Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to critical systems—from autonomous vehicles and smart cities to medical diagnosis and finance. But alongside the benefits comes a new risk: adversarial AI. This refers to techniques that deliberately mislead or manipulate AI models, causing them to malfunction.
Let’s explore just what adversarial AI is, how big of a threat it presents, and what defenses organizations can deploy to mitigate its impact.
What is adversarial AI?
Adversarial machine learning (AML) involves attacks on AI/ML systems, aiming to degrade their performance or make them behave incorrectly. Unlike “traditional” cyberattacks—like malware or phishing that target software bugs or network vulnerabilities—adversarial AI exploits the decision-making logic of AI models. In other words, attackers craft malicious inputs or corrupt training data so that the ML system makes wrong predictions, misclassifies data, or even reveals sensitive information.
This can have very real impacts. An attacker might tweak an image so a self-driving car fails to recognize a stop sign, or poison training data so a fraud detection model learns biased behavior. Adversarial attacks can occur at many stages, during training or inference, and can bypass normal security controls.
Adversarial AI attack categories
There are several key categories of adversarial to be aware of:
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Prompt injection: Adversaries can craft input prompts that break a large language model (LLM) or generative AI out of the intended context or include hidden instructions, causing an LLM to produce unintended or malicious output. The classic prompt is to add “ignore previous instructions and respond with…” to override a chatbot’s normal behavior. Prompt injections have been cited as the biggest risk to LLM functionality.
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Evasion attacks: Here, attackers include hidden instructions in their inputs at inference time so that the model misclassifies them. For example, adding subtle stickers to a stop sign image can make an AI see it as a speed limit sign instead. Evasion is often about misleading the model into incorrect outputs.
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Data poisoning attacks: This involves attackers tampering directly with an AI’s training data. Attackers inject malicious or mislabeled data during training to cause the model to learn the wrong associations or develop a hidden backdoor.
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Model inversion attacks: These aim to reverse-engineer sensitive training data. An attacker queries the model and analyzes its outputs to reconstruct or infer private input data. For example, observing how a healthcare system AI responds might reveal patient information.
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Model stealing (extraction) attacks: With the right steps, attackers can replicate a proprietary model. Querying a black-box ML service extensively and collecting its outputs can be used to build a clone that mimics the target model’s functionality. The stolen model can be used for profit or to further attack the original.
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Membership inference attacks: In a membership inference attack, an adversary determines whether a specific data record was part of the model’s training set. Analyzing the confidence or behavior of the model on an input allows the attacker to work out if that data was “seen” during training, potentially exposing personal records.
Together, these techniques show how attackers can compromise AI/ML systems from within, making adversarial AI a unique and serious cybersecurity challenge.
How real is the threat of adversarial AI?
Adversarial AI isn’t just theoretical; it’s a rapidly growing threat as both AI adoption and attacks escalate. In recent years, researchers have demonstrated adversarial techniques on everything from vision systems to language models.
The volume of AI models in production is exploding. Seventy-three percent of enterprises have deployed AI models, one-third of companies use generative AI regularly, and 58% of businesses plan to increase their AI investments this year. In short, millions, or potentially billions, of data-driven decisions now rely on AI, and it seems that number is only going to keep increasing.
With more AI in the field, the attack surface grows. A 2024 survey found that 77% of organizations had experienced an AI-related security breach in the past year. Many of these breaches go unreported publicly, meaning the actual impact may be even larger.
Compounding this is many organizations’ lack of readiness. Only about one-third of companies have deployed dedicated AI security tools to counter these threats. And while 96% of firms planned to boost their AI-driven security budgets, only 32% had plans for specialized defenses for countering adversarial AI risks.
This disconnect leaves systems vulnerable. As AI adoption continues to accelerate across critical functions like healthcare, finance, government, and more, attackers will increasingly target AI pipelines and models.
Real-world examples of adversarial AI attacks and their impacts
Adversarial AI attacks aren’t just hypothetical; they’ve already happened and are continuing to happen. While the severity of the attacks range from petty pranks to potentially devastating, they all show just how vulnerable AI models can be to exploitation.
Tricking chatbots (prompt injection)
Chatbots have been manipulated by users into absurd behaviors through prompt injection. For example, a Chevrolet dealer’s AI Chat-GPT-based chatbot was pranked into offering a brand-new 2024 Chevy Tahoe for $1. Users could persistently instruct the bot to phrase offers as “legally binding,” no matter how ridiculous. One user got the bot to shave $47,000 off the truck’s $58,000 price tag.
Similarly, Air Canada faced legal fallout when its support chatbot misquoted a bereavement fare and told a customer to purchase a full-priced ticket. A tribunal ruled that the airline must honor the AI’s offer after the bot misinformed a customer.
These cases show that insecure chatbot prompts can lead to binding “offers” or legal liabilities if not carefully managed.
Evasion of vision systems
Autonomous systems can be fooled by tiny physical changes. Researchers demonstrated that adding nearly invisible stickers to a road could trick Tesla’s autopilot into veering into oncoming traffic. In one experiment, three small stickers on the asphalt confused the lane-detection AI, causing the car to cross left unexpectedly.
Other studies showed how modest alterations to stop signs or speed-limit signs, like carefully placed tape or paint, make vision models see one thing (a speed limit) when it’s actually another (a stop sign).
These evasion attacks mean self-driving cars, drones, and safety systems could be manipulated by attackers in the real world, with potentially dire and deadly consequences.
Fooling image classifiers
One of the earliest AI “gotchas” occurred in image recognition. Google researchers in 2015 showed that an image of a panda could be imperceptibly modified so that a neural network misclassified it as a gibbon, while to the human eye both images are clearly pandas.
This classic adversarial example demonstrated that tiny pixel changes can utterly confuse AI. Although harmless on its own, it highlighted how real systems, like medical imaging or surveillance, could be tricked to make critical misidentifications, simply by adding crafted noise to inputs.
Data poisoning- Microsoft Tay
In 2016, Microsoft launched Tay, a Twitter chatbot meant to learn from users. Within hours, trolls flooded Tay with racist and offensive content. Tay “learned” and began spewing hateful tweets, forcing Microsoft to shut it down.
This is a textbook poisoning attack: bad actors injected malicious “training” inputs (tweets) that rapidly corrupted the AI’s behavior. While AI models have advanced massively since 2016, Tay’s meltdown should be a warning on how learning systems can be led astray by malicious data, transforming a benign AI into a toxic one.
Privacy breach via training data (model inversion)
In 2023, an incident occurred that showed the dangers of leaked training data. A San Francisco-based artist named Lapine discovered that her private medical photos (intended only for doctors) ended up in LAION-5B, a massive open dataset used to train generative AI. Though LAION claimed only public images were included, the dataset contained her photos.
This incident wasn’t an “attack” per se, but it illustrates how models and datasets can expose personal data. If a malicious actor performed model inversion on a model trained on LAION, they could potentially reconstruct even more sensitive details from private images. It’s a wake-up call that raw data leakage leads to privacy violations once integrated into AI.
Model theft – ChatGPT cloning
Even closed-source AI models can be copied. Security firm Mindgard demonstrated a successful model-stealing attack on ChatGPT-3.5 Turbo. They issued carefully chosen queries and analyzed the outputs, and extracted a much smaller model that nevertheless performed the same task on a benchmark. Amazingly, this was done with only $50 of API queries. The cloned model was later used to craft better attacks against ChatGPT itself.
This proof-of-concept shows that proprietary AI models can be reverse-engineered and replicated through repeated querying, posing a threat to companies’ intellectual property and giving attackers working replicas to use in new exploits.
Necessary defenses against adversarial AI
As adversarial threats grow, organizations must adopt AI-specific security measures. Here are some of the most prominent measures.
Robust data hygiene
You must strictly control and validate your training data. You can use data sanitization and anomaly detection to filter out corrupted or malicious inputs before training. Combine this with differential privacy and encryption so that models leak minimal information about individual records. For example, adding noise to outputs (differential privacy) can thwart inversion attacks by obscuring exact data points.
Adversarial training and model hardening
Incorporating adversarial examples into the training process teaches models to resist them. In practice, this means training on both normal and adversarially-altered inputs, like slightly altered images or prompts, to make the model robust. Techniques like model regularization and sensitivity analysis can further reduce vulnerability to small input changes. Regular model retraining on updated, clean data also helps correct any learned biases or backdoors.
Input validation and monitoring
At inference time, scrutinize any inputs for anomalies or malicious patterns. You should implement input validation filters, like scanning prompts for hidden instructions or detecting unusual statistical patterns in data. Then, monitor the model’s outputs and query patterns. You should rate-limit excessive queries and watch for unusual usage that could signal model extraction attempts.
Many experts recommend red teaming to simulate attacks on your own AI and discover blind spots. Red-team exercises and penetration testing for AI can proactively reveal vulnerabilities like prompt injections, data poisoning, or evasion pathways, allowing fixes before real adversaries strike.
Secure AI architecture and governance
Your AI systems should be designed with layered security from the outset. For mission-critical AI, consider using secure enclaves or hardware roots of trust. This should be combined with an AI supply chain inventory to know what data and pretrained components are used.
Adopting frameworks like the NIST AI Risk Management Framework (AI RMF) to guide governance and risk assessment. This emphasizes building trustworthiness into AI from the start, including risk identification, design safeguards, and continuous monitoring. In other words, treat AI the way you treat other critical infrastructure: with formal policies, audits, and cross-disciplinary teams.
Conclusion
Adversarial AI isn’t a hypothetical concern, it’s a real and growing threat. As AI systems become more and more common, attackers have new avenues to disrupt operations, steal data, or cause harm by hacking the models themselves. The examples above show that even seemingly harmless systems can be fooled with relative ease, and the consequences can be huge.
The good news is that mitigation is possible. Adopting AI-aware security means you can stay ahead of many attacks. Red-team your AI, invest in AI risk management, and treat your models as critical assets.