It created a new challenge for cybersecurity experts to detect the anomaly and deal with the potential threat. In the previous cyberattacks that involved malware, phishing, ransomware, or social engineering attacks, it was easy to detect the malicious elements creating a disruption inside a system, but in the data poisoning attacks, it is not easy to prevent the dangers so easily. Traditional attacks were external, which involved identifiable malicious programs, codes, URLs, or unusual behavior. But data poisoning attacks come from within and are intermingled with the original information, which makes them hard to identify and eliminate.
Why is Data Poisoning Far More Dangerous?
No doubt, data poisoning is far more dangerous than other types of cyber attacks. It targets the brain of the AI and ML models, causing them to generate fake, erroneous outputs and reprogram the core functions of a system. It corrupts their learning process at the root level, and things go in the wrong direction from the very beginning, eroding the trust of the users in the AI models.
Data poisoning causes the predictions and output of the machine to be biased and flawed. Cybercriminals use data poisoning tactics to confuse or bias learning sets of machine learning models and manipulate AI models to generate bad data. Users take malicious output as legitimate and implement the process to achieve the desired results in their professional and public lives. But as soon as they implement the output obtained from the tampered AI models, they suffer a loss in different ways.
The poisoned data looks normal and natural when it is given by an artificial intelligence. The bad data, disguised as good data, easily convinces the users to take rash actions that trigger more extensive attacks and disruptions. When an AI model is corrupted at its core, it becomes highly difficult to identify and fix the error. Fixing the error means you have to start everything from scratch, which involves filtering the data for its errors, retraining or reprogramming the models all over again. It is a time-consuming and expensive process that can be highly disruptive for the people who are directly associated with the affected AI models.
What are the Different Types of Data Poisoning?
There are different types of data poisoning based on the methods and targets cybercriminals use to tamper with or manipulate AI and ML systems by feeding bad information to the machines. They use both internal and external gateways and sources to inject malicious information into the learning data sets of AI models. On top of these facts and figures, here are some of the most common types of data poisoning you will come across:
Availability Attacks
Availability attack is a classic example of data poisoning that is used to make the entire AI model work badly. In this attack, the malicious actors feed wrong, messy, and misinformation to the training model, causing it to learn the wrong thing as right and throwing off its learning. For example, the cybercriminals label bad data as good data, attribute wrong facts and features to different things. You can imagine this situation by a scenario in which a system is taught to spot animals, labeling horses as lions and lions as horses. It gets confused and starts giving you wrong outputs.
This is similar to a situation in which a student is given a book of poems to prepare for a physics exam. No matter how hard a student learns the book, he will never be able to qualify for the physics exam. In the availability attacks, the learning sets of data are misinformed and attribute wrong things to trick the system into inhibiting the wrong things as right at the root level. This type of data poisoning gives bogus output, spoiling everything at its core.
Integrity Attacks
Integrity attacks are sophisticated data poisoning attacks in which bad actors tweak the training data in a subtle way that makes the machine learning model make mistakes on certain points and outputs, and everything else looks fine. The overall information and operations remain the same, but only a certain piece of data is manipulated and changed to give wrong outputs.
You can relate this error by equating it with a spam filter that is capable of identifying most of the malicious emails, but leaves messages that are tampered with small variations to bypass the security system. The tampered data acts like an undercover backdoor that opens only when you know the right way. You won’t be able to detect the small errors and follow things without much thinking.
Backdoor Attacks (a subtype of Integrity)
In the backdoor data poisoning, the attackers feed the secret data to the machine learning models. This secret data renders the model trigger the wrong answer, but only when that trigger shows up. The attackers add a small pattern to the training model to manipulate it to react to the feed in a specific way. When you give it a certain type of command or prompt, it will react in a certain harmful or negative way to that command and give you some information that should have been hidden. It provides a backdoor to launch further attacks on the users. The manipulation is so subtle that a normal user won’t be able to detect it.
Targeted Attacks
Targeted data poisoning attacks are carried out to target an individual or achieve specific goals. In the targeted attacks, the bad actors manipulate the training sets of machine learning by injecting misinformation about a specific person or towards a specific goal. The slightly tweaked data gives wrong information about the target and builds the perception of the people around. Except for this one case, everything else remains the same, which makes people around believe in the output that AI provides. This method puts the targeted person or element vulnerable to a wide range of risks and dangers.
Clean-Label Attacks
Clean label attacks are highly complicated data poisoning attacks in which the machine learning models are fooled without changing the labels on the data. In these types of attacks, the bad actors create inputs that look normal and are labeled correctly. But in reality, they are designed in a way that quietly misleads the model during training. It behaves like a Trojan horse that looks harmless from the outside, but inside, the trouble is waiting to unfold. Adversaries input normal data to the machine learning model with a slight variation and alteration, but it leads to misclassification of the correct data. Clean-label data poisoning uses vague and hidden methodology to manipulate machine learning transformers to trick the whole system into producing wrong reports, less accurate predictions, and describing things as something different for small variations.
Model Inversion Attacks
In this type of attack, black hats manipulate the machine learning models to reveal the data it was trained on. It is like reverse engineering of the AI model and its protocols to compromise the programs and processes upon which it runs. The attacker sends a lot of inputs to the model and carefully observes the responses coming out of the model. Based on their observation, they begin to guess or rebuild different parts of the training data. The model leaks valuable data through its own answers and outputs. When the attackers ask the same questions repeatedly, it starts giving blurry but recognizable facts about the sensitive and private information. The attackers can reach the right information using these vague facts and figures over time.
Model inversion attacks pose a serious privacy risk for the models that are trained on sensitive data like financial, healthcare, legal, and personal details of individuals. It is silent and sneaky because it does not trigger any security alert when leaking private information. The process doesn’t involve breaking into the system; rather, it is just the regular process of asking questions and giving prompts to the Artificial intelligence machines.
Previous Incidents of Data Poisoning!
If we look at some previous incidents of data poisoning attacks, it gives a big picture that describes how it can affect us in different ways. It is capable of corrupting AI systems, manipulating the outputs, and creating a reality that is bogus and damaging in various ways. It can dismantle the whole foundation of truth from its very core. Once the foundation is manipulated and altered, everything built on it will lead to disruptions and bad results. Here are some examples of previous data poisoning incidents in big corporations.
Microsoft Tay Chatbot (2016)
Microsoft launched its chatbot on Twitter to interact with users and learn from their conversations. After its launch for twenty-four hours, users flooded it with offensive and toxic language. The learning sets assimilate this data into their algorithm and begin posting this very harmful content itself. In this case, the AI learned directly through the online crowd poisoning. It got easily manipulated with the user’s interactions and started behaving badly on its own. Microsoft had to stop this process after some time when the whole thing converted into an RR disaster. Hence, without a proper security protocol, it is difficult to monitor and protect AI engines from running in a useful and harmless way.
Tesla Autopilot / Traffic Sign Attacks
In this incident, some researchers found that Tesla’s Autopilot, which uses cameras and AI to recognize traffic signs, could be fooled into misreading traffic signs by making very small changes. They placed small stickers or added tiny patterns to a stop sign. When the car interacts with these signs, it does not stop. The AI misread the sign as a speed limit of 45 instead of a stop. It shows how small visuals can poison the perception-based AI models to respond in the wrong way.
If the car fails to detect a stop sign and keeps running instead of stopping, then it can lead to serious accidents. This incident shows how just adding a small addition to the stop sign can affect the results of Tesla’s Autopilot system. This is another classic example of how vulnerable AI can be and how it fails to recognize signs, while a driver can easily see the difference by looking at the surroundings.
Twitter Bot Training Poisoning
This is another classic example of data poisoning that exposes the limitations of AI models and cybersecurity challenges. In this incident, bots and fake accounts were used to flood Twitter with biased, fake, or misleading content. When these fake accounts were used to flood Twitter with biased, bogus, and misleading content, the training sets pick up and learn the biased content and take it as normal and real-world conversation. It failed to differentiate between the real and the genuine information.
The ML models process tons of online data, which is full of errors and biases. The fake posts create an automated system that is fed lies and serves lies to the people without understanding their reality. During Twitter Bot Training Poisoning process is clear that just a few thousand fake posts can manipulate AI to promote and spread misinformation. This tendency is also visible in the creation of deepfake media files and texts to promote and spread misinformation.
What are the Signs of Data Poisoning in AI Models?
If you are using any automated or AI-driven software or programs to do your professional and daily work, then it is highly important that you ensure they are not data poisoned. You have to check that they are giving you the right and valid information in the relevant contexts. To do so, you must ensure your AI and ML programs are free of adulteration and misleading data sets. Here are some key signs that help you identify data poisoning possibilities of your programs:
Sudden Drop in Model Accuracy
If your AI model is not performing optimally or fails to give you clean and valid output, then it could be data poisoned. There must be some corruption or misplaced information in the data sets that is causing the sudden drop in model accuracy, and giving relevant and valid information.
Strange or Inconsistent Predictions
If your spam filter is allowing spam or your facial recognition is not able to identify or misidentify the faces, then they know that there is something fishy. Unexpected errors are strong indications of messed-up datasets inside the ML engine. The specific error cases are the result of targeted poisoning inside the system. Be careful, the target can be you or your professional business.
High Overall Accuracy with Specific Failures
When you see that everything is alright, but some specific cases always behave or respond in a certain inaccurate way. It is a strong sign of integrity or a backdoor attack. It is configured or manipulated to mislead you or take the wrong step, which will disrupt normal.
Biased or Skewed Outputs
When you see biased or skewed responses for certain groups, topics, and opinions, know that your AI machine is being manipulated and fed with bad information. The one-sided and biased results are signs of data poisoning at the core level of the machine learning system. Somebody is trying to build a narrative about certain things and people to spread lies or mobilize public opinion in a direction that is based on discrimination.
Unusual Training Behavior
Longer training time, unstable loss curves, spikes in validation errors are all signaling to corrupt data sets in the machine learning engine. If you are experiencing any of the elements during your interaction with the AI models, then stop using them because they are exposing you to security vulnerabilities and misinformed initiatives.
How to Protect Against Data Poisoning?
Protecting against data poisoning is one of the most important things if you are thinking of implementing AI models in your life for seeking information, automating business processes, communication, or education. You have to make strict protocols to train your machine learning models and use only authentic and valid data, clean of biases, errors, subtle manipulations, stealthy alterations, covert tampering, imperceptible variations, and camouflaged distortions. Here are some effective safety measures you should take to prevent data poisoning of your AI and ML models:
1. Use Trusted and Curated Data Sources
When you are training your AI and ML models, make sure you use verified datasets, internal and monitored sources. Avoid data collected from unknown and insecure sources that are open and crowdsourced. Poisoned data is often injected from unsafe and harmful public sources. Avoiding such random sources can protect your AI models from bad and misinformed datasets.
2. Monitor Training Data for Anomalies
Make sure you constantly monitor and filter your training data for mislabeling, duplication, outliers, inconsistencies, and suspicious patterns. Do not let a single piece of bad information slip into your system. Think of it like food safety, where you check every ingredient and follow the proper hygiene to keep yourself healthy and protected from food poisoning. Small bits or pieces of bad food can have serious effects on your health. So is the case with the AI models, where if you let any poisoned dataset slip into your system, it can have severe consequences down the line.
3. Robust Validation & Testing
You must follow a robust validation and testing protocol to check the accuracy and validity of the datasets you are feeding to your machine learning engines. Here are some parameters that you can use to implement robust validation and testing on your training datasets.
- You must check the edge cases for unusual, rare, and extreme situations falling outside the normal range of data. Bad actors often use edge cases to inject malicious and bad content into your AI engines. These attacks are difficult to notice and identify.
- Rare inputs are other tactics that show up very infrequently in the training data. These inputs rarely come up in the datasets that make the model unable to detect them. For example, if you train an AI model to classify animals and see a larger number of dogs and cats but only one picture of a capybara, this one picture is a rare input. Bad actors can easily do data poisoning using the rare inputs to affect the particular outputs.
- For a robust testing and validation process, you must also check how well the model performs for different groups of people or categories such as age, gender, race, and religion. For example, you can see that a face recognition system can work differently for different groups of people. It can recognize young faces quickly, but when it comes to recognizing children, it may not be good enough. To ensure your AI model performs at an optimal level, even for subgroups, you have to get rid of these small anomalies.
4. Limit Who Can Contribute to Your Data
You must choose who can contribute to your training sets, how much they can submit, and what can be taken in if you are publicly sourcing data from online platforms. You have to set clear limitations on access privileges to the core training set development. Proper management and restrictions can help you stop attackers from flooding your system with bad content.
5. Use Differential Privacy or Noise Injection
You must use a differential privacy method to protect personal and sensitive information to training the AI models or analyzing the data during the training sessions. It can hide sensitive data patterns, reduce over-reliance on any one example, or make it harder for attackers to manipulate model behavior. Most importantly, this method is highly useful when it comes to boosting privacy and fairness in the machine learning model.
6. Train with Robust Algorithms
Use robust and advanced algorithms to train your machine learning models, as they are more immune to data poisoning attacks. Here are some examples of how you can train your models with the robust algorithms:
- Adversarial Training: It is a stress training in which you train your model to avoid sneaky and harmful patterns to keep things straight and seamless.
- Outlier-Resistant Loss Functions: This model measures its own mistakes during the learning process. If attackers poison a model, outlier-resistant doesn’t overreact to strange or extreme data points and reduces its effects on the overall model.
- Data Sanitization Tools: Using data filtration and sanitization tools is one of the most valuable methods that keep a machine learning engine safe from bad and malicious inputs.
7. Retrain and Audit Regularly
If you are getting bad and misinformed output, then you must inspect your AI models and retrain them with fresh, valid, and relevant datasets. Run bias, integrity, and security audits and watch for model drift and performance shifts. Prevention is better than poisoning the data, as it can become highly difficult to sort out things when it is already polluted.
