’ll recognize underlying patterns without labeling data, and they’ll help companies become more resistant to adversarial attacks, so they’ll rely less and less on manually curated datasets in the future. That will save the laborious work of creating large, labeled datasets as it improves the generalization capabilities of AI models, making them less vulnerable to Adversarial attacks. 

Quantum computing is also emerging as a key tool in AI security. Quantum algorithms can detect and neutralize AI attacks with much faster detection, learning models that are more resilient to attacks, and quantum cryptography that keeps bad actors from manipulating the data used to train AI models in the first place.

Automated adversarial attack detection systems are already a reality. They’re designed to detect adversarial inputs in real time using machine learning algorithms. But future models will integrate more deep learning techniques to detect more complex and subtle adversarial attacks. They’ll be able to detect the most sophisticated perturbations in data, learning from vast datasets. They’ll also evolve to spot new types of attacks without human intervention. They’ll also have the ability to correct or mitigate the damage as it happens with self-healing mechanisms.

Advanced Threat Detection is Here Today with Upwind

With advanced behavioral analytics, a deep understanding of the typical runtime behavior of models means anomalies are flagged immediately in real time so teams can spot manipulation before attackers compromise other systems or extract sensitive data. And with more and more data points, behaviors of assets at runtime become increasingly accurate for better threat detection.

Want to see Adversarial machine learning in action? Get a demo today.

FAQ

What is an adversarial AI example?

An example of Adversarial AI is an image recognition system being tricked into misclassifying images based on manipulation of pixel values in a way that’s imperceivable to humans. The attack may lead an inventory system to misidentify products, leading to disruptions in the supply chain and incorrect stock levels.

In Adversarial AI, attackers manipulate AI systems. Other examples include altering transaction data so fraudulent activity goes undetected or altering sensor data so self-driving vehicles make incorrect decisions. 

What is an adversarial attack on Gen AI?

In generative AI, models are misled to create incorrect outputs. For example, an attacker could manipulate prompts in a chatbot to give biased information to customers, damaging brand reputation and leading to confusion and frustration. It could be manipulated to generate irrelevant suggestions in product recommendations or provide incorrect information on store policies. 

Generative AI used directly with customers means AI must be closely monitored to prevent the risk of Adversarial AI attacks and designed with fail-safes to prevent attackers from being able to alter the training that models receive.

What are the implications of Adversarial AI for data security?

Adversarial AI poses real risks to data security. Typically, vulnerabilities in AI models create opportunities for attackers to manipulate AI systems, bypassing security and reverse-engineering private information from AI models.

The implications for data security include:

• Data breaches: Extracting sensitive data from models through attacks like model inversion.
• Corruption of data: In poisoning attacks, harmful data is injected into training datasets, leading to poor model predictions and classifications. The process damages data integrity.
• Bypassing security systems: Attackers can infiltrate systems undetected and access organizational data.
• Loss of trust: All manipulated AI systems can lead to a loss of trust in the models and data systems, as well as the organizations involved. 

What are the real-world implications of Adversarial AI? 

Even benign glitches in non-critical AI systems threaten the organizations that are challenged by them, with customers losing faith and trust in a company’s ability to provide reliable, secure services. The real-world implications of Adversarial AI are far-reaching, getting at the very heart of how organizations and their customers interact. Key implications include:

• Security breaches: Adversarial attacks can pave the way for attackers to gain access to sensitive data and networks. For example, facial recognition systems could be tricked into granting access that opens the door to the rest of an organization’s systems.
• Financial losses: From financial fraud to market manipulation, AI systems protect a multitude of critical and sensitive regulated systems. Breaches can be costly.
• Impact on autonomous systems: Adversarial AI can mean the manipulation of systems that provide medical diagnoses or those that power self-driving cars. With misleading data fed into these systems, human lives are at risk.
• Misinformation and reputational damage: Adversarial attacks with generative AI models can produce misleading and harmful content, and they can do so on a grand scale, damaging brand reputation or engineering social disorder.
• Data privacy risks: Attackers can exploit techniques like model inversion to extract private information from models, leading to data breaches and violations of security regulations like GDPR.