What is AI Data Poisoning and Why Does it Happen?

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From chatbots to digital assistants, Artificial Intelligence (AI) is taking the world by storm. AI simulates human intelligence via machines and has transformed the way we live and work by automating tasks such as customer service or quality control that are usually conducted by humans. Recently, ChatGPT stunned users by offering AI-powered conversations based on a few user-based prompts. Along with providing information about pretty much any topic imaginable, the model-based chatbot can also write and edit code, solve math problems, and produce texts. Users can also install various ChatGPT plugins for an enhanced AI experience.

A huge advantage of AI over human workers is that machines can work faster and make very few mistakes. AI systems generally work by consuming vast amounts of training data to make predictions about future requests. For example, feeding examples of texts to chatbots helps them learn to generate lifelike dialogues based on numerous topics. AI programming utilizes cognitive skills such as data acquisition and creating rules, known as algorithms, to complete specific tasks, choosing the most suitable algorithm for the job, and self-correcting to ensure accurate results. 

What is AI data poisoning and how does it work? 

As with most forms of technology, AI is also susceptible to hacks and cybercrime. By manipulating algorithms, cybercriminals can control the output of AI functions in so-called data poisoning attacks. If a machine’s stored data is inaccurate or unreliable, its AI algorithms will not produce accurate results. For example, if a chatbot is programmed to misunderstand user requests, it will unintentionally produce tainted responses. Likewise, AI-based translation tools could be programmed to misunderstand the meaning of a word or phrase, leading to an incorrect translation. Data poisoning attacks are essentially acts of deception that intentionally and maliciously mislead AI data stores. 

Why does data poisoning happen? 

AI continues to jeopardize the livelihoods of artists, writers, and other creatives, and AI was directly responsible for the loss of 3,900 or around 5% of U.S.-based jobs in May this year. The threat of jobs or crafts being taken over by AI is very real, and many people are understandably unhappy. Recently, artists have started using a new tool named Nightshade to make invisible changes to the pixels in their artwork before uploading it online so that it produces unpredictable results when discovered by AI systems. Artists are using the tool to protect themselves from AI tools that use their artwork without permission. The tool can turn dogs into cats and cars into cows, making the results useless to unsuspecting users. 

What happens to AI systems when data is poisoned?  

Poisoned data systems occur when malicious or misleading information is injected into the AI training dataset. This method ruins the learning process and creates bias, which causes faulty decision-making and incorrect responses and is known as backdoor poisoning. So-called training data poisoning occurs when attackers influence learning models toward a particular outcome or bias that benefits the perpetrator. Moreover, model inversion attacks allow cyber criminals to extract specific and sensitive information from the AI model’s output, which is then used to the hacker’s advantage. Finally, stealth attacks create vulnerabilities that are practically undetectable during testing. However, these vulnerabilities can be exposed and exploited once models are launched. 

AI can also be used to create deep fakes, which are used to manipulate content and defame individuals. These can be used to produce lifelike images, videos, or audio files to impersonate victims and recreate scenarios that do not exist or have never occurred. Deep fakes are produced by using two AI algorithms: one that creates a replica of the desired media, and another that can report on differences between real and fake images. This can be repeated until no more fake imagery is detected.   

How can we combat data poisoning? 

Data poisoning is an unfortunate phenomenon that is difficult to prevent as it can be hard to detect contaminated data. It would be impossible to sift through the vast amount of information in an AI software database to check for accuracy and potential poisoning, but businesses can implement practices to minimize the risk of attacks. First, they should be careful when sharing sensitive data and inform employees not to input private company information into AI tools, reducing the risk of data leaks or tampering. Moreover, performing penetration tests against company systems and networks as part of a comprehensive cybersecurity strategy can help businesses understand weaknesses and potential vulnerabilities. This should be repeated regularly to ensure that systems are intact.

Additionally, implementing a so-called moving target strategy against attackers can protect machine learning models by occasionally changing algorithms. When training AI software, businesses should ensure that they feed information from consistent, valid, and pre-screened data sources to avoid data poisoning, and think twice before relying on untrusted or uncontrolled sources. Diligently selecting databases and information for AI software training is a crucial step towards combating data poisoning, and companies should remember to be proactive in safeguarding themselves.  

Conclusion: The future of AI and AI data poisoning 

While AI is helpful in many ways, such as streamlining work processes and reducing labor costs, it is not without uncertainties. Data poisoning is the newest threat to AI tools, and it looks like it is here to stay. Threats become increasingly sophisticated as technology becomes more advanced, which can have a devastating impact on victims. Although some data poisoning is intended to protect artists and creatives, others are created out of malice. As technology becomes integrated into our everyday lives, hackers will find ways to exploit vulnerabilities in AI systems and steal sensitive information. Companies can protect themselves by performing regular penetration tests and staying updated with the latest information regarding AI threats.