Adversarial AI refers to the use of artificial intelligence techniques and algorithms to create or exploit vulnerabilities in AI systems for malicious purposes. It involves the deliberate manipulation or attack on AI models, data, or algorithms to deceive or compromise the intended functionality of the AI system. Adversarial AI techniques are designed to exploit weaknesses in AI systems, leading to misclassification, evasion, or manipulation of their outputs. 

The significance of adversarial AI in the cybersecurity landscape is twofold. First, as AI technologies become increasingly integrated into various domains, including critical infrastructure, finance, healthcare, and defence, the potential impact of adversarial AI attacks becomes more significant. Adversaries can exploit vulnerabilities in AI systems to gain unauthorized access, steal sensitive information, manipulate decision-making processes, or cause disruptions with severe consequences. 

Secondly, traditional security measures and techniques may not be effective in defending against adversarial AI attacks. Adversarial AI leverages the unique characteristics of machine learning algorithms to craft attacks that are difficult to detect and mitigate. This challenges the trustworthiness and reliability of AI systems, raising concerns about the integrity and robustness of the technology. 

As AI continues to advance and become more pervasive, understanding adversarial AI and developing countermeasures becomes crucial for cybersecurity practitioners. It is essential to identify and address the vulnerabilities that adversarial AI exploits to ensure the security and trustworthiness of AI systems. By studying adversarial AI, cybersecurity professionals can gain insights into potential attack vectors, develop robust defence mechanisms, and enhance the resilience of AI systems against malicious actors.