👉 Opponents' engineering strategies often focus on creating vulnerabilities in AI systems that can be exploited to undermine their performance or security. These strategies include adversarial attacks, where inputs are intentionally modified to cause misclassification or incorrect outputs; data poisoning, which involves corrupting training data to degrade model accuracy; and model inversion attacks that aim to extract sensitive information from the model itself. Additionally, opponents may employ evasion techniques, such as generating inputs that bypass detection mechanisms or exploit model weaknesses through iterative testing. These tactics highlight the ongoing arms race between AI developers and adversaries, emphasizing the need for robust defensive measures in AI systems.
