Can AI Be Hacked? The Reality of AI Vulnerabilities

From Siri to Alexa, AI-powered products have made our lives more convenient.

However, with the increasing use of artificial intelligence, questions about its security have also emerged. 

Hackers are finding new ways to exploit vulnerabilities in AI systems and cause harm. 

But what exactly is AI hacking? And what are the consequences of such attacks?

In this post, we will explore the vulnerability of AI systems, what can be done to prevent hacking, and what happens when an AI system is hacked.

Can AI be Hacked?

Yes, AI is vulnerable to attacks because it is based on algorithms that can be exploited and manipulated by malicious actors. 

Several forms of hacks leverage weaknesses in modern AI architecture. But, defensive measures can be employed to safeguard these systems.

Why is AI a Target for Hackers?

AI is at the core of many consumer and business products today. 

By identifying vulnerabilities in these systems, hackers stand to gain financially. Hackers can steal valuable assets like user data or code. 

Some organizations also issue bounties. These monetary rewards are for individuals who alert them to security soft spots.

Lastly, some hackers just want notoriety. Hackers have existed since the dawn of technology. Some may argue even earlier if we were to include social engineering or conning as a form of hacking.

There is always someone out there interested in testing the boundaries of security.

AI Hacking Specialist

AI prompt hacking is a popular form of hacking that targets AI systems that use natural language processing (NLP). 

In an AI prompt hack, the attacker manipulates the AI by injecting malicious commands or code into the conversation. This tricks the AI into performing actions that it wouldn’t normally do. 

An example of this type of hacking is DAN. This community-led project on Reddit identified text prompts that override some of OpenAI’s restrictions on ChatGPT.

We have seen some hilarious examples of users bypassing ChatGPT’s limitations using this method. 

Example of DAN in action

But as more products rely on OpenAI’s model, this type of hacking becomes particularly concerning.

AI models like GPT that use NLP are becoming more prevalent and are often connected through an API to critical applications, such as healthcare, finance, and security.

A savvy prompt engineer hacker can creatively identify ways to potentially steal sensitive information, manipulate financial transactions, or even cause physical damage to infrastructure connected to the internet of things (IoT). 

For example, a hacker could trick a medical conversational AI agent into disclosing confidential patient information or convince a bank’s chatbot to inappropriately transfer funds to their own account.

Examples of AI Being Hacked

There are several types of AI hacking. 

Three primary methods are data manipulation, model theft, and adversarial attacks.

Data manipulation involves accessing AI systems and changing data to influence decision-making. Think prompt hacking as mentioned above, which involves exploiting the data fed into the model. 

With Microsoft’s new Bing AI assistant, we have already seen users trick the model.

Model theft is a particularly alarming example of AI hacking where the underlying training data is stolen and exposed by malicious actors. 

The problem with model theft is that it’s difficult to detect, and once the AI system is hacked. It can be used to carry out attacks for a long time before being detected.

For example, many new generative AI consumer products like AI avatar generators and AI voice generators collect personal data for training purposes. 

If one of these training model files is leaked, someone can use your likeness for identity theft or extortion.

In the case of the Clearview AI hack, these exploits can reach the scale of billions of images. Also, apps like Replika AI are the potential target of hacks given their vast collection of sensitive user data.

Adversarial attacks use adversarial examples to trick an AI into making incorrect decisions. With adversarial machine learning, hackers manipulate the data inputs used to train an AI to trick it into making incorrect decisions. 

For example, an attacker could add small, imperceptible perturbations to an image, such as changing the color of a single pixel, which would cause an AI system to classify it incorrectly. 

This can potentially break certain security systems like facial recognition software or self-driving cars, which rely on AI algorithms to make decisions.

How to Protect AI-Powered Systems

According to Acumen Research and Consulting, one key AI hacking statistic is that the market for AI-based cybersecurity is set to surpass $130 billion by 2030.

To prevent AI prompt hacking and other forms of attacks, it’s critical to put in place strong security measures and regular updates of the AI-connected software and seed prompt. 

Regular security audits and penetration testing can help find and patch weaknesses before a hacker can exploit them. 

From a technical standpoint, the key to defending against cyber attacks with AI is to use it in conjunction with other security protocols, like firewalls, intrusion detection systems, and encryption. This multi-pronged approach minimizes vulnerabilities and curbs the chance of a successful hack.

One often overlooked vulnerability to improve cybersecurity is training employees. Staff members can learn how to manually review unusual user inputs and report suspicious behavior. This can include regular cybersecurity training sessions, workshops, and certifications, which help staff members to stay up-to-date with the latest security trends and best practices.

Often the human eye can pick up on awkward text phrasing or unnatural language intended to deceive an AI agent.

In the future, AI will drive an arms race between cybersecurity professionals and hackers. 

Access to cloud computing resources, large datasets, and engineering creativity will be a significant determining factor in protecting users’ assets and data.

While AI hacking is not going anywhere, we remain excited about the potential for AI tools to increase productivity and creativity.

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About the Author

Andrew has over 10 years of experience in advising businesses on growth marketing and strategy. He earned his MBA from NYU Stern with a concentration in data analytics and marketing. Andrew is based out of New York and currently consults Fortune 500 clients and startups on data science projects, digital marketing, and finance.

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