Preventing Technical Interview Cheating: A Network-Layer Framework
By Aiseptor Team · July 6, 2026
48% of technical candidates were flagged for AI assistance across 19,368 live interviews, and 61% of those flagged candidates would still have advanced past the hiring bar undetected, according to Fabric's 2026 State of AI Interview Cheating report. That second number is the one that should worry a hiring team more than the first: most of what's being flagged is still getting through.
Here's why webcam proctoring and lockdown browsers structurally miss this, and what actually prevents it in a live or take-home technical interview.
Key Takeaways
- Invisible AI overlays and on-device LLMs run outside the browser and outside the webcam's field of view, which is exactly why behavioral signals (gaze, typing rhythm) miss most of what matters.
- Take-home coding assessments face a different threat than live interviews: unlimited unsupervised time to route a problem to an AI model or a proxy solver.
- Network-layer enforcement blocks the path to AI assistance and remote-access tools directly, rather than trying to infer cheating from candidate behavior.
- Aiseptor integrates via REST API and requires no webcam, keystroke logging, or persistent software on the candidate's device.
Why Behavioral Signals Miss Modern Cheating
Gaze tracking and typing-rhythm analysis were built to catch a candidate looking up an answer or pasting code from an external source. Both signals depend on the cheating leaving a visible trace on the monitored device. An invisible AI overlay like Cluely renders answers on a transparent layer that never enters a screen recording, and a candidate reading it looks identical to a candidate thinking. A local model running via Ollama or LM Studio generates no outbound network call a proctoring tool could flag. Neither produces the kind of behavioral tell these tools are designed to detect, because neither one requires the candidate to do anything visibly unusual.
Live Interviews vs. Take-Home Assessments
The two formats fail in different ways. A live interview is supervised for an hour, so the exposure window is short but the candidate has real-time AI assistance available the moment a question is asked. A take-home assessment runs unsupervised for days, giving a candidate unlimited time to route the problem to an AI model, a proxy solver, or a colleague, with no proctor watching at all. Both are closed the same way: by controlling what the candidate's device can reach, not by trying to observe the candidate more closely.
What Actually Stops It
Aiseptor enforces a per-session, default-deny network policy on the candidate's device: only the coding platform and any documentation the interview explicitly allows are reachable. AI inference endpoints, on-device LLM processes, and remote-access tools are blocked or detected regardless of what they're named, because the enforcement targets the technique (a network call to an AI backend, a screen-capture-exclusion flag, a locally loaded model) rather than a specific tool's signature.
The enclave is ephemeral: it deploys in seconds, requires no kernel driver, and removes itself completely once the interview ends, with nothing left on the candidate's machine. There's no webcam, no microphone, and no keystroke logging, which matters for candidate trust in a BYOD hiring process where the machine belongs to the candidate, not the company.
Deploying This in a Hiring Pipeline
Integration is a REST API call into an existing coding-assessment platform or ATS, with a signed webhook carrying the integrity verdict once the session ends. This is a "complement, not replace" model: it doesn't touch how you evaluate code quality or conduct behavioral interviews. It closes the one layer those processes were never built to reach. Enterprise hiring teams can request access and start testing it against a real interview immediately; the first 5 sessions are free.
See Aiseptor for enterprise hiring, or request free access to try it on your next technical interview.
Frequently Asked Questions
How do candidates bypass traditional interview proctoring?
Invisible AI overlays and on-device LLMs both run outside the browser and outside a webcam's field of view, so neither produces a signal that browser-based or behavioral proctoring is built to catch.
Can Aiseptor detect invisible AI overlays like Cluely?
Aiseptor targets the screen-capture-exclusion flag an overlay must set to stay hidden, along with the network path it needs to reach its AI backend, rather than matching a specific tool by name.
Is this invasive for the candidate?
No. There's no webcam, microphone, or keystroke logging, and the enclave removes itself completely once the session ends.
How does integration work?
Via a REST API that triggers a security enclave at the start of a session and returns a signed integrity verdict at the end, typically integrated in under a business day.
Does this cover both live interviews and take-home assessments?
Yes. Both face the same underlying threats on different timelines, and both are closed the same way: a default-deny network policy for the duration of the session.
Can Aiseptor prevent second-device cheating?
It applies its network policy across every interface on the candidate's device, closing the path an AI assistant reached through a tethered phone or second connection would otherwise use.
