Adversarial testing refers to the deliberate introduction of crafted inputs designed to expose weaknesses, blind spots, or unsafe behaviours in AI systems. These inputs, often called adversarial examples, are used to simulate challenging or malicious scenarios that the AI system may encounter in real-world environments. Adversarial testing plays a vital role in evaluating the robustness and resilience of AI models, especially in high-risk settings such as defence, cybersecurity, and public safety.

In the AI assurance context, adversarial testing helps to uncover vulnerabilities that may not be evident during conventional testing or validation. For example, a computer vision model used in object detection for autonomous drones might fail to identify manipulated inputs that look normal to a human observer but trigger incorrect classifications. Such vulnerabilities can result in system failures or be exploited by malicious actors.

The process of adversarial testing typically involves generating small perturbations to input data (e.g., images, audio, text) that deceive the AI model without significantly altering the input’s appearance to humans. These methods range from gradient-based attacks to black-box fuzzing techniques. For generative models, adversarial testing might involve prompt engineering to elicit unsafe, biased, or nonsensical outputs.

Adversarial testing supports safety assurance by identifying failure modes before deployment. It is closely aligned with practices such as red teaming, threat modelling, and penetration testing in cybersecurity. By proactively stress-testing AI systems, developers and operators can implement mitigation strategies — such as retraining with adversarially robust data, improving detection of anomalous behaviour, or applying input filters — to strengthen resilience.

In regulated or safety-critical sectors, adversarial testing may be required to demonstrate that a system has been evaluated under worst-case or high-risk conditions. It can also inform risk assessments, model documentation (e.g., model cards), and certification processes. Assurance teams often rely on independent third-party testers to conduct adversarial testing as part of unbiased evaluations.

Furthermore, adversarial testing is essential for AI systems deployed in contested or unpredictable environments — such as autonomous surveillance, border monitoring, or disinformation detection — where hostile or ambiguous data inputs are likely. By uncovering latent weaknesses, adversarial testing ensures systems can operate securely and effectively under stress.