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Fin AI Security Engineer interview questions

Fin, the company formerly called Intercom, builds the Fin AI customer agent, which answers customer queries across chat, email and voice and works with Intercom's help desk and others. Salesforce completed its acquisition of Fin in September 2026. Its security team covers cloud security, detection engineering and incident response for a product that puts an AI agent in front of customers' data.

1 question tagged16 concepts to master4 core topicsrole: AI Security Engineer

The Fin AI Security Engineer interview process

Limited public data
Limited public data: no current official guide or account of a security interview loop was found as of 4 November 2026. A 2018 Intercom post described a build-and-pair practical for product engineers. Ask your recruiter for the structure.

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.

Fin 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.

NO VERIFIED BAND

We do not have a verified compensation band for this role at Fin recorded in this profile. Check the current posting for the role, level and location before comparing offers. Their careers page is the authority, and postings in some jurisdictions are required to state a range.

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

Questions modeled on Fin loops

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More from the tracks Fin's loop tests

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

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Go deeper on the topics Fin's loop tests

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

LLM app security33 →Agents & MCP47 →Infrastructure38 →Detection & IR37 →

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

What a Fin 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.

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.
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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.

AGENT & MCP SECURITY

Foundational
Agentic AI SecurityAgentic AI security protects systems in which a model proposes actions through tools. Untrusted inputs and model errors can influence those proposals, so the host and resource services must enforce the task's authority independently: which data can be read, which actions can run, what requires approval and when execution must stop.
Foundational
The Lethal TrifectaThe lethal trifecta, a term coined by Simon Willison in 2025, is the combination of three capabilities in one AI agent: access to private data, exposure to untrusted content, and the ability to communicate externally. With all three, an instruction hidden in untrusted content can make the agent read private data and send it out. Use the combination to trace a possible disclosure path, then remove unnecessary capabilities or enforce restrictions outside the model. A narrower token alone does not revoke an older, broader token.
Foundational
Excessive AgencyExcessive agency, LLM03 in the OWASP Top 10 for LLM Applications 2026 (LLM06 in 2025), is when an LLM-based system holds more functionality, permissions or autonomy than its task requires, so that a manipulated or mistaken model can cause outsized harm. The fix works on all three dimensions separately: narrow tools, least-privilege credentials scoped to the requesting user, and human or policy gates on consequential actions.
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Human Approval That HoldsHuman approval adds a person’s decision to an action that policy already permits. Trusted presentation, an authenticated approver, immutable action details and execution-side enforcement connect that decision to its effect. Review volume and usability matter, but a click, signature or second device alone does not establish informed consent or prevent duplicate execution.

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.
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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.
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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.

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.
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Where to apply, and official Fin resources

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

ApplyCareers at Fin

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

ABOUT THE ROLE
What is an AI security engineer?How AI security loops are builtWhat AI security roles payHow to become an AI security engineer
FIN INTERVIEW FAQ
Does Fin (formerly Intercom) hire security engineers?▲

Yes, in small numbers. On 4 November 2026 its board listed Cloud Security Engineer roles in Dublin and London.

What is the Fin interview process?▼

Walk into your Fin 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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