Day 02 of Learning Adversarial AI

‎📚 Day 02 of Learning Adversarial AI ‎ ‎AI Threat Mode

ling ‎ ‎Threat modeling is a structured process used to identify possible risks in a system before attackers exploit them. In traditional cybersecurity, threat modeling focuses on networks, servers, and applications. In AI security, threat modeling must also include datasets, models, and machine learning infrastructure. Because AI systems depend on data and automated learning, their security risks are different from those of conventional software systems. ‎ ‎The first step in AI threat modeling is identifying the assets of the AI system. Assets are the valuable components that need protection. In machine learning systems, assets can include the training dataset, trained models, feature engineering pipelines, model parameters, and prediction APIs. For example, a fraud detection model used by a financial institution is a valuable asset because attackers may try to manipulate or bypass it to commit fraud. Protecting these assets is critical for maintaining system reliability and trust. ‎ ‎The second step is understanding attacker goals. Attackers may target machine learning systems for multiple reasons. Some attackers may try to manipulate predictions so the model produces incorrect results. For example, an attacker may attempt to bypass a malware detection model by modifying malicious files so the model classifies them as safe. Others may try to steal the model itself because trained models can be expensive and time consuming to build. In some cases, attackers may try to extract sensitive information from the training data, such as personal or confidential information embedded in the dataset. ‎ ‎The final step in threat modeling is mapping the attack surfaces across the machine learning infrastructure. Attack surfaces are the points where attackers can interact with the system. In an ML system, these surfaces include data collection pipelines, training environments, model storage systems, APIs that expose predictions, and monitoring systems. By carefully analyzing each stage of the pipeline, security engineers can identify where attacks might occur and design defenses before deployment. 

‎ ‎📚 AI Attack Taxonomy ‎

 ‎To understand adversarial AI clearly, it is useful to categorize different types of attacks. This structured classification is known as an attack taxonomy. It helps researchers and security engineers organize threats based on when and how they occur within the machine learning lifecycle. ‎ ‎The first category is training time attacks. These attacks target the model during the training phase. One common example is data poisoning, where attackers insert malicious samples into the training dataset. The goal is to influence how the model learns so that it behaves incorrectly in specific situations. Another example is backdoor attacks, where hidden triggers are embedded into training data so the model behaves normally most of the time but produces incorrect outputs when the trigger appears. ‎ ‎The second category is inference time attacks. These attacks occur after the model is deployed and actively making predictions. The most well known example is adversarial examples, where attackers slightly modify inputs to fool the model. For instance, a small and almost invisible perturbation added to an image can cause a computer vision model to misclassify objects. Even though the change is not noticeable to humans, it can significantly affect model predictions. ‎ ‎Another important category is privacy attacks. Machine learning models often learn patterns from large datasets that may contain sensitive information. Attackers can exploit this by attempting to recover private data from the model. Techniques such as membership inference attacks allow attackers to determine whether a specific record was part of the training dataset. Model inversion attacks attempt to reconstruct sensitive information by analyzing model outputs. ‎ ‎The final category is supply chain attacks. Modern AI systems depend on many external components such as open source libraries, pretrained models, datasets, and third party APIs. If attackers compromise any part of this supply chain, they can introduce malicious behavior into the system. For example, a compromised pretrained model downloaded from an external repository may contain hidden backdoors. Because AI development heavily relies on external resources, supply chain security has become a major concern in AI systems. ‎ ‎Understanding these attack categories helps security professionals design stronger defenses. By analyzing when attacks occur and what part of the system they target, engineers can implement monitoring, validation, and security controls across the entire machine learning pipeline. ‎ ‎Follow NextGen AI Hub for more:l ‎ ‎React with "👍" if its helpful and share 🔁 ‎