TL;DR
AI-powered cheating tools have transformed interview fraud from a desperate act into a subscription service. Tools like Cluely and Interview Coder use invisible screen overlays and real-time audio transcription to feed candidates answers during live interviews.
- Cheating tools cost $20-50/month while offering access to $150,000+ salaries
- Modern tools use invisible overlays that screen-sharing software cannot detect
- 59% of hiring managers suspect candidates have used AI to misrepresent their abilities
- Detection requires analyzing behavioral signals like response timing, eye movements, and speech patterns
- AI interview platforms like Fabric can detect cheating with 85% accuracy using 20+ forensic signals
Introduction
A candidate joins your video call, answers technical questions with confidence, and demonstrates impressive problem-solving skills. They pass the interview. Three months later, they cannot perform basic tasks that any qualified hire should handle easily.
This scenario is becoming alarmingly common. The candidate you interviewed may have had an invisible assistant feeding them answers the entire time.
Interview cheating has evolved dramatically. What once required a second person in the room or a phone hidden under the desk now runs silently in the background as a $20/month subscription. Tools like Cluely, Interview Coder, and Leetcode Wizard can transcribe interviewer questions in real-time, generate expert-level responses, and display them on screen without appearing in shared video feeds.
This post explains why candidates are turning to these tools, how the technology actually works, and what recruiters can do to protect their hiring process.
Why are candidates cheating in interviews?
The rise of AI cheating tools comes down to simple economics and shifting attitudes about AI assistance.
The risk-reward calculation favors cheating. A $20-50 monthly subscription to a cheating tool is negligible compared to a potential $150,000 engineering salary. Even if a candidate gets caught occasionally, the potential payoff from one successful fraudulent hire makes the gamble worthwhile from their perspective.
Competition has intensified. Tech layoffs and increased candidate pools mean more people competing for fewer positions. Some candidates view AI assistance as a necessary equalizer, especially when they believe other candidates are already using these tools.
The line between preparation and cheating has blurred. These tools market themselves as "interview assistants" and "confidence boosters" rather than cheating software. Candidates can rationalize that using an AI co-pilot is similar to studying with ChatGPT beforehand.
Remote interviews created the opportunity. The shift to video-based hiring opened a vulnerability that did not exist with in-person interviews. When candidates control their physical environment, they can set up secondary devices, invisible overlays, and audio capture systems without detection.
Gartner projects that by 2028, one in four candidate profiles will be entirely fake, driven by generative text, synthetic voice, and deepfake technologies. The problem is accelerating: Fabric's data shows cheating adoption more than doubled from 15% in June 2025 to 35% in December 2025.
How do candidates use AI tools like Cluely in live interviews?
Modern cheating tools have solved the technical challenges that made earlier methods detectable. Understanding these mechanisms is essential for building effective countermeasures.
1. Invisible screen overlays
Tools like Cluely and Interview Coder use low-level graphics hooks to render content that exists only on the candidate's local display. When a candidate shares their screen via Zoom or Google Meet, the video encoding captures the desktop beneath the cheating overlay. The candidate sees a transparent heads-up display floating over their coding environment while the interviewer sees only the code editor.
This creates a teleprompter effect where the candidate can maintain eye contact with their work while reading real-time suggestions.
2. Real-time audio transcription
For behavioral interviews, these tools capture the interviewer's voice through virtual audio drivers. The audio runs through speech-to-text engines like OpenAI's Whisper, gets transcribed, and feeds into an LLM that generates structured responses. This entire loop from question to answer appearing on screen takes approximately 1-2 seconds.
3. OCR screen capture for coding problems
For technical assessments where problem statements appear visually, tools like Leetcode Wizard use continuous optical character recognition. The tool captures frames from the problem area, extracts text via OCR, and feeds it to models trained on competitive programming datasets. The AI generates optimal solutions with complexity analysis and humanized explanations.
4. Secondary device configurations
As proctoring platforms have hardened their defenses, cheaters adapted by separating the display from the monitored device. The cheating software runs in stealth mode on the main computer but pushes answers to a paired phone or tablet via WebSocket connection. The candidate positions the phone just below the webcam's field of view, appearing to glance at notes while reading AI-generated solutions.
How can recruiters detect AI cheating during interviews?
Detection requires moving beyond traditional proctoring methods like tab-switch monitoring or browser lockdowns. These approaches generate false positives and are easily bypassed by modern tools.
1. Watch for flatline response timing
Genuine candidates respond quickly to easy questions and pause longer for complex ones. Cheaters using AI tools show suspiciously consistent timing because the software follows the same processing steps regardless of question difficulty. A candidate who takes exactly 4 seconds to answer both "Tell me your name" and "Explain database sharding" is likely waiting for their AI to generate responses.
2. Observe eye movement patterns
When people recall information, their eyes drift upward or to the side. When reading, eyes move in horizontal lines from left to right with quick snaps back to the start of each line. Candidates reading from invisible overlays display this mechanical reading pattern while supposedly thinking through answers.
3. Listen for structural speech patterns
AI-generated responses tend to be grammatically perfect and rigidly organized. Phrases like "There are three main points to consider: First… Second… Third…" suggest reading from a script. Genuine speech includes natural hesitations, self-corrections, and restarts.
4. Use question-echoing as a tell
Many candidates stall by repeating questions while their AI processes: "So, you want to know about database scalability…" This fills the 3-4 second gap while answers generate. Frequent verbatim repetition with little substantive content in the opening seconds indicates potential tool use.
5. Ask about non-existent technologies
LLMs cannot verify information in real-time. Asking candidates to explain a fictional library or framework exposes AI dependence. A genuine candidate will say they cannot find documentation. A cheater's AI will confidently hallucinate methods and syntax for the fake technology.
How does Fabric help prevent and detect interview cheating?
Fabric takes a fundamentally different approach to interview integrity. Rather than relying on crude signals that modern tools easily bypass, Fabric conducts conversational AI interviews that make the interview itself the detection mechanism.
Adaptive questioning breaks cheating tools. When a candidate provides a perfect textbook answer, Fabric's AI immediately drills down: "Can you tell me about a specific time you applied that in a project and it failed?" LLMs struggle to maintain context when forced to pivot quickly or provide highly specific personal experiences. This context switching forces candidates off-script, revealing their actual abilities.
Multi-signal analysis catches what humans miss. Fabric's detection engine analyzes 20+ signals simultaneously: gaze tracking for reading patterns, response timing variance, keystroke dynamics, LLM-typical phraseology, and content coherence compared to resume baseline. These signals combine into a probability score indicating likelihood of synthetic assistance.
Behavioral forensics replace binary proctoring. Traditional proctoring flags tab switches with high false positive rates. Fabric analyzes the cognitive fingerprint of the interview: the micro-delays, the eye movements, the speech patterns that distinguish genuine problem-solving from script-reading.
Based on extensive human evaluations, Fabric detects cheating in 85% of cases. The platform provides timestamped reports with full analysis so hiring teams can verify results and make informed decisions.
Conclusion
Interview cheating has evolved from a fringe problem into an industry-wide crisis. The tools are sophisticated, affordable, and designed specifically to be undetectable by traditional methods.
The most important shift for hiring teams is recognizing that static assessments and passive proctoring no longer work. Cheating tools thrive on predictability. They struggle with adaptive conversations, unexpected follow-ups, and questions that require genuine experience rather than retrieved information.
Protecting your hiring process means combining interviewer training on behavioral tells with technology that can analyze the signals humans cannot perceive. The cost of a bad hire easily exceeds $50,000 in direct losses. Investing in integrity verification is no longer optional.
FAQ
What is Cluely and how does it work?
Cluely is an AI-powered interview assistance tool that uses invisible screen overlays and real-time audio transcription to generate answers during live interviews. It captures interviewer questions, processes them through language models, and displays responses that screen-sharing software cannot detect.
Can interviewers see when candidates use AI cheating tools?
Most modern cheating tools are designed to be invisible to screen-sharing software. However, they create detectable behavioral patterns including consistent response timing, reading eye movements, and overly structured speech that trained interviewers and AI detection systems can identify.
What is Fabric?
Fabric is an AI-powered interview platform that conducts conversational technical interviews while analyzing 20+ behavioral and linguistic signals to detect cheating. It uses adaptive questioning and multi-signal forensics to verify candidate authenticity.
How effective is Fabric at detecting interview cheating?
Based on extensive human evaluations, Fabric detects cheating in 85% of cases. The platform provides detailed timestamped reports so hiring teams can review the specific signals that triggered alerts.
What should recruiters do if they suspect a candidate is cheating?
Ask unexpected follow-up questions that require specific personal experiences. Introduce fictional technologies to test whether candidates verify information. Use platforms like Fabric that analyze behavioral signals beyond what human observation can detect. Document patterns across multiple interviews to identify systematic issues.