The Mystery of AI Poisoning - What It Is & How to Prevent It
Exploring the rise of AI poisoning and strategies to defend against it
I’ve had food poisoning before, and trust me—it’s unpleasant. But what’s happening to artificial intelligence (AI) is far more dangerous: AI poisoning. This is when an attacker designs malicious code purposely to exploit underlying vulnerabilities in machine learning systems to alter their outputs.
AI is not completely safe. While it is a tremendous benefit to businesses, governments, and individuals, that does not mean that hackers can’t manipulate it to their advantage. This article will explore the rise of AI poisoning, what it entails, and the essential steps you can take with AI poisoning prevention. Are you ready? Let’s get going.
What is AI poisoning?
AI poisoning, also known as model poisoning, happens when attackers sneak bad data into the training process of machine learning systems. Just like a student learning the wrong lesson, these models produce faulty results because they’ve been fed incorrect information.
Take a young child. When training the child what a dog is, they will slowly begin to recognize what a dog is. But, if you introduce cat pictures but call them dogs, you are introducing false data, which leads to confusion. You are poisoning their young mind. The same is true with AI poisoning but on a much larger scale that, in the wrong hands, could cause catastrophic events.
Model poisoning not only happens with altering data, but it also uses adversarial inputs, or tampered feedback loops. Adversarial inputs are inputs to ML models that an attacker deliberately designs data to make the model make a mistake. They are specialized inputs that are intended to confuse the model.
Tampered feedback loops are another method used by hackers. By feeding the AI bad data, again and again, they make the model ‘learn’ incorrect patterns, reinforcing wrong decisions over time.
The growing dependence on AI in industries like healthcare, finance,
Why AI Poisoning is a Growing Threat
The growing dependence on AI and the consequences of poisoned models in industries like healthcare, cybersecurity, autonomous driving, and finance makes it vital to practice AI poisoning prevention. According to the NIST (The National Institute of Standards and Technology), four types of poisoning attacks appear below:
1. Availability Poisoning
This affects the entire machine learning model and, in essence, causes a denial-of-service attack on users of the AI system.
2. Targeted Poisoning
Targeted poisoning attacks change the ML model’s prediction on a small number of targeted samples.
3. Backdoor Poisoning
A backdoor attack occurs when an attacker slightly alters AI models during training, causing unexpected behavior under selected triggers.
4. Model Poisoning
Model poisoning attempts to directly modify the trained ML model to inject malicious functionality into the model.
As AI increases, AI poisoning security is critical. Organizations of all sizes and types could be targets. Misdiagnoses in healthcare, erroneous decisions in autonomous systems, or financial losses could have catastrophic consequences, including the death of an individual. Does that scare anyone? It does me.
Methods to Prevent AI Poisoning
Is it possible to thwart the efforts of bad actors in propelling AI cybersecurity threats? Sure. Take a look at the following list.
Here are some essential methods to prevent AI poisoning:
Data Validation and Sanitization
Rely on trusted data sources and pair automated tools with human review to catch suspicious inputs before they do damage.
Strong Training Methods will help models recognize poisoned inputs. To make it even more secure, you can use federated learning. Training takes place on individual devices. The model updates are then uploaded for sharing and aggregating, not the raw data.
Constant Model Monitoring
By intense monitoring, you can better spot sudden shifts in behavior, which can prevent AI poisoning.
Model Robustness
The strength of the AI model as it relates to the regulations it receives can be accomplished through two methods – regularization techniques to help AI models not depend so much on small changes, making it harder to trick the model by feeding it insufficient data.
The second way is through ensemble learning. Instead of depending on one model, we can use multiple models called an ensemble. This allows them to make decisions as a group. This makes it harder to trick all the models in the group.
Differential Privacy: This limits the influence of any single data point, making models resilient to targeted poisoning.
Authentication and Access Control:
Only the employees or trusted individuals should be able to modify datasets or models. Limiting access lessens the potential for poisoning attempts.
Model Monitoring and Post-Deployment Vigilance
Periodic Retraining
Training of AI models is not a one-time-train and forget it. Periodically, take the time to retrain with new, validated data.Collaborative Research and Industry Standards
Did you know that strength is by numbers? Joining industry groups focused on AI security will help you stay updated on the latest developments and techniques to prevent poisoning.
Blockchain for Model Integrity
Another smart technique is implementing blockchain technology to record all modifications to a model and its datasets. Creating an undisputable log will alert you if data used in training or the model itself has been tampered with.
Organizations can better protect AI systems from poisoning by implementing these strategies and ensuring long-term security. This restricts poisoned data from having long-term effects. Only include new datasets from diverse and secure sources. Oh, one more thing. If poisoning is detected, implement a way to roll back to a previous version.
Real-World Examples of AI Poisoning
Specific real-world examples of AI poisoning were not found in my research. However, there are three sectors where it would be plausible to see dangerous results from AI poison injection.
1. Healthcare – A hospital’s AI system could be tampered with to produce inaccurate diagnoses due to poisoned training data. Or, a pharmaceutical company could have its models distribute the wrong medication.
2. Financial Sector – It is not unthinkable to envision malicious actors compromising market disruptions.
3. Autonomous vehicle systems can be manipulated through data poisoning. Similar to training children what a dog looks like and providing them with pictures of cats, just think if the same scenario occurred with self-driving cars, only with mixing up street signs. Can you imagine the traffic jams, car crashes, and worse that could happen?
Future of AI Security and AI Poisoning
AI cybersecurity threats and AI poisoning prevention are two areas that are closely linked. There needs to be strong industry-wide security measures put in place that will ensure a safe environment for those businesses using machine learning.
AI isn’t going anywhere—it’s already in use across nearly every industry. But as AI gets smarter, so do the hackers trying to exploit it. That’s why we need more robust defenses now, more than ever.
Emerging tools like artificial immune systems, which can detect and respond to threats in a manner akin to biological immune systems, represent the forefront of preventative technologies. Similarly, encryption and blockchain technology advancements could provide new ways to secure training data against tampering.
There are emerging tools like artificial immune systems. These systems can detect and respond to threats in a manner akin to biological immune systems. Stronger encryption methods, along with blockchain technology, are evolving.
But wait! Shouldn’t we have more government oversight and policies to slow the attempts of bad actors? I say yes. In my mind, it will take the concerted efforts of cybersecurity experts, government, and AI developers to create and maintain a robust AI environment and prevent AI poisoning. What do you say?
Get Your Free SEO Checklist
Reference:
1. Vassilev A, Oprea A, Fordyce A, Anderson H (2024) Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations. (National Institute of Standards and Technology, Gaithersburg, MD) NIST Artificial Intelligence (AI) Report, NIST Trustworthy and Responsible AI NIST AI 100-2e2023. https://doi.org/10.6028/NIST.AI.100-2e2023