Is Cluely Detectable? (2026)

Short answer: Cluely's overlay is hard to see on a shared screen, but "detectable" is a bigger question than what shows up in a Zoom window. Recruiters now have detection vendors that train against Cluely by name, behavioral systems that watch the candidate instead of the software, and — separately — a 2025 breach that turned the product itself into a trust problem. This page walks through each, with a source on every factual claim. If you want the full product review (pricing, history, verdict), read the Cluely review; this one is only about detectability.

We build Interview Coder, a desktop AI assistant for live coding interviews, so Cluely competes with us in the broad sense. Read this with that bias in mind — and check every link.

"Can Cluely be detected?" collapses three different questions that have different answers:

Keep these separate, because most "is it detectable" debates mix them up. Below, each gets its own answer.

Cluely is a native desktop app for macOS and Windows, not a browser extension. Its overlay renders through low-level GPU hooks rather than as a normal capturable window, which is why Zoom, Google Meet, and Microsoft Teams do not pick it up during a screen share (Wikipedia). Detection vendor Fabric describes the same mechanism in technical terms: an overlay of this class "renders at the GPU level rather than in the application layer, so screen-sharing software simply cannot see it" (Fabric).

That architecture matters because the video-conferencing tools have no AI-on-the-shared-frame detector. Zoom, Meet, Teams, and Amazon Chime do not analyze what's on your screen for cheating — the only thing that can betray a tool is appearing in the captured pixels, and those pixels are produced by the operating system's compositor (Zoom share docs, Google Meet present docs). On Windows, a window marked with SetWindowDisplayAffinity(WDA_EXCLUDEFROMCAPTURE) is removed by the Desktop Window Manager from every capture surface — the exact APIs those conferencing apps use (Microsoft docs). On macOS, when you share a single window through the modern system picker, the requesting app only receives the streams for the selected windows and everything else stays private (addpipe).

The one architectural caveat, and it is real: relying only on the legacy macOS NSWindow.sharingType=.none flag is no longer enough on macOS 15+, because ScreenCaptureKit ignores it and composites all windows to one framebuffer it reads directly (tauri-apps issue #14200, Apple developer forums). Robust invisibility depends on per-window picker isolation, not the deprecated flag. The point is architectural: a native, window-excluded overlay is structurally outside the screen-share stream in a way a browser tab can never be — a browser tab is itself a capturable surface and gets composited into any full-screen share (Zoom share docs).

So on question 1 — the shared pixels — Cluely's overlay genuinely is hard to see, and independent reviewers concede it (Cluely review). That is not the end of the story.

Question 2 is where Cluely's situation differs from a less-famous tool. Detection is not only about pixels anymore. A class of interview-monitoring vendors has emerged whose entire pitch is catching invisible assistants, and Cluely is a named target.

Fabric scores 20+ behavioral signals per interview and calls out Cluely by name. Fabric's system combines gaze tracking, response-timing consistency, keystroke dynamics, language-pattern analysis, and eye-movement patterns into a single cheating-probability score, claiming roughly 85% detection across a study of 19,368 interviews (Fabric). Fabric writes about overlay tools like Cluely and Interview Coder explicitly as the threat its product is built to catch (Fabric). The independent Cluely review makes the same point from the other direction: the viral "Cheat on Everything" marketing that built the brand also made it the tool recruiters train their systems against first.

Sherlock AI runs a multimodal adversarial classifier. It combines device activity, audio environment, and candidate behavior, and monitors "eye movement, gaze patterns, and engagement anomalies" to flag live-interview cheating (Sherlock, Sherlock blog).

But read what these tools actually detect. None of them fingerprints the Cluely binary on the machine. Fabric's most-cited tell is the uniform response-lag loop — a candidate who shows "nearly identical delays regardless of question complexity," waiting four to five seconds to state their name and four to five seconds to explain database optimization (Fabric). The eye-movement analysis distinguishes recall (unfocused, drifting) from reading ("smooth horizontal sweeps from left to right") on the webcam (Fabric). The other signals are in-browser telemetry — clipboard activity, tab-switching, secondary-screen detection (Fabric).

Here is the tell made concrete. Picture the interviewer asking two questions back to back: "What's your name?" and "How would you index this table for a range query?" A candidate reading both answers off a hidden overlay pauses about the same length of time before each — roughly four to five seconds — because the bottleneck is reading, not thinking (Fabric). A candidate answering from their own head fires the easy question instantly and visibly works through the hard one. The flat, content-independent latency curve is the signal, and in our own experience watching practice rounds the giveaway is almost never the screen — it is a candidate who reads a fluent answer they then cannot defend in the follow-up. That is question 3 wearing question 2's clothes. These systems flag the candidate's behavior and environment, not the overlay's presence. They are triggered identically by someone reading off a phone or a second monitor. A native overlay produces no browser event to log, no enumerable display, and — when window-excluded — no capturable pixels; what's left is same-screen eye movement and response timing, which the systems target regardless of how help arrived. Fabric's own description concedes that the overlay "renders at the GPU level... so screen-sharing software simply cannot see it" — its detection rests on behavior, not on seeing the tool (Fabric).

The honest read: Cluely's fame is a real liability for question 2. When a product is a household name, detection vendors write their playbooks against it first. That is a marketing-driven disadvantage, separate from how good the overlay is — and it is one no general-purpose "cheat on everything" tool can shed.

The traditional assessment platforms — HackerRank, CodeSignal, Codility, CoderPad — run their integrity stacks inside the browser, and that bounds what they can see. HackerRank's tab proctoring "works by monitoring browser focus events and detecting when the assessment window loses focus" (HackerRank); its copy-paste interception and full-screen enforcement are page-level (HackerRank Secure Mode). CoderPad's paste handler fires only on clipboard paste into its own editor, and its "clicked away" flag is a browser focus event on the pad's tab (CoderPad). CodeSignal and Codility proctoring runs inside Chrome or Firefox and relies on the page Visibility API, getUserMedia, and the browser's screen-share prompt (CodeSignal, Codility). Proctorio is explicitly a Chrome extension with no native install; every lockdown signal it has is bounded by the extension sandbox (Proctorio).

A native overlay sits outside that sandbox. It never pastes into the IDE, never moves browser focus, and is not a tab or window the extension can enumerate. Here is where each platform's integrity layer actually runs, and what it can and cannot reach:

The one real exception — and it matters — is HackerRank's Desktop App Mode. This is an optional, locked-down native client a company can require instead of the normal browser test. When it's in play, the "browser-bound" logic above no longer holds: HackerRank's own Knowledge Base states the app "detects and closes any other programs that candidates try to open during the test, including invisible cheating tools such as Cluely and Ultracode." That is the platform naming Cluely, by name, as something it detects and shuts down. So the accurate version is: in a standard browser-based HackerRank assessment a native overlay sits outside the sandbox, but if the company has switched on Desktop App Mode, Cluely is detected — and you usually won't know in advance which mode you're walking into. We cover each platform's mechanisms in detail in the sibling pages: how HackerRank detects cheating, the broader HackerRank cheating overview, and CoderPad cheating detection.

The exception worth naming: the strongest non-browser layer these platforms have — HackerRank's ML plagiarism engine, CodeSignal's Suspicion Score, Codility's Similarity Check — is content-based, scoring the code you submit against leaked, AI-generated, and prior-candidate solutions (HackerRank ML detection, CodeSignal). That reacts to what gets typed, not to any overlay being present. It is a separate risk surface, and it is one no overlay — Cluely or otherwise — addresses. CoderPad itself states it "does not directly detect external AI tools running on the candidate's desktop" (CoderPad).

Question 1 through 3 all assume the threat is someone catching you mid-interview. The 2025 breach is a different exposure: the product was the leak.

In mid-2025, attackers accessed Cluely and exposed personal data, interview transcripts, and screenshots from more than 83,000 users (Medium, BlueDot). The cause was not exotic — reporting on the incident says Cluely's developers left an admin password file in a public GitHub repository, which, combined with weak GraphQL protections and a client-side paywall check, handed attackers unrestricted access (Medium).

Sit with what that data is. Cluely is designed to capture everything on your screen. A breach of that store is not a leaked email list — it is potentially the screenshots and transcripts of whatever you used it for, including interviews (BlueDot). That is detectability after the fact: not a recruiter seeing the overlay, but your session sitting in a dataset someone else now holds. It is a trust point about the vendor, sourced and separate from the overlay's screen-share behavior — and it is the kind of risk that matters most for a tool whose whole premise is surveilling your screen.

Putting the three questions back together:

No real-time tool can honestly promise zero detection risk, and proctoring platforms keep investing in detection (how HackerRank detects cheating). The honest framing is architectural, not a guarantee: a native window-excluded overlay is the structurally hardest thing for each platform to flag, platform by platform — but the residual exposure is your behavior and your vendor's security, neither of which the overlay fixes.

Same architecture class, different focus. Interview Coder is a native desktop app — not a browser extension — that uses OS-level window exclusion so it does not appear in screen captures, takes no keyboard focus from the test window, stays out of the dock and Activity Monitor, and is driven entirely by keyboard (how HackerRank detects cheating). Architecturally, that means it produces none of the in-browser events HackerRank, CoderPad, CodeSignal, Codility, and Proctorio log, and nothing for those browser extensions to enumerate.

On the company's own record, Interview Coder reports 100,000+ users and zero documented detection cases. We will not turn that into "undetectable on every platform, guaranteed," because nobody can honestly make that claim — the behavioral and content-similarity surfaces above are real, and they apply to every tool. What we will say is narrower: built only for coding interviews, running coding answers on Claude Sonnet 4.6, the product gives you a solution you can defend in a follow-up — which is the exact thing the behavioral detectors and the in-person round are probing for, and the exact thing a context-free snippet fails.

If you're comparing the field with the same sourcing rules, see the Parakeet AI review, the Cluely review, and our roundup of the best AI interview tools for 2026.

How this stacks up against every other platform's detection is laid out in our guide to undetectable AI interview tools.

Interview Coder is a native desktop app used by 100,000+ engineers for live coding interviews. It reads the problem on your screen and generates working solutions with explanations in real time, powered by Claude Sonnet 4.6, with OS-level window exclusion, keyboard-only control, and no browser extension to enumerate. Start free at $0, go Monthly Pro at $299, or pay $799 once for Lifetime Pro — no subscription, nothing to cancel. Full disclosure: this guide is published by Interview Coder, which competes with Cluely.