Machine Learning Engineer
hardmachine-learning-engineer-adversarial-security
What ML security risks should engineers consider (poisoning, adversarial, prompt injection)?
Answer
ML systems have unique attack surfaces.
Risks:
- Data poisoning (training data)
- Adversarial inputs
- Model extraction
- Prompt injection for LLM apps
Mitigations include input validation, rate limiting, monitoring, access controls, and separating untrusted inputs from privileged tools.
Related Topics
SecurityMLOpsReliability