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Amazon AI Security Engineer interview questions

Amazon's AGI Security team keeps Amazon's foundation models and generative AI applications secure, and AWS hires specialist solutions architects for AI security. Amazon publishes a detailed interview preparation page for security engineers, which makes its loop the best-documented of any company here.

The Amazon AI Security Engineer interview process

Documented

Public guidance on the Amazon AI Security Engineer interview experience, including round formats and preparation priorities. Last reviewed November 4, 2026.

Official guidance for security engineers generally. The AGI or AI security team-specific loop is unverified.

RoleSecurity Engineer
  1. 1
    Technical phone screenAbout 60 minutes with a senior team member, one or two rounds depending on the role. Behavioural and technical questions, including secure code review and security fundamentals, framed by Amazon's Leadership Principles.
  2. 2
    Interview loopFive 60-minute interviews with members of the security engineering community. Each includes two or three behavioural questions answered in a situation, task, action, result format with measurable outcomes.
WHAT THEY'RE EVALUATING
  • Secure code review
  • Scripting and automation
  • Threat modeling
  • Security testing
  • Web vulnerabilities and mitigations
  • Cryptography
  • Security fundamentals

Amazon's preparation page covers security engineers generally. It is assumed, not confirmed, to apply to the AGI Security team.

Sources and review scope

Compiled from the company's careers pages and candidates' public accounts, all linked in Sources above. Different teams at one company, such as red team, product security and research, may interview differently, and the process varies with level and location. Plan with this, then confirm your own rounds with the recruiter.

Amazon AI Security Engineer salary

Each figure cites where it came from. If we found no posted or aggregated range for this company, we say so and leave the figure out.

POSTED BY AMAZON
$136K - $213KbaseEmployer posting

This band covers the title Security Engineer, Artificial General Intelligence. Compare it only with that title. One posting frequently spans several levels, so the top of a wide range reflects the most senior hire, not a typical offer.

Employer-posted base range (lowest to highest US geographic market) for a role posted 15 December 2025 and since closed, read via a job-board mirror on 4 November 2026.

Every posted band we have, by employer and level, is in the AI security engineer salary guide.

Questions modeled on Amazon loops

256 questions · 47 unlocked for you

More from the tracks Amazon's loop tests

The most-voted questions from Amazon's other rounds.

8 questions · 1 unlocked for you

Go deeper on the topics Amazon's loop tests

The tracks that map to a Amazon AI Security Engineer loop, ordered easy to hard.

The concepts Amazon's AI Security Engineer loop assumes you know

What a Amazon interviewer expects you to reason from without being told: memory traffic, KV sizing, scheduling, parallelism. Foundation concepts are free to read, the advanced ones need Premium.

SECURITY FOUNDATIONS

Foundational
Trust Boundaries, Assets and AdversariesSecurity reasoning starts with three questions: what is worth protecting, who could want it and with what ability, and where data or control crosses from one level of trust to another. A trust boundary marks a change in authority or trust. Reviewing the checks at those crossings helps explain failures such as SQL injection and prompt injection.
Foundational
Confidentiality, Integrity, Availability and AuthenticitySecurity controls exist to protect a small set of properties: confidentiality (only the right parties can read), integrity (nothing changes without authority), availability (the system works when needed) and authenticity (you know who or what you are dealing with). Naming the property a control protects is the fastest way to see what it does not protect, and AI systems put new pressure on every one of them.
Foundational
Threat Modeling with Data-Flow Diagrams and STRIDEThreat modeling is a structured way to find what can go wrong in a design before it is built. Draw a data-flow diagram, mark the trust boundaries, and walk each element through STRIDE: spoofing, tampering, repudiation, information disclosure, denial of service and elevation of privilege. The output is a list of specific threats, each with a decision: mitigate, accept, transfer or remove the feature.
Advanced🔒 Premium
Threat Modeling AI SystemsThreat modeling an LLM application uses the same data-flow diagram and STRIDE walk as any system, with new elements drawn in: the context assembler, retrieval stores, the tool layer, memory, and the model and its supply chain. Two adjustments make it work. Annotate every input to the context with its author, because any author can steer the model. And classify every control as deterministic or probabilistic, because only deterministic controls can carry a boundary.

APPLICATION SECURITY

Foundational
Injection VulnerabilitiesInjection happens when data from an untrusted source is combined with code for an interpreter, such as a SQL engine, a shell or a template engine, and the interpreter treats part of the data as instructions. The structural fix is to keep code and data in separate channels: parameterised queries, argument arrays instead of shell strings, sandboxed templates. Prompt injection is the same failure in an interpreter that offers no separate channel, which is why its defences look different.
Foundational
Cross-Site Scripting and Output EncodingCross-site scripting (XSS) is injection into a browser: attacker-controlled content ends up in a page as executable script, running with the site's origin and the victim's session. It comes in stored, reflected and DOM-based forms. The fixes are contextual output encoding (frameworks do this by default), sanitisation when HTML must be rendered, and a strict Content Security Policy as a second layer. Chat interfaces that render model output as HTML or Markdown are a new and common source.
CoreSign in
Content Security Policy in DepthA Content Security Policy is a response header the browser enforces for one document: it decides which scripts, styles, frames and connections the page may use, blocks the rest and can report violations. Host allowlists failed in practice because allowed domains host code an attacker can reuse. A strict policy trusts scripts by per-response nonce or by hash, propagates trust with strict-dynamic, and adds object-src and base-uri. Trusted Types closes DOM sinks. CSP limits XSS impact; it does not fix XSS or stop data leaving.
Advanced🔒 Premium
How Browsers Separate Origins: SOP, CORS and CSRFThe same-origin policy lets a page send requests almost anywhere but stops its script reading responses from other origins. CORS is the server's way to relax that read restriction for chosen origins; it never stops a request being sent. CSRF is the attack that exploits sending: a cross-site page makes the victim's browser submit a request with the victim's cookies. The defences are separate: exact CORS allowlists for reads, and for writes a CSRF token, Fetch Metadata checks, strict content types and SameSite cookies as defence in depth.

CRYPTOGRAPHY & PKI

Foundational
Hashes, MACs and Digital SignaturesA hash turns any input into a fixed fingerprint, but anyone can recompute it, so it proves nothing on its own. A MAC adds a shared secret key, so only key holders can produce a valid tag. A digital signature uses a private key that only the signer holds and a public key anyone can use to verify, which adds non-repudiation. Choosing between them is a question of who holds which key.
Foundational
Symmetric Encryption and AEADSymmetric encryption uses one secret key to encrypt and decrypt. On its own it hides content but does not detect tampering, which has caused real attacks. Authenticated encryption with associated data (AEAD), such as AES-GCM or ChaCha20-Poly1305, encrypts and attaches a tag that makes any modification fail to decrypt. Its one hard rule is that a nonce must never repeat under the same key.
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Public-Key Cryptography and Key ExchangePublic-key cryptography gives each party a key pair: a public key that can be shared and a private key that never leaves its owner. It enables signatures and key exchange, which lets two parties who have never met agree on a shared secret over an observed network. Protocols use it to set up a session and then switch to fast symmetric encryption. Its weak point is knowing whose public key you have, and its long-term threat is quantum computing.
Foundational
How TLS 1.3 WorksTLS 1.3 sets up an encrypted, authenticated connection in one round trip. The client and server run an ephemeral Diffie-Hellman exchange to agree on fresh keys, the server proves its identity by signing the handshake with the private key behind its certificate, and both sides confirm with a MAC over everything exchanged. After that, data flows under AEAD encryption. TLS protects data in transit between two endpoints; it does not protect the endpoints, the data at rest, or decide who is allowed to do what.

INFRASTRUCTURE SECURITY

Foundational
What Containers Isolate, and What They Do NotContainers isolate processes using Linux namespaces, cgroups, capabilities, seccomp and security modules, but every container on a host shares one kernel. A kernel bug or a misconfiguration such as a privileged container, a mounted container runtime socket or a broad host mount lets code escape to the host. For untrusted code, including code written or run by AI agents, stronger boundaries such as gVisor's user-space kernel or Firecracker and Kata microVMs put a much smaller or separate kernel between the workload and the host.
Foundational
Sandboxing Untrusted CodeAgents that write and run code, notebooks that execute model output, and platforms that run customer code all execute code nobody reviewed. A sandbox makes that safe by combining a strong isolation boundary with an environment that has nothing valuable in it: no credentials, minimal files, no network or tightly allowlisted egress, resource and time limits, and disposal after use. Isolation answers whether code can escape; the environment answers what it can do without escaping.
CoreSign in
Cloud IAM and Privilege Escalation PathsCloud identity and access management decides what every user, service and workload can do, so it is the real perimeter of a cloud environment. Privilege escalation happens when a principal can use its permissions to gain more: attaching policies to itself, passing a powerful role to a resource it creates, changing code that runs under another role, or minting credentials. ML platforms are full of such paths because training and notebook roles are often broad. Least privilege, permission boundaries, organisation-level guardrails and path analysis keep them closed.
Advanced🔒 Premium
The Inference Stack as Attack SurfaceAn LLM inference service is a stack of components that all handle untrusted input: an API gateway, authentication and quotas, request and prompt processing, tokenizers, the model server, GPU drivers, caches, logging, and the management plane. Each layer has classic risks such as parsing bugs, exposed dashboards and broken tenancy, plus AI-specific ones: cost exhaustion, model extraction through the API, prompt leakage through shared caches, and data retained in logs. Threat-modeling the stack layer by layer turns 'secure the model' into concrete controls.

LLM APPLICATION SECURITY

Foundational
Insecure Output HandlingInsecure (or improper) output handling, LLM10 in the OWASP Top 10 for LLM Applications 2026 (LLM05 in 2025), is passing model output to a browser, database, shell, template engine, URL fetcher or other interpreter without the validation and encoding you would apply to user input. Because output is shaped by every author in the model's context, an attacker who can influence the context can reach those interpreters, recreating cross-site scripting, SQL injection, command injection and SSRF.
Foundational
Data Exfiltration Channels in LLM ApplicationsAn injected model that has read private data still needs a way to get it out. LLM applications offer many: explicit tools such as email or HTTP, URLs the model asks a tool to fetch, markdown images and links that clients load or users click, link previews, and content written somewhere the attacker can read. Closing these channels deterministically is one of the most reliable defences against prompt injection, because it works no matter how the injection was phrased.
Foundational
RAG SecuritySecuring retrieval-augmented generation means three things. Retrieval must enforce the asking user's permissions in code, with permissions copied into the index and kept in sync, tenants isolated and deletions propagated. Retrieved text must be treated as untrusted, because anyone who can write a source can inject instructions or misinformation. And the answer must be handled safely on its way out. Permission failures can expose data even when no prompt injection occurs.
CoreSign in
Sensitive Information DisclosureSensitive information disclosure, LLM02 in the OWASP Top 10 for LLM Applications (2025 and 2026), covers personal data, secrets, proprietary information and other users' data leaking through an LLM application. The leak paths include retrieval without authorization, context shared across users, prompt and response logs, caches, fine-tuning data, model memorisation, third-party providers and the answer itself. Controls minimise what enters each path and enforce access before data reaches the model.

NETWORK SECURITY

Foundational
Attacker Positions on the NetworkNetwork attacks are best understood by the attacker's position: on the local link (ARP spoofing, rogue DHCP, evil-twin Wi-Fi), in name resolution (DNS spoofing, resolver compromise, registrar takeover), in routing (BGP hijacks and leaks), or as a trusted intermediary (an interception proxy or an installed root). Each position lets the attacker read or alter traffic that is not protected end to end. TLS with proper certificate validation defeats all of them unless the attacker can make the client trust their certificate, which is why the strongest network defences are authentication, plus controls that stop the attacker getting into position.
Foundational
DNS Security: Resolution, Poisoning, DNSSEC and Encrypted DNSDNS resolution passes through a stub resolver, a recursive resolver and a chain of authoritative servers, with caching at each step. Attacks target each part: forged responses that poison caches, malicious or compromised resolvers, takeovers of registrar and DNS-provider accounts, and dangling records that allow subdomain takeover. DNSSEC signs records so validating resolvers can detect forgery but provides no confidentiality; DNS over TLS and HTTPS encrypt the client-to-resolver hop but trust the resolver. TLS certificate validation remains the backstop when DNS lies.
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Trust on the Wire: HSTS, Certificate Transparency, Pinning and InterceptionTLS protects traffic only when the client correctly decides to trust the server's certificate. HSTS stops browsers downgrading to HTTP or letting users click through warnings; Certificate Transparency makes mis-issued certificates visible; CAA records restrict which CAs may issue for a domain; pinning narrows trusted keys for clients you control; and interception proxies deliberately insert a private root, creating a new trusted point with its own risks. Operating these well means automated certificate management, staged rotation, monitoring, and knowing which failures are attacks and which are configuration.
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Segmentation and Egress ControlNetwork controls matter most after initial compromise. Segmentation divides the network into zones with explicit, default-deny flows between them, from coarse zones (users, servers, management, sensitive data) to per-workload microsegmentation keyed on workload identity. Egress control limits outbound connections to what each system needs, breaking command-and-control, tool downloads, exfiltration and SSRF. Both are designed from observed flows, rolled out in monitor mode, kept as code with owners, and turned into detection by alerting on denied traffic.

DETECTION & RESPONSE

Foundational
Detection Engineering: From Hypothesis to Tested AlertDetection engineering turns knowledge of attacker behaviour into reliable alerts. Each detection starts from a behaviour attackers cannot easily avoid, depends on a data source that is confirmed to record it on the relevant assets, is tuned against history with narrow, owned exceptions, is tested by emulating the technique, and ships with a runbook and metrics. Managed as code, detections are reviewed, tested end to end, rolled out in shadow mode and retired when they stop earning their cost; ATT&CK is used to prioritise and to find gaps, not to colour a heatmap.
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MITRE ATT&CK and Sigma: a Shared Language for DetectionsMITRE ATT&CK is a knowledge base of adversary behaviour organised as tactics (goals) and techniques (how), now linked to detection strategies and the telemetry needed. Its 2026 releases split Defense Evasion into Stealth and Defense Impairment, giving Enterprise 15 tactics, and added AI-focused techniques. Sigma is a vendor-neutral rule format: a detection is written once in YAML and converted for each SIEM, with correlation rules for counts and sequences. Coverage means tested detections on collected data, not coloured cells.
Foundational
Incident Handling: Declare, Contain, Scope, Recover, LearnIncident handling turns an alert into a controlled response: declare against written criteria, assign an incident commander, contain with the least evidence-destroying action that stops harm, preserve volatile evidence, scope across hosts, identities and cloud accounts, eradicate persistence, recover from trusted states, and review blamelessly with tracked actions. NIST SP 800-61 Revision 3 (April 2025) frames this inside the Cybersecurity Framework 2.0 functions, so preparation, governance and improvement are part of incident response, not separate from it.
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Endpoint and Network Telemetry for DetectionEndpoint telemetry (process creation with command lines and parents, network connections per process, file and persistence changes, authentication and module loads) shows what ran and why. Network telemetry (flow records, DNS, proxy logs and TLS metadata) shows who talked to whom, how much and how regularly, even when payloads are encrypted. Sensors collect these through kernel callbacks and event tracing on Windows, eBPF and audit on Linux, and Endpoint Security on macOS. Attackers try to blind sensors and blend into allowed channels, so telemetry must be shipped off-host quickly, watched for silence, enriched and correlated.

Where to apply, and official Amazon resources

Straight from Amazon: open roles and the company's own hiring guidance. Prep here, then apply there.

External links to Amazon's own pages. Roles and processes change; always confirm on the official site.

ABOUT THE ROLE
AMAZON INTERVIEW FAQ
What is the Amazon AI Security Engineer interview process?▲

Security Engineer. Official guidance for security engineers generally. The AGI or AI security team-specific loop is unverified. Stages: Technical phone screen → Interview loop. Key focus: Secure code review. Based on the public sources linked on this page. Interview formats change, so confirm yours with the recruiter.

Does Amazon hire AI security engineers?▼
What is the Amazon AI security interview process?▼
What does Amazon pay for AI security roles?▼

Walk into your Amazon AI Security Engineer interview ready

A single bank spans every round, from threat modeling and code review to designing secure agents, and every answer begins with what an attacker wants. Start with the free answers and see whether the depth fits.

Every worked answer in all 17 topics, every lesson in every course, the full concept curriculum, the complete practice-test bank, and the downloadable security field guides.

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