Learning Cybersecurity and Bug Hunting in the AI Era
As AI-generated vulnerability reports overwhelm some bug bounty programs, aspiring security researchers face new questions about how to build practical skills and verify findings.
Radwan AbdulhadiEditor and technology writer
The Impact of AI Reports on Bug Bounties
Finding a real security flaw and getting paid for it remains an appealing goal for those learning cybersecurity. Bug bounties offer a practical chance to test skills on real software and help developers make applications safer. However, headlines regarding programs closing or pausing under a flood of AI reports can make that career path feel uncertain for beginners.
Earlier this year, the widely used data transfer tool and library ended its paid bounty program after similar problems, as detailed by BleepingComputer. When curl ended its program on January 31, 2026, maintainer Daniel Stenberg explained that submission quality had deteriorated significantly. Confirmed vulnerabilities fell from above 15% in previous years to below 5% starting in 2025.
By April, Stenberg reported that submission quality had improved and the share of confirmed vulnerabilities returned to roughly 15% to 16%, though extensive AI use remained present in those reports. While some programs face pressure, others continue to operate, with Google's wider vulnerability rewards programs paying out $17.1 million in 2025.
Building Strong Foundations in Browser-Based Labs
For newcomers trying to understand where to put their effort, building the skills to produce verifiable findings is a crucial starting point. Before chasing payouts, aspiring researchers must learn how underlying systems function. For web security, this means understanding HTTP requests, login sessions, permissions, and how applications handle data.
Platforms like TryHackMe provide browser-based environments where learners can practice without affecting live services. You can learn cyber security hands-on, right in your browser, utilizing guided learning paths designed for every level from complete beginners to advanced practitioners.
Structured learning environments let students make mistakes, repeat tests, and inspect results safely. Beginners can start learning today by exploring resources tailored to fundamental networking, Linux, Windows, and web applications.
Structured Paths for Technical Mastery
Structured curricula help give every new tool a clear purpose and teach researchers how to understand the evidence it produces. TryHackMe offers the ultimate path to get you started on building these essential technical habits.
For a comprehensive roadmap, TryHackMe’s Cyber Security 101 learning path covers networking fundamentals, Linux and Windows operations, web application basics, alongside fundamental offensive and defensive security principles.
Working through these modules allows students to practice basic networking and command line skills, helping them investigate what happens when a tool produces an unexpected result during an assessment.
Using AI Responsibly in Cybersecurity Training
While artificial intelligence tools are frequently misused to generate overwhelming volumes of unverified vulnerability reports, AI can still serve as a valuable study assistant. Beginners can use AI to help explain unfamiliar error messages, suggest questions to investigate, or review the clarity of their lab notes.
Experts recommend attempting tasks independently first before consulting an AI model with a specific point of confusion. For instance, after inspecting a lab request, a learner might ask the model to explain how a session cookie relates to a logged-in account, rather than asking the AI to generate a solution entirely.
Concluding a study session by repeating the task without external assistance ensures that the concept has been truly understood. If a researcher can successfully explain which evidence supports a finding and what would disprove it, they have acquired a skill applicable beyond that specific exercise.
A Four-Week Practical Practice Plan
Establishing solid habits over a month of focused practice can prepare learners for real-world scenarios. During week one, students should work through networking, operating system, and web basics by drawing simple request flows and identifying where permissions are checked.
Week two involves choosing a single vulnerability type and practicing it across multiple authorized labs while keeping a concise record of the evidence behind each result. In week three, learners can integrate AI for hints after making their own attempts and revisit earlier labs without help.
By week four, researchers should write a complete report for a lab finding and have a peer attempt to reproduce it. Maintaining this focus on evidence and reproducibility ensures readiness for broader security roles, including security operations, incident response, and penetration testing.
Sources
- BleepingComputerFrom Beginner to Bug Hunter: Learning Cybersecurity in the AI Era.
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