What Is AI Poisoning? A Guide

Before they can power AI models and AI agents, machine learning algorithms must be trained to find patterns in and interdependencies among large data sets. But what if a training data set was deliberately seeded with data aimed at making the model work for a malicious actor rather than those who trust the AI to help them?

That scenario is called AI poisoning, and security researchers have shown that it’s possible to corrupt AI models by training them with data designed to deliver manipulated results or by taking advantage of design flaws in the underlying code. And it doesn’t need to happen during initial configuration—the algorithms in a foundation model will undergo multiple rounds of training and, later, yet more training if the model is fine-tuned for a particular task. This ongoing process opens a new front in an organization’s data security struggle.

What Is AI Poisoning?

AI poisoning is the act of manipulating an AI system by contaminating its training data or by exploiting vulnerabilities in its supporting architecture. These exploits are designed to alter or diminish the system’s ability to inform critical decisions or to tap into the system’s interactions with sensitive information.

While “poisoning” is a provocative term, the activity itself has its roots in common malicious attacks on data architectures, with twists added for the particulars of AI systems, such as injecting malicious data into the training data set or otherwise modifying data, leaving the AI model with incorrect patterns and causing it to produce undesirable or even harmful outputs. Or, as in past data breaches, attackers might exploit vulnerabilities in the AI model's architecture to achieve their goals, whether that’s undesired outcomes or compromised performance.

As AI systems become increasingly prevalent and more complex—including via a growing number of autonomous AI agents—the risk of AI poisoning increases. This highlights the need for generative AI services that offer data security measures and testing protocols to help deliver integrity and reliability.

AI Poisoning Explained

AI poisoning refers to manipulating the security and accuracy of an AI model’s architecture or training data. These exploits are perpetrated for many reasons. For example, an AI model’s training data might be altered so it won’t recognize fraudulent transactions, market manipulation, or emails containing malware, effectuating the theft of funds or data. Or an AI system might be maliciously altered to offer incorrect medical diagnoses or legal recommendations.

AI poisoning can be carried out by a range of actors with different motivations. These include malicious individuals seeking to cause harm or disruption, competitors aiming to undermine a company’s AI-based products, state-sponsored groups engaged in cyberwarfare, or disgruntled employees.

Poisoning attacks can take various forms. One method is label flipping, where an attacker changes the correct labels of training data to incorrect ones. Another approach is data injection, which involves adding entirely new, fabricated data points with incorrect labels. More sophisticated techniques include clean-label poisoning, where the poisoned data appears legitimate but still causes the model to learn incorrect patterns, and backdoor attacks, which trigger specific, unwanted behaviors when certain input patterns are present.

Preventing AI poisoning starts with securing the data used to train AI models, including using robust data validation and verification processes that check for anomalies, inconsistencies, and potential tampering. When sourcing outside training data, it’s advisable to use trusted and reputable providers, such as government agencies and research institutions, as well as companies and social media platforms that repackage and anonymize site data for AI training purposes. However, some firms obtain and sell training data from broad internet scrapes, and these need to be carefully vetted.

Organizations with large and highly varied data sets can use data sanitization tools offered by their data science service providers to clean and filter training data and help remove potentially malicious or poisoned samples. Another common strategy for improving model accuracy, the ensemble method, trains multiple models on a data set, or on variations of that data set, and then aggregates their outputs for a final answer. This can help detect and mitigate the effects of poisoning by using the power of collective decision-making.

Formal, ongoing monitoring and maintenance of AI systems themselves are also essential for preventing and detecting AI poisoning. Best practices include regularly auditing the performance of AI models and monitoring for unusual behavior or outputs.

Generative AI–based applications and AI agents are now embedded in business applications and development platforms, and they deliver value in creative ways across industries and government operations. As AI becomes more central to business processes, protecting generative AI training and fine-tuning operations from AI poisoning schemes is essential for mitigating financial risks and safeguarding brand reputation and customer trust.

AI Poisoning FAQs

How does AI poisoning work?

AI poisoning attacks exploit the fundamental process of machine learning, which involves training a model on a data set. Attackers introduce poisoned data into the training data, often with subtle modifications that are hard to detect. Over time, the AI model learns from this corrupted data, leading to unwanted or incorrect predictions and decisions.

What are the potential consequences of AI poisoning?

The impact of AI poisoning can be severe. It can result in AI systems making inaccurate predictions, misclassifying objects or entities, or exhibiting other unwanted behavior. For example, a poisoned AI system for a self-driving car might fail to recognize certain hazards, or a facial recognition system could misidentify individuals. In critical applications, such as healthcare or finance, AI poisoning can lead to life-threatening situations or significant financial losses.

How can AI poisoning be detected and prevented?

Detecting AI poisoning requires robust data validation and monitoring techniques. This includes implementing data quality checks, anomaly detection algorithms, and regular audits of training data. Additionally, using diverse and extensive data sets for training can make it harder for poisoned data to have a significant impact. Prevention also involves securing the data collection and storage processes, implementing access controls, and educating data providers and users about potential threats.

Are there any examples of AI poisoning attacks?

Yes, AI poisoning attacks have been demonstrated by security firms in various contexts. One notable example is an attack on email spam filters, where carefully crafted emails trained the AI model to misclassify spam as legitimate emails. Another example is the manipulation of image recognition systems by adding small, imperceptible deviations to images, causing misclassification.

How can organizations protect themselves from AI poisoning?

Organizations should adopt a comprehensive security strategy that includes data security measures, regular model validation, and a response plan for potential attacks. This involves investing in data integrity checks, employing security professionals, and fostering a culture of security awareness among employees. Regularly updating and retraining AI models with clean data can also help mitigate the effects of poisoning attacks.