Meta Security Engineer Interview Questions

Authentication proves identity, while authorization controls what that identity can do. I would design both with least privilege, short-lived credentials where possible, and strong auditability so access can be reviewed later. The most important thing is to make access decisions explicit and consistently enforced.

I would segment the network, restrict east-west traffic, enforce strong service identity, and limit administrative access paths. Then I would add monitoring for unusual authentication patterns, privilege escalation, and unexpected host-to-host communication. The goal is to make one compromised system much less useful to an attacker.

I would examine model inputs, data storage, access controls, dependency risks, API exposure, and whether sensitive training data could leak through logs or outputs. I would also look for abuse cases such as prompt injection, model extraction, or unauthorized inference access. Security for ML is both traditional application security and model-specific risk management.

I would keep secrets out of source code, store them in a dedicated secrets management system, and scope access as narrowly as possible. I would also rotate them regularly and monitor for accidental exposure in logs, configs, and build artifacts. Good secret handling depends on both storage and operational discipline.

I would acknowledge it quickly, validate the report, assess severity, and coordinate remediation through the appropriate internal process. Then I would keep the reporter informed and ensure the fix is verified before closure. A strong response balances urgency, professionalism, and careful validation.