White Paper

AI security challenges: exploring Adversarial Machine Learning threats

Analysing different categories of attacks and real-world vulnerabilities in Artificial Intelligence and Machine Learning systems.

AI security challenges

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The intersection of AI and Cybersecurity

The synergy of Machine Learning and Cybersecurity reveals a landscape abundant with opportunities and hidden challenges. As the domain of Artificial Intelligence and Machine Learning continues to witness remarkable progress, the complexities of handled data grow, escalating risks for businesses. These advancements in AI and ML has led to pioneering breakthroughs while also uncovering vulnerabilities that require careful consideration. Within this context, two pivotal dimensions come to light: Offensive Machine Learning (OML) and Adversarial Machine Learning (AML). OML employs ML as an offensive tool in cyberattacks, while AML focuses on targeting ML models themselves, unearthing an array of security concerns that warrant thorough exploration. Our white paper aims to present the main categories of attacks based on whether they occur during the training or prediction phase, briefly explaining the differences between these phases.

Attacks at training time


The model training phase, crucial for shaping AI systems, becomes a battleground for malicious actors seeking to exploit vulnerabilities and compromise model integrity.

Here, we can highlight the key attack categories adversaries use in training:

  • Poisoning attacks: in this method, attackers subtly manipulate the training data, introducing perturbations that mislead the model's learning process. These perturbations can lead to skewed decision boundaries, causing the model to make incorrect predictions.

  • Backdoor attacks: backdoors, or hidden behaviors, are strategically embedded during training. These dormant patterns activate under specific conditions, allowing attackers to exert control over the model's behaviour post-training, potentially leading to malicious outcomes.

Attacks at prediction time


In the realm of Adversarial Machine Learning, challenges persist even after training, emerging during real-world application. Attacks at prediction time exploit vulnerabilities in deployed AI models, urging proactive defenses. Here, we discuss key attack dimensions:

  • Evasion attacks: malicious inputs subtly manipulate models, leading to misclassifications and incorrect judgments, impacting image recognition, language processing, and more.

  • Inference attacks: threats to data privacy arise as attackers determine if specific data points were in the training dataset, risking the exposure of sensitive information.

  • Extraction attacks: attackers clone models by reconstructing them from inputs and outputs, compromising functionality and performance across various industries.

  • Model inversion: inversion attacks leverage model parameters to reverse-engineer inputs, raising privacy concerns and potential data disclosure.

Empower your defense with Reply

At Reply, we specialise in pioneering solutions that address evolving Adversarial Machine Learning challenges. Our expert teams are dedicated to providing cutting-edge strategies that fortify your AI systems against diverse attacks, from training-time vulnerabilities to prediction-time perils.

With an in-depth understanding of attackers’ techniques, we stand ready to equip your organisation with robust defence mechanisms. Partner with us to not only navigate Adversarial Machine Learning complexities but also thrive within them, ensuring the resilience, security, and effectiveness of your AI initiatives.