How HackerRank Catches AI-Generated Code by Deploying Advanced ML Plagiarism Detection

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AI plagiarism detection is now table-stakes for fair technical hiring. With nearly a third of code now AI-generated and candidates flooding assessments with copied solutions, employers face an unprecedented challenge. The stakes couldn't be higher: HackerRank handles around 172,800 technical skill assessments daily, and ensuring their integrity is critical for companies seeking genuine talent.

Why AI-Generated Code Puts Assessment Integrity on the Line

The explosion of AI coding assistants has fundamentally changed the technical assessment landscape. According to HackerRank's 2025 Developer Skills Report, 82% of developers have already experimented with AI tools, and 55% actively use them at work. This widespread adoption creates a serious fairness problem: 73% of developers say it's unfair to compete against AI-assisted candidates in assessments.

The scale of this challenge goes beyond individual fairness concerns. When candidates submit AI-generated code that passes initial screenings, companies risk making costly hiring mistakes. The financial impact compounds quickly across organizations processing thousands of assessments monthly.

The Evolving Threat Landscape: From GPT-4 to Invisible Helpers

Traditional plagiarism detection methods are increasingly ineffective against modern AI tools. As HackerRank discovered, "GPT-4 can not only pass AP exams and simpler coding challenges; it can also bypass MOSS code similarity, which has long been the industry standard for coding plagiarism detection."

The threat has evolved beyond simple copy-paste operations. Research from competitive programming contexts shows that widely-used automated similarity checkers can be thwarted by simple code transformations or novel AI-generated code. New "invisible" tools actively evade detection through techniques like semi-transparent overlays that avoid appearing in screen recordings.

Meanwhile, the AI detection arms race continues to intensify. As one analysis noted, "The evolution of AI writing and detection tools represents a classic arms race: as generation improves, detection must adapt." This dynamic forces assessment platforms to continuously evolve their detection capabilities.

HackerRank's Two-Layer Defense: MOSS + Machine Learning

HackerRank combats AI-generated code through a sophisticated two-pronged approach. The platform employs two primary models to detect plagiarism: MOSS (Measure of Software Similarity) for structural analysis and an advanced AI-powered system for behavioral detection.

The company's machine-learning based plagiarism detection model characterizes coding patterns and checks for plagiarism based on numerous signals. This multi-signal approach has proven remarkably effective: HackerRank's advanced plagiarism detection system achieves an incredible 93% accuracy rate.

How MOSS Flags Structural Similarity

MOSS operates as the foundation layer of HackerRank's detection system. The model tokenizes candidate code and compares these tokens to detect substantial overlaps, focusing on the overall structure rather than superficial elements.

The system categorizes matches into clear tiers. It flags code as high similarity at "High: ≥ 90%" match rate, medium at 80-90%, and low below 80%. This graduated approach helps reviewers prioritize which submissions require immediate attention.

ML Overlay Targets AI Patterns

Beyond structural analysis, HackerRank's plagiarism detection system tracks dozens of signals across three categories: coding behavior features, attempt submission features, and question features. These signals capture subtle patterns that traditional similarity engines miss.

The privacy-conscious design ensures fairness: the system analyzes patterns in code submissions to indicate whether answers may be copied from or generated by unauthorized sources, all without using personal data such as gender, race, age, or location.

Inside the Model: Accuracy, Signals & Bias Safeguards

HackerRank's model demonstrates exceptional precision in real-world testing. "Currently, our advanced plagiarism detection system achieves an incredible 93% accuracy rate." The system predicts plagiarism suspicion as either High, Medium, or No categories, providing clear guidance for hiring teams.

The commitment to fairness extends beyond technical accuracy. Research has shown concerning bias patterns in AI detection tools: a Stanford University study found that detection tools falsely flagged 61.2% of TOEFL essays from non-native English speakers. HackerRank addresses these concerns by focusing on code patterns rather than linguistic style.

Transparency remains central to the system's design. As one analysis of AI detection ethics noted, "The opaque nature of AI decision-making makes it difficult for students to challenge plagiarism accusations, raising concerns about fairness and due process." HackerRank counters this by providing detailed detection reports that hiring teams can review alongside candidate submissions.

Proctor Mode: Guardrails Beyond Code

While AI detection forms the foundation, HackerRank layers additional integrity measures through Proctor Mode. This feature monitors respectfully, flags suspicious activity, and even detects invisible tools that candidates might use during assessments.

The system includes multiple monitor detection to spot external displays and image proctoring that captures the candidate's environment during tests. These visual records provide crucial context for reviewers evaluating flagged submissions.

Navigating NYC AEDT Audits & Global Fairness Rules

As AI-powered hiring tools proliferate, regulatory frameworks are emerging to ensure fairness. New York City's Automated Employment Decision Tool law, effective January 1, 2023, mandates annual bias audits for AI systems used in employment decisions.

These regulations require employers to notify candidates at least 10 business days before using an AEDT, with penalties starting at $500 for violations. The requirements reflect growing concerns about AI bias in hiring processes.

Industry data underscores the need for such safeguards. Research shows that 65% of companies use AI in their hiring process, making robust detection and fairness measures essential for maintaining equity in technical assessments.

How HackerRank Outperforms Other AI Detectors

HackerRank's multi-layered approach delivers superior results compared to traditional plagiarism checkers. The ML-based detection system is three times more accurate at detecting plagiarism than traditional code similarity approaches.

Common alternatives show significant limitations. One review found that the "Grammarly AI Detector, which I have spoken of favorably in the past, seems useless" for detecting AI-generated content. Meanwhile, tools like Turnitin achieve 98-100% accuracy for standard AI text but struggle with code-specific patterns.

HackerRank's advantage comes from combining structural analysis with behavioral signals, something text-focused detectors cannot replicate. This comprehensive approach catches sophisticated evasion attempts that slip past single-method systems.

Best Practices: Embedding Detection Into Your Hiring Workflow

Successful implementation requires thoughtful integration into existing processes. Statistics show that 30 to 50 percent of candidates cheat in online assessments for entry-level positions, making proactive measures essential.

Research from academic settings recommends "a multi-faceted approach that combines improved detection algorithms, mastery-based learning techniques, and authentic assessment practices" to ensure code originality. This same principle applies to technical hiring.

Organizations should configure their assessments to capture multiple signals beyond just the final code submission. As HackerRank emphasizes, "The unique aspect of our plagiarism detection is its ability to flag potential referencing by the candidate (not just copy-pasting), regardless of the external source, be it Stack Overflow, InterviewCoder, or even ChatGPT running on a phone!"

Keeping Technical Hiring Fair in the AI Age

Maintaining assessment integrity has become more critical than ever as AI reshapes software development. With more than 25% of Fortune 100 companies employing HackerRank, the platform's commitment to fairness impacts millions of developer careers.

The technology continues evolving to meet new challenges. HackerRank's learning model means it will only get more accurate over time, staying ahead of emerging AI coding tools.

Ultimately, the goal isn't to ban AI but to ensure fair competition. As the platform emphasizes, integrity means following agreed-upon rules, creating a level playing field where genuine skills determine success. With over 2,500 companies and 40% of developers worldwide using HackerRank, this commitment to fairness shapes the future of technical hiring.

For organizations serious about maintaining assessment integrity while adapting to an AI-augmented world, HackerRank provides the sophisticated detection capabilities needed to identify truly skilled developers, not just those with the best AI assistants.

FAQ

How does HackerRank detect AI-generated code on assessments?

HackerRank uses a two-layer system: MOSS for structural code similarity and a machine learning model for behavioral patterns. MOSS tokenizes code to find overlaps, while the ML model classifies suspicion as High, Medium, or No based on multiple signals. This combination surfaces both copy-paste and AI-generated submissions.

What signals does the ML model use beyond MOSS?

According to HackerRanks product documentation and blogs, the model evaluates dozens of signals across coding behavior, submission patterns, and question attributes. It focuses on code and interaction patterns rather than personal data, aligning with HackerRanks privacy commitments. This helps identify AI usage that simple similarity checks miss.

How accurate is HackerRank's plagiarism detection and how are results reported?

Per HackerRanks blog, its advanced system achieves about 93% accuracy in detecting suspicious submissions. Results include similarity tiers from MOSS and AI-suspicion levels from the ML model, with detailed reports to help reviewers make informed decisions.

What role do Proctor Mode and the upcoming Secure Browser play?

Proctor Mode respectfully monitors test sessions, flags suspicious activity, and helps detect invisible assistive tools, including multiple monitor use and image proctoring. HackerRank has announced a Secure Browser to create a more controlled environment, reducing opportunities for unauthorized assistance.

How does HackerRank compare to traditional detectors?

HackerRank reports that its ML system is roughly three times more accurate than traditional code-only similarity approaches because it combines structural and behavioral signals. Text-focused tools may perform well on prose but often miss code-specific patterns that HackerRanks approach captures.

How can teams integrate detection into their hiring workflow effectively?

Enable both MOSS and ML-based detection, turn on Proctor Mode, and review flagged submissions alongside candidate code. Configure assessments to capture multiple signals, document reviewer decisions for audit readiness, and align processes with emerging regulations like NYCs AEDT notifications.