When AI Gets Tricked: The Threat of Adversarial Attacks

Artificial Intelligence (AI) systems have become an integral part of modern life, powering applications in healthcare, finance, security, and more. These systems excel at learning patterns and making decisions, but their reliance on data-driven models also makes them vulnerable to exploitation. Despite their transformative potential, AI systems are not immune to risks, especially when faced with intentional manipulation. Adversarial attacks, where malicious inputs are designed to deceive AI models, expose the fragility of these systems and challenge their reliability in critical applications.

Adversarial attacks are like tricking a smart system on purpose. Adversarial attackers deliberately alter input-data to cause models to produce incorrect predictions or disclose sensitive information.

Types of Adversarial attacks

1. Poisoning: Manipulating the data before it is used for training the AI model
2. Evasion: Manipulating Input to cause the model to generate incorrect prediction
3. Model Extraction: Querying the model to reveal model’s architecture & parameters
4. Inference: Querying the model about sensitive information in its training data

Poisoning Attack

Model poisoning occurs when attackers deliberately manipulate training data, such as by mislabeling it, to mislead an AI model. This causes the system to produce incorrect outputs even on correctly labeled data. The most common causes of model poisoning include sourcing training data from unreliable sources like Common Web Crawl or public datasets, as well as frequent retraining without proper data validation and checks. These vulnerabilities can lead to biased decision-making, reduced accuracy, and exploitable weaknesses in AI systems.

Example: An attacker poisons an AI spam filter by injecting emails into the training data that look like spam but are labeled as legitimate. As a result, the model learns to misclassify spam as safe, allowing harmful content to pass through undetected.

Evasion Attack

An evasion attack is a type of adversarial attack where an attacker manipulates inputs during the inference stage (after the model has already been trained) to trick the AI system into making incorrect predictions or classifications. Evasion attacks can compromise the reliability of AI systems.

Example: Imagine a spam detection system trained to classify emails as spam or legitimate. An attacker might slightly alter the wording or structure of a spam email to bypass the filter. To a human, the email still looks like spam, but the system misclassifies it as legitimate because of the subtle changes.

In self-driving cars, attackers can add stickers or paint patterns on stop signs to confuse the AI into misidentifying them as speed limit signs. While the changes are subtle, the model’s perception is manipulated, potentially causing dangerous outcomes.

Model Extraction Attack

A model extraction attack is when an attacker tries to steal or replicate a machine learning model by analysing its outputs. The attacker repeatedly queries the model with carefully chosen inputs and observes the results to infer how the model works internally. This allows them to create a copy (or approximation) of the original model without having direct access to it. Model extraction attacks compromise the confidentiality of the AI model and open the door to other attacks, such as bypassing security features or conducting other adversarial attacks.

Example: Suppose an AI company builds a proprietary fraud detection model. An attacker can query the model with different transaction data and observe the outputs. Over time, they can create a copy of the fraud detection model and use it to evade detection in their fraudulent activities.

Inference Attack

An inference attack occurs when an attacker exploits a trained AI model to extract sensitive information about the data it was trained on. Instead of targeting the model’s performance or functionality, the attacker focuses on uncovering private details, such as individual data points or patterns from the training dataset. This occurs due to insufficient data scrubbing and sanitisation. Inference attacks can lead to privacy violations, regulatory non-compliances and trust issues.

Example: Imagine a healthcare AI model trained to predict diseases based on patient data. An attacker could query the model with slightly altered patient records and, based on the model’s responses, infer sensitive details like whether a specific patient has a particular disease or whether their data was included in training.

Adversarial attacks in AI underscore the importance of building robust, secure, and trustworthy machine learning systems. As AI continues to permeate critical domains like healthcare, transportation, and security, understanding and mitigating these attacks is no longer optional — it’s essential. While adversarial attacks reveal the vulnerabilities of AI systems, they also highlight opportunities for innovation in defense mechanisms, such as adversarial training, anomaly detection, and secure data handling. By prioritising security at every stage of AI development and deployment, we can ensure that these systems remain reliable, resilient, and capable of driving progress in a safe and ethical manner.

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