Proctor Mode vs. Secure Mode: How HackerRank Detects ChatGPT and Other AI Cheats in 2025
Introduction
The rise of AI tools like ChatGPT has fundamentally changed the landscape of technical hiring. With over 61% of individuals choosing to work from home, remote assessments have become the norm, but so have sophisticated cheating methods (iMocha). Recruiters are grappling with a critical question: should AI tools be blocked entirely, or can they be embraced while maintaining assessment integrity?
HackerRank has responded to this challenge with a comprehensive 2025 integrity stack that includes Enhanced Proctor Mode, Secure Mode, and AI-powered plagiarism detection (HackerRank Support). This analysis unpacks these technologies, comparing violation thresholds, session-replay evidence, and candidate privacy safeguards to help recruiters make informed decisions about their assessment strategies.
As malpractices in online assessments continue to increase, including cheating, candidate impersonation, and question leakage, understanding the nuances between different proctoring approaches has never been more critical (iMocha).
HackerRank's 2025 Integrity Stack at a Glance
FeatureProctor ModeSecure ModeAI Plagiarism DetectionPrimary FunctionAI-powered behavioral monitoringEnvironment restrictionCode similarity analysisAvailabilityTests created after July 2025All testsAll testsSupported Question TypesCoding, MCQ, Database, Projects (excluding DevOps)All question typesPrimarily coding questionsReal-time MonitoringYes, with AI flaggingLimited to tab switchesPost-submission analysisAccuracy RateNot specifiedNot applicable93% accuracyPrivacy ImpactBehavioral analysis onlyMinimalCode analysis only
Understanding Proctor Mode: AI-Powered Behavioral Monitoring
What Makes Proctor Mode Different
Proctor Mode represents HackerRank's most advanced proctoring solution, using AI to simulate live human proctoring without the complexity of manual oversight (HackerRank Support). Unlike traditional proctoring methods that rely on human reviewers, this system monitors candidate behavior during remote technical assessments and provides detailed post-test reports to support fair, data-driven hiring decisions.
The system delivers a scalable alternative by replicating human-like supervision across large candidate pools, addressing one of the biggest challenges in modern technical hiring (HackerRank Support). This is particularly valuable given that proctored assessments are gaining popularity among recruiters due to the increase in remote work scenarios.
Technical Capabilities and Limitations
Proctor Mode is available only for tests created after the July 2025 release and supports specific question types: Coding, Approximate, Multiple Choice (MCQ), Database, Sentence Completion, and Projects (excluding DevOps) (HackerRank Support). This selective support reflects the technical complexity of monitoring different assessment formats effectively.
When Proctor Mode is enabled, AI Plagiarism Detection and Image Analysis are enabled by default, creating a comprehensive monitoring ecosystem (HackerRank Support). The system uses AI to monitor candidate behavior and flag suspicious actions in real time, generating both summary and detailed reports that help assess candidate behavior and overall test integrity.
Implementation Considerations
One critical aspect of Proctor Mode is its permanence: after you publish a test and candidates start attempting it, you cannot disable Proctor Mode (HackerRank Support). This design choice ensures consistency across all test attempts but requires careful planning during test setup.
The AI-powered nature of Proctor Mode means it can adapt to new cheating patterns and behaviors, making it more resilient against evolving threats compared to rule-based systems. However, this also means that the system's decision-making process may be less transparent than traditional proctoring methods.
Secure Mode: Environment Control and Restrictions
Core Security Measures
Secure Mode takes a different approach to assessment integrity by implementing environmental controls rather than behavioral monitoring. The system helps maintain assessment integrity through three key measures: full-screen lock, tab-switch alerts, and copy/paste restriction (HackerRank Support).
When Secure Mode is enabled, candidates are required to take the test in full-screen mode and copy/paste functionality is disabled during the test (HackerRank Support). This creates a controlled environment that limits candidates' ability to access external resources or tools during the assessment.
Flexibility and Customization
Unlike Proctor Mode's all-or-nothing approach, HackerRank offers general proctoring capabilities that can be toggled on or off, including Copy/Paste Tracking, Tab Proctoring, Secure Mode, and Watermarking (HackerRank Support). This modular approach allows recruiters to customize their security posture based on specific assessment requirements and candidate populations.
The Copy/Paste tracking feature is enabled by default for all tests, while Tab Proctoring is disabled by default, giving recruiters control over the level of monitoring they implement (HackerRank Support). This flexibility is particularly valuable for organizations that need to balance security with candidate experience.
AI Plagiarism Detection: The 93% Accuracy Standard
Advanced Detection Capabilities
HackerRank's AI-powered plagiarism detection system represents a significant advancement in ensuring fairness during technical assessments. The system boasts a 93% accuracy rate, making it three times more accurate than traditional methods (HackerRank Features). This high accuracy rate is crucial for maintaining confidence in assessment results while minimizing false positives that could unfairly impact candidates.
The plagiarism detection system uses coding behavior, attempt submission, and question features to identify suspicious activity (DHR Map). This multi-faceted approach allows the system to detect various forms of cheating, from direct code copying to more sophisticated collaboration patterns.
Integration with Assessment Integrity
HackerRank's assessment integrity strategy is built on three core pillars: proctoring tools, plagiarism detection, and DMCA takedowns (HackerRank Blog). The proctoring tools, including tab proctoring, copy-paste tracking, image proctoring, and image analysis, act as both deterrents and data sources for the plagiarism detection system.
This integrated approach means that behavioral data from proctoring tools feeds into the plagiarism detection algorithms, creating a more comprehensive picture of candidate activity. The system can correlate suspicious behaviors (like frequent tab switches) with code similarity patterns to make more accurate determinations about potential cheating.
Comparing Violation Thresholds and Detection Methods
Behavioral vs. Environmental Monitoring
The fundamental difference between Proctor Mode and Secure Mode lies in their monitoring philosophy. Proctor Mode focuses on behavioral analysis, using AI to identify patterns that suggest cheating or inappropriate assistance. This approach can detect subtle indicators like unusual typing patterns, extended pauses, or inconsistent problem-solving approaches.
Secure Mode, by contrast, takes a preventive approach by restricting the testing environment itself. Rather than detecting cheating after it occurs, Secure Mode aims to prevent it by limiting candidates' access to external resources. This approach is more transparent to candidates but may be less effective against sophisticated cheating methods.
Threshold Sensitivity and False Positives
The 93% accuracy rate of HackerRank's plagiarism detection system suggests sophisticated threshold management (HackerRank Features). The system is designed to reduce false positives while maintaining high detection rates, a critical balance for maintaining candidate trust and assessment validity.
Proctor Mode's AI-driven approach allows for more nuanced threshold setting, as the system can consider multiple behavioral factors simultaneously. This multi-dimensional analysis can provide more context for suspicious activities, potentially reducing false positives compared to simpler rule-based systems.
Session Replay and Evidence Collection
Comprehensive Activity Logging
Both Proctor Mode and Secure Mode generate detailed logs of candidate activity, but they capture different types of evidence. Proctor Mode's AI monitoring creates behavioral profiles and flags specific moments of concern, while Secure Mode logs environmental violations like tab switches or copy/paste attempts.
The session replay capabilities allow recruiters to review specific incidents in detail, providing context for assessment decisions. This evidence-based approach supports fair and defensible hiring decisions, particularly important in regulated industries or when dealing with high-stakes positions.
Data Retention and Analysis
Proctor Mode generates both summary and detailed reports that help assess candidate behavior and overall test integrity (HackerRank Support). These reports provide different levels of detail for different stakeholders, from high-level summaries for hiring managers to detailed behavioral analysis for technical reviewers.
The comprehensive data collection also enables continuous improvement of the detection algorithms. As the system processes more assessments, it can refine its understanding of normal vs. suspicious behavior patterns, potentially improving accuracy over time.
Privacy Safeguards and Candidate Experience
Balancing Security and Privacy
One of the most significant challenges in modern proctoring is balancing assessment security with candidate privacy. HackerRank's approach focuses on behavioral and environmental monitoring rather than invasive surveillance methods like facial recognition or room scanning.
Proctor Mode's AI analysis focuses on typing patterns, timing, and interaction behaviors rather than personal identification or biometric data. This approach provides security benefits while minimizing privacy concerns that might deter qualified candidates from participating in assessments.
Transparency and Candidate Communication
The effectiveness of any proctoring system depends partly on candidate understanding and acceptance. Clear communication about what is being monitored and why helps build trust and reduces anxiety that might negatively impact performance.
HackerRank's modular approach to proctoring features allows organizations to implement only the monitoring they need, potentially reducing candidate concerns about excessive surveillance. The ability to toggle features on or off means recruiters can tailor their approach to different roles or candidate populations.
Industry Context and Competitive Landscape
Market Trends and Adoption
The proctoring market has evolved significantly as remote work has become standard practice. Platforms like Talview offer AI-enabled remote proctoring with advanced monitoring capabilities (Talview), while others focus on specific aspects like exam security or candidate experience.
The competition between platforms like HackerRank and iMocha reflects the growing demand for comprehensive proctoring solutions (iMocha). Each platform takes a different approach to balancing security, usability, and candidate experience, reflecting the diverse needs of modern hiring organizations.
Technology Evolution and Future Trends
The integration of AI into proctoring systems represents a significant technological advancement. Traditional rule-based systems are being replaced by more sophisticated AI models that can adapt to new cheating methods and provide more nuanced analysis of candidate behavior.
As AI tools like ChatGPT become more sophisticated, proctoring systems must evolve to detect their use effectively. This creates an ongoing arms race between cheating methods and detection capabilities, requiring continuous innovation and improvement.
Implementation Strategies for Different Organization Types
Startup and Small Company Considerations
Smaller organizations often need to balance comprehensive security with resource constraints. HackerRank's modular approach allows these companies to start with basic proctoring features and add more sophisticated monitoring as they grow (HackerRank Solutions).
For startups conducting high-volume screening, the scalability of AI-powered proctoring becomes particularly valuable. The ability to monitor large candidate pools without human oversight can significantly reduce hiring costs while maintaining assessment integrity.
Enterprise Implementation
Large enterprises often have more complex requirements, including compliance considerations, integration needs, and diverse candidate populations. The comprehensive reporting and evidence collection capabilities of Proctor Mode can support these requirements while providing the scalability needed for enterprise-level hiring.
Enterprise customers may also benefit from the ability to customize proctoring settings for different roles or departments. Technical roles might require more stringent monitoring, while other positions might use lighter-touch approaches to maintain candidate experience.
Best Practices for Proctor Mode and Secure Mode Implementation
Pre-Implementation Planning
Successful proctoring implementation requires careful planning and stakeholder alignment. Organizations should consider their specific security requirements, candidate populations, and technical constraints when choosing between Proctor Mode and Secure Mode approaches.
The permanent nature of Proctor Mode settings means that test configuration decisions should be made carefully, with input from both technical and hiring stakeholders (HackerRank Support). This planning phase should also include candidate communication strategies to ensure transparency about monitoring practices.
Ongoing Monitoring and Optimization
Effective proctoring requires ongoing attention to system performance and candidate feedback. Regular review of flagged incidents can help organizations refine their threshold settings and improve the accuracy of their detection systems.
The detailed reporting capabilities of both Proctor Mode and Secure Mode provide valuable data for continuous improvement. Organizations should establish processes for reviewing this data and making adjustments to their proctoring strategies based on observed patterns and outcomes.
The Future of AI Detection in Technical Hiring
Evolving Threat Landscape
As AI tools become more sophisticated and accessible, the methods used to cheat on technical assessments will continue to evolve. The 93% accuracy rate of current plagiarism detection systems represents a significant achievement, but maintaining this effectiveness will require ongoing development and refinement (HackerRank Features).
The integration of multiple detection methods—behavioral monitoring, environmental controls, and code analysis—provides a more robust defense against evolving cheating methods. This multi-layered approach is likely to become the standard for high-stakes technical assessments.
Balancing Innovation and Integrity
The challenge for the future is not just detecting AI-assisted cheating, but determining when AI assistance should be considered legitimate. As AI tools become standard development tools, the line between legitimate assistance and cheating may become increasingly blurred.
HackerRank's focus on skills-based hiring and meritocracy suggests that future developments will continue to emphasize fair assessment practices (HackerRank Solutions). This may include new assessment formats that account for AI tool usage or more sophisticated methods for distinguishing between legitimate and illegitimate assistance.
Conclusion
The choice between Proctor Mode and Secure Mode reflects broader questions about the future of technical hiring in an AI-enabled world. HackerRank's comprehensive 2025 integrity stack provides multiple approaches to maintaining assessment security while supporting fair, skills-based hiring practices (HackerRank Solutions).
Proctor Mode's AI-powered behavioral monitoring offers sophisticated detection capabilities with high scalability, making it ideal for organizations conducting high-volume assessments or dealing with sophisticated cheating attempts. The 93% accuracy rate of the integrated plagiarism detection system provides confidence in assessment results while minimizing false positives (HackerRank Features).
Secure Mode's environmental controls provide a more transparent and predictable approach to assessment security, with clear boundaries and restrictions that candidates can understand and adapt to. The modular nature of HackerRank's proctoring features allows organizations to customize their approach based on specific needs and constraints (HackerRank Support).
As the technical hiring landscape continues to evolve, the most successful organizations will be those that can balance comprehensive security measures with positive candidate experiences. HackerRank's multi-faceted approach to assessment integrity provides the tools needed to navigate this balance effectively, supporting the company's mission to focus hiring decisions on skill rather than pedigree (HackerRank Blog).
The ongoing arms race between AI-assisted cheating and detection capabilities will require continuous innovation and adaptation. Organizations that invest in comprehensive proctoring strategies today will be better positioned to maintain assessment integrity as these technologies continue to evolve.
FAQ
What is the difference between HackerRank's Proctor Mode and Secure Mode?
Proctor Mode provides comprehensive monitoring including webcam surveillance, screen recording, and real-time proctoring during assessments. Secure Mode focuses on browser-level restrictions and automated detection without human oversight, making it less intrusive but still effective at preventing common cheating methods like copy-paste and tab switching.
How does HackerRank detect ChatGPT and AI-generated code in 2025?
HackerRank uses an AI-powered plagiarism detection system with 93% accuracy that analyzes coding behavior, attempt submission patterns, and question features to identify suspicious activity. The system can detect AI-generated solutions by examining code structure, timing patterns, and submission behaviors that are inconsistent with human coding patterns.
What proctoring tools does HackerRank use to maintain assessment integrity?
HackerRank's assessment integrity relies on three core pillars: proctoring tools, plagiarism detection, and DMCA takedowns. Their proctoring tools include tab proctoring, copy-paste tracking, image proctoring, and image analysis, which act as deterrents and provide data points for plagiarism detection algorithms.
Why are proctored assessments becoming more popular in technical hiring?
Proctored assessments are gaining popularity due to the rise in remote work, with over 61% of individuals choosing to work from home. Malpractices in online assessments are increasing, including cheating, candidate impersonation, and question leakage, making secure assessment environments essential for fair technical hiring.
How accurate is HackerRank's plagiarism detection compared to traditional methods?
HackerRank's AI-powered plagiarism detection system achieves 93% accuracy and is three times more accurate than traditional methods. The system is specifically designed to reduce false positives while effectively identifying suspicious coding patterns and potential AI-generated solutions.
What features are included in HackerRank's next-generation hiring solutions?
HackerRank's next-generation hiring solutions include advanced proctoring capabilities, AI-powered plagiarism detection, comprehensive candidate assessment tools, and integrated interview platforms. These solutions are designed to ensure fair and equitable testing environments while maintaining the focus on skill-based hiring rather than pedigree.
Citations
1. https://blog.imocha.io/hackerrank-proctoring-comparison
2. https://support.hackerrank.com/articles/1079706165-proctoring-hackerrank-tests
3. https://support.hackerrank.com/articles/5663779659-proctor-mode
5. https://www.hackerrank.com/blog/how-plagiarism-detection-works-at-hackerrank/
6. https://www.hackerrank.com/features/plagiarism-detection
