Adversarial Attacks on AI: A Complete Guide to LLM Security Risks

Cybersecurity in AI is a hot topic. Compared to other tech innovations, AI is still considered fairly young, so the security issues related to its use are still being discussed in different communities. However, in addition to standard risks, like data leakage or phishing attacks, another threat has emerged—adversarial attacks. This type of attack poses a great threat to large language models (LLMs) and AI-powered apps, as it directly affects the results they produce.

This does not mean that you can’t protect your software from these attacks. In this article, we are going to tell you what adversarial attacks are, how they work, and what measures your business should take to minimize their risk. Read on!

What Is an Adversarial AI Attack?

Anyway, what are adversarial attacks in AI? Let's start with the definition. An adversarial attack is a type of cyber attack on machine learning neural networks and artificial intelligence models that distorts the input data, which leads to incorrect results and wrong decisions.

Here’s an example: Imagine you want to create an AI solution that can recognize animals in photos. If you show it a picture of a dog, it says “dog.” But if someone subtly alters that image in a way you can’t notice with the naked eye, the AI might now say “bread loaf” instead. That’s an adversarial attack.

Such attacks can target AI systems used in:

• Autonomous vehicles (causing misinterpretation of road signs)

• Security systems (tampering with facial recognition)

• Finance (fooling fraud detection algorithms)

• Healthcare (misdiagnosing)

Such interventions into the work of AI in these industries can lead to serious, even dangerous, consequences for business reputation and profits.

How Do They Work and Why Are They so Dangerous?

Let’s take a closer look at how exactly adversarial attacks confuse artificial intelligence algorithms. In short, they exploit the way AI models (especially those based on deep learning) process patterns in data.

Here are the basic principles of how adversarial attacks work in more detail:

• AI models learn by recognizing patterns in numbers. For example, if your model needs to learn and remember an image, AI perceives the picture as a matrix of pixel values.

• Hackers can tweak those numbers ever so slightly. Humans may not see it right away, but the model will get confused.

• These tweaks are often generated with the help of mathematical algorithms that figure out the smallest possible change needed to flip the model’s output.

Depending on the original data, the type of tweaks can vary. For images, it will be pixel noise, for audio files—changes of sounds, for text—incorrect words. Whatever data you use for your model, AI adversarial attacks are one of your greatest risks. Why?

First of all, they are hard to detect. The changes can be so small and unnoticeable that you won’t find anything until the AI gives you the incorrect results. Second, the systems that usually get targeted are extremely sensitive to mistakes. If a car’s autopilot misreads traffic signs, it can lead to severe accidents and grave injuries. And if an AI model misdiagnoses a patient, the results can literally threaten the life of a human being. Finally, it’s difficult to build robust defences against them. Certain defence mechanisms do exist (and we’ll talk about them), but for the most part, AI models don’t generalize well outside of their training data.

Types of Adversarial Attacks

Adversarial attacks come in many flavors. When categorising them, you should pay attention to the two most important factors: How much the attacker knows about the model and what their goal is. Here's what the main types of adversarial attacks on AI systems look like:

Depending on the knowledge:

• White-box attacks: The hacker knows everything. Literally everything. The model’s architecture, training data, and parameters. With such a deep understanding of the system, they can create extremely precise adversarial examples.

• Black-box attacks: It’s the polar opposite of the previous type. The attackers don’t know anything about the model and the way it works. They can only feed inputs and observe outputs (like an API). However, it doesn’t mean they are ineffective.

• Gray-box attacks: This one is the middle ground between the two previous types. The attacker can know something about the model, for example, its type or the kind of data it used for training.

Depending on the purpose:

• Evasion attacks: Their goal is to avoid detection or mislead the model at test time. It’s most common in image classification and malware detection.

• Poisoning attacks: These attacks target the training data. They introduce malicious, deceptive, or incorrect data into the training set so the model learns bad behavior.

• Model inversion attacks: With the help of model inversion attacks, hackers can reconstruct training data from model outputs. This is a direct threat to data privacy.

• Inference attacks: This type of attack is even more dangerous for data privacy. Its goal is to extract sensitive information about the training data, model behavior, or user inputs.

• Model extraction attacks: Such attacks can recreate the target model by repeatedly querying it. This approach can be “useful” for stealing IP or building black-box attacks.

What makes all these attacks even more unsafe is that they can be transferred from one model to another. For example, attackers can trick a public model with an altered image and then reuse it against a custom model. That’s why your team should know how to effectively protect your AI from such inputs.

Why Are LLMs Particularly Vulnerable?

Sometimes, LLMs feel like a black box, and that's a huge part of the problem. Unlike traditional software, where you can trace logic from point A to point B, LLMs are built on statistical relationships between billions of data points.

This complexity, which is the very source of their power, also makes them really fragile. LLMs are trained on a dataset so vast that it's impossible to fully vet. This training data is filled with biases, factual errors, and even intentionally malicious content. An attacker doesn't always need a prompt to finish their job. Sometimes, they just need to find the right phrase that taps into a weird corner of the training data.

Real-World Implications and Security Risks

The risks here aren't just theoretical. When an LLM is compromised, it can have immediate and severe consequences. And these vulnerabilities can be exposed to real-world threats with tangible impacts.

Manipulation of Public Opinion and Spread of Misinformation

A compromised LLM can become a super-spreader of disinformation. Imagine an attacker using adversarial prompts to generate thousands of realistic-sounding but completely false news articles, social media posts, or comments. These could be used for basically any malicious purpose.

Data Breaches and Leakage of Sensitive Information

Many companies are combining LLMs with their internal databases and proprietary knowledge. An attack known as "indirect prompt injection" can turn this combination into a legit threat. For instance, an attacker could hide a malicious prompt in a webpage or document, and when the model processes that data, it will find the new “instructions” and comply.

Financial Fraud and Automated Social Engineering

LLMs are masters of mimicry. Attackers can use them to automate highly personalized phishing campaigns at an unprecedented scale. An adversarially guided LLM could scrape a target's social media, learn their writing style, and then write a fraudulent email to a colleague asking for a wire transfer. The email would sound so authentic that it could easily bypass human suspicion and, unfortunately, lead to direct financial losses.

Reputational Damage and Erosion of User Trust

What happens when a customer-facing chatbot is tricked into generating offensive, biased, or harmful content? The screenshots go viral, the brand is publicly shamed, and users lose faith in the service. The reputational fallout can be devastating.

Bypassing Safety Filters for Malicious Code Generation

The creators of major LLMs have put extensive "guardrails" in place to prevent them from being used for obviously malicious purposes. But LLM adversarial attacks can dodge these safety filters. Attackers can trick the model into providing the very instructions it was designed to refuse, and now your friendly coding assistant turns into a tool for cybercrime.

Disruption of Automated Business and AI-Powered Services

Businesses automate more complex workflows with the help of AI agents, and an adversarial attack could sabotage these processes. Imagine an AI-powered inventory management system being tricked into ordering thousands of unnecessary items and causing logistical chaos. Or a customer support agent being manipulated to cancel the accounts of legitimate customers.

How to Protect Your AI from Adversarial Attacks?

A complete protection from adversarial attacks is, unfortunately, impossible to reach. Just like any other software, AI will always be at risk of such interventions. However, with the help of strategic, technical, and business decisions, you can establish a proactive and efficient approach to threats. Here’s a breakdown of how you can protect your AI from adversarial attacks:

Adversarial Training

This type of precaution is aimed at preemptive education of your model. Besides using clean data, your team can create its own adversarial examples and feed them to the model as a sort of “vaccine” against possible attacks. As a result, your model will learn to recognize and resist bad inputs. This approach can be especially useful for fraud detection models and NLP systems. But it usually requires more resources, so you need to be prepared for additional spending.

Input Sanitization

If your AI receives public or user-submitted data, you need to clean and validate the inputs before feeding them into the AI. This process will protect your model not only from adversarial attacks, but also from bias and errors. And the fewer mistakes your AI makes, the more users will trust it. You can use data augmentation or detection filtering to monitor what exactly your model consumes.

Anomaly Monitoring

Here, you will need to use third-party tools for real-time monitoring. They will detect suspicious input patterns or model behavior, flag conflicting predictions, and send the necessary information for manual review. Even the smallest inconsistency should raise an alert, since the damage it can do to the model and its output can be huge.

Limited Exposure to APIs

If your model is exposed as an API, you can apply some API security measures like limiting the query rates or obfuscating API outputs. This can be especially important for web applications since APIs are the backbone of their functionality. Protect your API and don’t give attackers free tools to study your model.

Ensemble Models

To get the most accurate results possible and to strengthen your defences, you can combine multiple models and only accept decisions when all of them are in agreement. Such an approach makes it harder for attackers to build an input that can fool all of them at once.

Secure Training Pipeline

AI is as good as the data you feed it. And if you don’t protect your data well enough or don’t check for potential mistakes, hackers can easily train your model to fail. You can protect your data by using trustworthy sources and auditing training sets for possible anomalies.

Privacy Preservation

If your model has to handle sensitive data as part of its tasks, your duty is to reduce the risk of leaks and breaches. Techniques like differential privacy (adds statistical noise to mask the presence of individual data points) and homomorphic encryption (allows the model to use encrypted data without exposing its raw version) can be your best solutions. Also, a simple authentication system that will let only authorized personnel view the data will be a good step.

Red Team Exercises

They say “the best defence is a good offence.” This phrase works really well in AI security. If you have a dedicated security team (or partner with a third-party one), they can simulate attacks like poisoning or model extraction to test your security measures. When they find vulnerabilities, you will definitely know where the real hackers can strike and what you should do to fix them.

Educate Your Teams

Finally, train your team. Even the best-performing AI model is still supervised by humans. If they don’t know what adversarial attacks are, how to detect them, and what to do to prevent them, all other security measures won’t do much. Train your developers, data scientists, and product managers to understand adversarial threats.

The Future of Adversarial Robustness in AI

The future of AI security robustness will be built on proactive defense rather than reactive fixes.

Development of Formal Verification Methods for Models

Formal verification uses mathematical proofs to guarantee that a model behaves as expected under all possible conditions. Instead of just testing a model on a million examples, you would prove that it cannot produce harmful outputs, no matter how clever the adversarial input.

Emergence of AI Security as a Standard Practice (AISecOps)

For decades, we’ve had DevOps to integrate development and operations, and more recently, DevSecOps to bake security into the software lifecycle. The next evolution is AISecOps. This means creating dedicated teams and processes focused exclusively on the security of AI models.

Standardized Benchmarking and Threat Sharing Consortia

Right now, everyone is fighting their own battles in isolation. A security team at one company discovers a new type of prompt injection attack, but has no standardized way to share that threat intelligence with others. In the future, we'll see the rise of industry consortia and open standards for risk mitigation and sharing information about new attacks.

Bottom Line

Adversarial attacks in AI are a huge threat. If they succeed, their consequences can be truly awful. But now you know what exactly they are and, most importantly, what you should do to protect your AI model from them.

If you want to know how secure your software solution is, contact us! The Yellow team will help you find out how strong your security measures are and what you need to strengthen even more.