10 AI Security Risks Every Developer Should Know

Artificial Intelligence is transforming how we build software, but it also introduces new security challenges that developers must understand and address.

1. Data Poisoning Attacks
Malicious actors can corrupt training data to manipulate AI model behavior.

2. Model Theft
Proprietary AI models can be stolen through API abuse or inference attacks.

3. Adversarial Examples
Carefully crafted inputs can fool AI systems into making wrong decisions.

4. Privacy Leakage
AI models might inadvertently reveal sensitive training data.

5. Prompt Injection
Language models can be manipulated through cleverly designed prompts.

6. Model Inversion
Attackers can reconstruct training data from model outputs.

7. Backdoor Attacks
Hidden triggers can cause models to behave maliciously.

8. Supply Chain Vulnerabilities
Pre-trained models might contain hidden malware or biases.

9. Evasion Attacks
Malware can be designed to evade AI-based detection systems.

10. Resource Exhaustion
AI systems can be targeted with DoS attacks consuming computational resources.

Stay vigilant and implement security best practices in your AI development workflow!