Adversarial AI and Model Poisoning Attacks

Adversarial AI is a technique where attackers manipulate machine learning models to generate incorrect outputs. By exploiting weaknesses in AI systems, cybercriminals can bypass security measures or corrupt AI-driven defenses.

Types of adversarial AI attacks:

• Evasion Attacks: Attackers alter inputs (e.g., modifying malware files) to trick AI models into classifying them as benign.
• Data Poisoning: Attackers inject malicious data into training datasets, leading to biased or faulty AI models.
• Model Extraction: Cybercriminals use AI to replicate proprietary machine learning models and exploit them for malicious purposes.

One real-world example is adversarial attacks against facial recognition systems, where slight alterations to an image can cause an AI model to misidentify individuals. As AI security measures evolve, attackers are finding new ways to exploit them, making adversarial AI a major cybersecurity challenge.