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Data Poisoning: Why AI Isn’t Infallible


Despite its robust reputation, AI is not the be-all, end-all of human invention. Most artificiaI intelligence software will outright tell you that it may produce incorrect, biased or offensive answers. For example, this message appears under your message thread in ChatGPT:

Think about how AI “self-learns.” Developers, who are as innately biased as any other humans, feed the machine tons of data sourced from around the web, which was created by other people.

Malicious actors can also inject manipulated data into the training dataset to influence the model’s learning and, subsequently, its performance out in the wild.

What is Data Poisoning?

Data poisoning is a type of cyber attack that manipulates or corrupts the data used to train machine learning models. This effectively “poisons” the entire foundation on which the AI bases all its knowledge.

The goal of data poisoning attacks is to introduce biases or vulnerabilities into the trained model, causing it to produce incorrect or undesirable outputs when presented with certain inputs. By tampering with the training data, attackers can deceive the model, compromise its integrity, or exploit it for malicious purposes.

Data poisoning attacks can occur through various means, including:

  1. Data Injection: Attackers inject malicious or misleading data directly into the training dataset used to train the machine learning model. This can involve modifying existing data or adding entirely new data samples.

  2. Data Manipulation: Instead of injecting new data, attackers modify a subset of the training data to alter the model’s behavior. By subtly changing the values or characteristics of certain data points, attackers can bias the model towards specific outcomes.

  3. Adversarial Examples: Adversarial examples are crafted inputs intentionally designed to mislead machine learning models. By adding imperceptible perturbations to legitimate data samples, attackers can cause the model to misclassify or make incorrect predictions.

The consequences of data poisoning attacks can be severe, particularly if targeted at critical systems or used to manipulate decision-making processes. For example, in autonomous vehicles, data poisoning could lead to misinterpretation of road signs or traffic patterns, resulting in potentially dangerous situations.

How Can AI Developers Outsmart Data Poisoning Attacks?

Defending against data poisoning attacks requires robust security measures throughout the machine learning pipeline, including:

  1. Data Validation: Implement strict data validation techniques to detect and remove potentially poisoned or manipulated data from the training dataset.

  2. Model Verification: Conduct thorough testing and verification of the trained model’s behavior to identify any signs of biases, inconsistencies or unexpected outputs.

  3. Anomaly Detection: Identify unusual or suspicious patterns in the training data, which may indicate the presence of poisoning attacks.

  4. Data Diversity: Utilize diverse and representative datasets for training, to minimize the impact of individual poisoned samples.

  5. Regular Updates: Continuous monitoring and regular updates to models with fresh, clean training data can counteract potential poisoning effects.

Data poisoning attacks highlight the importance of maintaining the integrity and security of training data and ensuring robust defenses to protect against adversarial manipulation of machine learning models.


Artificial intelligence is rapidly advancing, globally prevalent…and not always reliable. Data poisoning is just one example of how AI responses are not always as unbiased as we might hope. That’s not to mention the risks of outdated information or plagiarism that come with AI!

It’s important to always do your own research and verify what’s really true before you invest all your trust into artificial intelligence. After all, our best technology is still only as infallible as the people who made it — and we’re only human.

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