Amazon Cloud, Container & AI Infrastructure Security interview questions

The question is really 'what does hostile code have to break to reach the host?' Answer that for each option and the choice follows.

One link-local address hands out the machine's credentials to anything that can make a request from it. That includes a fooled agent with a fetch tool.

The provider secures the model service. Everything you put into it, connect to it and build on top of it is still yours.

Every layer parses something an attacker wrote, every request costs real money, and tenants share hardware. Walk the path and price each risk.

Training jobs need privileged-looking things: device access, big volumes, fast networking. Hardening is about granting exactly those and nothing else.

GPUs were built for throughput, not mutual distrust. Each sharing mode makes a different promise about memory, and some make none.

Containers are a bundle of kernel features, each limiting a different thing. The scored answer explains what each restricts, what it does not, and how to combine them for an untrusted workload.

Managed AI services remove servers, not responsibility. The scored answer covers identity, network, data, model, logging and governance controls that remain the customer's.

A customer model is customer code: loading it can execute anything, and serving it shares expensive hardware. The scored answer isolates execution, scans and constrains artefacts, and keeps tenants apart on GPUs and networks.

An AI request can cost a thousand times more than a web request, so volumetric thinking alone fails. The scored answer layers network, request and cost controls, and protects the GPU tier from ever seeing junk.

An AI system's behaviour lives in more places than its code. The scored answer versions every one of them together, so you can return to a known-good state in minutes after a bad change or a poisoning.

Most cloud breaches start with a setting, not an exploit. The scored answer moves those settings into reviewed code, checks them before deployment, and catches the drift that happens anyway.

Cloud attackers mostly use the same APIs your engineers do. The scored answer collects the right logs, writes detections for attacker behaviour, and keeps the logs out of the attacker's reach.

GPU nodes carry layers most patch programmes forget: drivers, firmware, CUDA libraries and container toolkits. The scored answer inventories them, prioritises by exposure, and patches without breaking month-long training runs.

Serving images bundle a model runtime, GPU libraries and dozens of Python packages. The scored answer controls what goes in, proves where it came from, and admits only verified images to production.

Sending prompts to a provider is a data flow like any other. The scored answer keeps it on private paths where possible, forces all model traffic through one controlled exit, and blocks everything else.

GPU hosts attract cryptominers and hold valuable weights and data. The scored answer contains without destroying evidence, scopes across the fleet, and rebuilds rather than cleans.

Serving nodes parse hostile input all day, so assume one falls. The design question is what it can reach: the registry, other tenants' models, the signing keys, or nothing at all.

Everything in Kubernetes goes through the API server, so most attacks are about who can talk to it and with what rights. The rest is what a pod can do to its node and to its neighbours.

Most escapes are not clever kernel exploits. They are configuration that handed the container the host: a privileged flag, a mounted socket, a host path or a shared namespace.

A bucket looks simple, but its policy, the identities that can touch it, its encryption keys, its logs and its lifecycle rules are each a way in or a way to lose it.

Snapshot a disk, read it somewhere else, delete the snapshot. That trick built a product category. Interviewers ask how it works, what permissions it needs, and what it cannot see.

Four open-source tools, four different questions: what happened, what is the state now, is this behaviour forbidden, and can the kernel stop it. Choosing well starts with knowing which question each answers.

IAM decides what a role may do; organisation guardrails decide what no role in an account may ever do. The scored answer explains the evaluation, the blind spots and a first set worth deploying.

RBAC decides who may create a pod; admission decides what kind of pod may exist. The scored answer layers the built-in options, handles webhook failure modes and remembers that the admission path is itself attack surface.

Assume the code is hostile and design until that stops mattering. Then the interviewer adds a GPU and the internet, and you design again.

One copy is a total loss. The design is about how few places weights exist, how few people can touch them, and whether a bulk copy can leave unnoticed.

Training clusters hold the data and the weights, and run code from hundreds of dependencies. Default-deny egress is the control that holds when everything else is fooled.

Many ML tools assume they run on a trusted network and offer 'run this code' as a feature. Put them on the internet and that feature is remote code execution.

The people who build frontier models need access to them, which is exactly the risk. The scored answer reduces who can touch weights directly, makes exfiltration hard and visible, and does it without poisoning research culture.

A framework that ties security investment to attacker capability. The scored answer explains the five levels, why the top ones are qualitatively different, and how labs use the idea.