AI is only as reliable as the data it learns from. If that data is manipulated, biased or corrupted, the model can produce inaccurate, unsafe, or misleading results.

This is known as data poisoning. It is a cyberattack where the data used to train or support AI tools, large language models (LLMs) and machine learning models is altered or corrupted.

What is data poisoning?

Data poisoning happens when malicious, fake or incorrect information is introduced into the training data that an AI model learns from. The goal is to change how the model behaves, reduce its accuracy, or create hidden security gaps that can be exploited later.

For businesses, this matters because AI tools are increasingly used for customer support, content creation, analytics, cybersecurity, repetitive admin and decision making. If the data behind those tools can’t be trusted, neither can the output.

How data poisoning works

AI models learn by being given examples and finding patterns in data to form relationships and predict answers. If an attacker changes the initial training data, the model learns the wrong patterns, or hidden triggers are embedded into it. This can happen in a few ways:

Insider access: Someone with access to internal files, training data, customer records or support content adds biased or malicious information.
Public contributions: Misleading content is posted online, added to public datasets, or contributed to open-source platforms where it may later be scraped for AI training.
Automation: AI tools can help attackers create large volumes of convincing but harmful content at scale.

The damage may not appear immediately. A poisoned model can seem normal but fail when it encounters a specific trigger.

Types of data poisoning attack

A targeted attack is designed to make the model fail in a specific situation. For example, a cybersecurity model could be manipulated to classify a certain type of malware as safe.

A non-targeted attack aims to reduce the model’s overall accuracy. This could make a spam filter, recommendation tool, or malware detection system less reliable.

Data poisoning vs prompt injection

Data poisoning and prompt injection are related to a degree, but they happen at different times.

Data poisoning affects the data the model learns from before or during training. The model absorbs the flawed information and carries that behaviour into future use.

Prompt injection happens while the model is being used. An attacker embeds hidden or explicit instructions in a prompt, website, document, or external source to manipulate the model’s response immediately.

How businesses can reduce the risk

You can’t remove every AI security risk, but you can reduce exposure.

Start by using trusted data sources. Avoid feeding AI tools unverified files, outdated or scraped content, or information from unknown sources. Limit who can edit site content, product details, support articles, internal documents, and customer records.

Monitor AI outputs for sudden changes in accuracy, strange answers, repeated errors, or biased responses.

Secure the systems your AI depends on

Data poisoning is an AI data problem, but if your site, e-mail or hosting is compromised, attackers may be able to alter the information that your business – and your AI tools – depend on.

Domains.co.za helps businesses build a safer foundation with secure web hosting, SSL certificates, backups, malware protection and 24/7 monitoring.

About Domains.co.za
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