Haqiqiy xavfsizlik kamchiligini topish va buning uchun pul olish — kiberxavfsizlikni o'rganayotganda jozibali maqsad. Bug bounty dasturlari sizga haqiqiy dasturiy ta'minotda o'z mahoratingizni sinab ko'rish va uning ishlab chiquvchilariga uni xavfsizroq qilishda yordam berish imkoniyatini beradi.
AI hisobotlari to'foniga duchor bo'lib, dasturlar yopilishi yoki to'xtatilishi haqidagi sarlavhalar bu yo'lni kamroq ishonchli his qilishiga olib kelishi mumkin.
Joriy yilning boshida, keng tarqalgan ma'lumot uzatish vositasi va kutubxonasi bo'lgan curl, shunga o'xshash muammolar tufayli o'z pullik bounty dasturini tugatdi.
Yangi boshlovchi sifatida, bu qarorlar sizning sa'y-harakatlaringizni qayerga yo'naltirish haqida amaliy savol tug'diradi.
Foydali boshlang'ich nuqta — nima noto'g'ri ketganini tushunish, so'ngra kimdir tekshira oladigan topilmalarni ishlab chiqarish uchun ko'nikmalarni shakllantirishdir.
Nima uchun ba'zi bug bounty dasturlari orqaga chekinmoqda
Bug bounty dasturi belgilangan qoidalar to'plami ostida mos keladigan xavfsizlik topilmalarini mukofotlaydi. Ishlab chiquvchilar har bir yuborishni ko'rib chiqishi, muammoni qayta tiklashi va uning ta'sirini baholashi kerak.
Xayoliy zaiflik haqidagi ishonchli hisobot kichik xavfsizlik jamoasi haqiqiy muammolar uchun kerak bo'lgan vaqtni sarflashi mumkin.
Curl o'z dasturini 2026-yil 31-yanvarda tugatganida, texnik xizmat ko'rsatuvchi Daniel Stenberg yuborishlar sifati qanday yomonlashganini tushuntirdi. U zaifliklar avvalgi yillarda 15% dan yuqori bo'lganidan 2025-yildan boshlab 5% dan pastga tushganini tasdiqladi. Dastur o'z faoliyati davomida 87 ta tasdiqlangan zaiflikni aniqlashga yordam bergan va tadqiqotchilarga 100 000 dollardan ortiq pul to'lagan.
Bu raqamlar aynan curl tajribasini tasvirlaydi.
Google'ning to'xtatilishi ham aniq doiraga ega: ta'minot zanjiri yuborishlari va ko'rib chiqilmagan hisobotlar ta'sirlanmaydi. Uning kengroq zaiflik mukofotlari dasturlari 2025-yilda tadqiqotchilarga 17,1 million dollar to'lagan. Yopilishlar va cheklovlar muayyan dasturlarga bosimni ko'rsatadi, boshqa imkoniyatlar esa davom etmoqda.
Umid uchquni
Aprel oyida Stenberg yuborish sifati yaxshilanganini va tasdiqlangan zaifliklar ulushi taxminan 15% dan 16% gacha qaytganini xabar qildi.
U bu hisobotlarda keng AI foydalanishini kuzatdi. Tadqiqotchilar haqiqiy muammolarni topishgan, garchi o'sib borayotgan hajm hali ham texnik xizmat ko'rsatuvchilar uchun ish yaratgan bo'lsa-da.
O'rganayotgan kishi uchun dars — topilmaning ortidagi dalillar uchun mas'uliyatni o'z zimmasiga olishdir. Hisobot yaratish, uning da'vosi to'g'ri ekanligini aniqlashdan ancha oson.
Topilmani tekshirish uchun kerakli asoslarni qurish
Birinchi to'lovingizni ta'qib qilishdan oldin, siz sinab ko'rishni rejalashtirgan tizimlar qanday ishlashini o'rganing. Veb-xavfsizlik uchun bu HTTP so'rovlarini, kirish sessiyalarini, ruxsatlarni va ilovalar ma'lumotlarni qanday qayta ishlashini tushunishni anglatadi.
Asosiy tarmoq va buyruq qatori ko'nikmalari vosita kutilmagan natija berganida nima sodir bo'layotganini tekshirishga yordam beradi.
Bu g'oyalarni o'rganish uchun mo'ljallangan muhitda mashq qiling. Siz xato qilishingiz, sinovni takrorlashingiz va natijani jonli xizmatga ta'sir qilmasdan tekshirishingiz mumkin. Har bir sessiya uchun aniq mahoratni tanlang, masalan, so'rovni tushuntirish yoki qaysi hisob resursga kirish huquqiga ega ekanligini aniqlash.
TryHackMe'ning Cyber Security 101 o'rganish yo'li tarmoq, Linux va Windows asoslari, veb-ilova asoslari, hamda hujum va mudofaa xavfsizligini qamrab oladi. Tuzilgan yo'l orqali ishlash har bir yangi vositaga maqsad beradi va u ishlab chiqaradigan dalillarni tushunishga yordam beradi.
Laboratoriyadan so'ng, nima o'zgartirganingizni, nima sodir bo'lganini va nima uchunligini yozib oling. So'ngra vazifani yozuvlaringiz yopiq holda takrorlang. Natijani tushuntirish va qayta tiklash qobiliyati sizning taraqqiyotingizni tekshirishning foydali usulidir.
O'rganishingizni qo'llab-quvvatlash uchun AI dan foydalaning
AI notanish xatoni tushuntirishga, tekshirish uchun savollarni taklif qilishga yoki yozuvlaringizning aniqligini ko'rib chiqishga yordam berishi mumkin. Agar siz avval vazifani o'zingiz bajarib, modelga aniq chalkashlik nuqtasini bersangiz, ko'proq foydali mashq olasiz.
Masalan, laboratoriya so'rovini tekshirgandan so'ng, modeldan sessiya cookie'si kirgan hisob bilan qanday bog'liqligini tushuntirishni so'rang. Agar siz tiqilib qolsangiz, bitta maslahat so'rang va allaqachon nima sinab ko'rganingizni tasvirlang. So'ngra keyingi qadamni sinab ko'ring va nima uchun ishlaganini tushuntiring.
Sessiyani yordamdan foydalanmasdan vazifani qayta bajarish bilan yakunlang. Agar siz qaysi dalil topilmani qo'llab-quvvatlashini va nima uni rad etishini tushuntira olsangiz, siz bu mashqdan tashqarida ham foydalanishingiz mumkin bo'lgan narsani o'rgangan bo'lasiz.
Har bir hisobotni qayta tiklash mumkin qiling
Ruxsat etilgan o'quv ilovasini tasavvur qiling, unda ikkita sinov hisobi bor. Hisob-faktura identifikatorini o'zgartirish muvaffaqiyatli javob qaytaradi. Kirish nazorati kamchiligini aniqlash uchun siz hali ham bitta hisob boshqa hisobning shaxsiy sinov hisob-fakturasiga kirishi mumkinligini va bu ilovaning mo'ljallangan ruxsatlarini buzishini ko'rsatishingiz kerak.
Siz namoyish eta oladigan xatti-harakatni, shu jumladan, bu sodir bo'lishi uchun zarur shartlarni yozib oling. Kengroq ta'sir haqidagi har qanday da'voni dalillarga bog'lang.
Topilmani yuborishdan oldin, hisobotingiz quyidagilarni o'z ichiga olganligini tekshiring:
- Ta'sirlangan aktiv yoki dasturiy ta'minot versiyasi, sizning sinovingiz dastur doirasiga kirishini tasdiqlovchi ma'lumot bilan.
- Muammoni qayta tiklash uchun zarur bo'lgan sozlash va aniq qadamlar, shu jumladan, har qanday zarur hisob ruxsatlari.
- Kutilgan xatti-harakat va haqiqiy natija, tegishli so'rovlar, javoblar yoki boshqa dalillar bilan qo'llab-quvvatlangan, sezgir ma'lumotlar olib tashlangan holda.
- Namoyish etilgan xavfsizlik ta'siri, har qanday taxminlar yoki sinovdan o'tkazilmagan imkoniyatlar aniq aniqlangan holda.
Birinchi oyingizga aniq maqsad bering
Bir oylik yo'naltirilgan mashq sizga foydali odatlarni shakllantirishga yordam berishi mumkin. Tezlikni boshlang'ich nuqtangizga moslashtiring va taraqqiyotni nima tushuntira olishingiz va takrorlay olishingiz bilan o'lchang:
- Birinchi haftada tarmoq, operatsion tizim va veb asoslari ustida ishlang. Oddiy so'rov oqimini chizing va identifikatsiya va ruxsatlar qayerda tekshirilishini tushuntiring.
- Ikkinchi haftada bitta zaiflik turini tanlang va uni bir nechta ruxsat etilgan laboratoriyalarda mashq qiling. Har bir natija ortidagi dalillarning qisqa yozuvini saqlang.
- Uchinchi haftada o'z urinishingizdan so'ng maslahatlar uchun AI dan foydalaning. Avvalgi laboratoriyani yordamsiz qayta ko'rib chiqing, so'ngra himoyachi muammoni aniqlash uchun foydalanishi mumkin bo'lgan jurnallar yoki boshqa dalillarni tekshiring.
- To'rtinchi haftada laboratoriya topilmasi uchun to'liq hisobot yozing. Hamkordan sizning ko'rsatmalaringizdan uni qayta tiklashni so'rang, yoki ularni o'zingiz yangi sozlashdan kuzatib boring va har qanday yetishmayotgan narsani tuzating.
Oxirida siz o'z fikrlashingizning yozuvi va yaxshilashingiz mumkin bo'lgan hisobotga ega bo'lishingiz kerak. Hali ham yordamga muhtoj bo'lgan joylarda mashq qilishni davom eting. Jonli dasturga tayyorlik sizning mahoratingiz va uning qoidalariga bog'liq, shuning uchun oyni boshlang'ich nuqta sifatida foydalaning.
Karyera variantlaringizni ochiq saqlang
Bug bounty ishi xavfsizlik ko'nikmalarini qo'llashning bitta usuli. Dalillarni tekshirishning bir xil odati xavfsizlik operatsiyalarida, hodisalarni javob berishda va kirish sinovlarida muhimdir.
Laboratoriyani tekshirganingizda, himoyachi muammoni qanday aniqlashi va ishlab chiquvchi uni tuzatish uchun nima o'zgartirishi mumkinligini tushuntirishni mashq qiling.
Bounty daromadi topilmaning haqiqiy, mos keladigan va dastur to'lov qoidalari bilan qamrab olingan bo'lishiga bog'liq.
Agar siz qurayotgan ko'nikmalaringiz bilan taraqqiyotni baholasangiz, o'rganish barqaror bo'lishi osonroq bo'ladi. Aniq laboratoriya yozuvlarini saqlang va o'quv platformasi ruxsat bergan joylarda yozuvlarni ulashing, shunda siz xavfsizlik rollarini o'rganayotganda muhokama qilish uchun aniq ishga ega bo'lasiz.
Keyingi sessiyangiz uchun bitta mashqni tanlang, o'z urinishingizni qiling va natijani tushuntiradigan dalillarni saqlang.
Va agar siz yo'l bo'ylab yo'l-yo'riq kerak bo'lsa, TryHackMe sizni haqiqiy xavfsizlik muhitida boshlashingiz mumkin bo'lganidek boshlash uchun yakuniy yo'lni taklif qiladi.
TryHackMe tomonidan homiylik qilingan va yozilgan.
Finding a real security flaw and getting paid for it is an appealing goal when you are learning cyber security. Bug bounties give you a chance to test your skills on real software and help its developers make it safer.
Headlines about programs closing or pausing under a flood of AI reports can make that route feel less certain.
Earlier this year, curl, the widely used data transfer tool and library, ended its paid bounty program after similar problems.
As a beginner, these decisions raise a practical question about where to put your effort.
A useful starting point is understanding what went wrong, then building the skills to produce findings that someone else can verify.
Why some bug bounty programs are pulling back
A bug bounty program rewards eligible security findings under a defined set of rules. Developers must still review each submission, reproduce the issue and assess its impact.
A convincing report about an imaginary vulnerability can consume time that a small security team needs for genuine problems.
When curl ended its program on January 31, 2026, maintainer Daniel Stenberg explained how the quality of submissions had deteriorated. He confirmed vulnerabilities fell from above 15% in previous years to below 5% starting in 2025. Across its lifetime, the program had helped uncover 87 confirmed vulnerabilities and paid researchers more than $100,000.
Those figures describe curl’s experience specifically.
Google’s pause also has a defined scope: supply chain submissions and outstanding reports remain unaffected. Its wider vulnerability rewards programs paid researchers $17.1 million in 2025. The closures and restrictions show pressure on particular programs, while other opportunities continue.
A glimmer of hope
In April, Stenberg reported that submission quality had improved and the share of confirmed vulnerabilities had returned to roughly 15% to 16%.
He observed extensive AI use in those reports. Researchers were finding real problems, although the growing volume still created work for maintainers.
For someone learning, the lesson is to take responsibility for the evidence behind a finding. Generating a report is easy compared with establishing whether its claim holds up.
Build the foundations you need to check a finding
Before chasing your first payout, learn how the systems you plan to test work. For web security, that means understanding HTTP requests, login sessions, permissions and how applications handle data.
Basic networking and command line skills help you investigate what is happening when a tool produces an unexpected result.
Practise these ideas in an environment designed for learning. You can make mistakes, repeat a test and inspect the result without affecting a live service. Choose a specific skill for each session, such as explaining a request or identifying which account is allowed to access a resource.
TryHackMe’s Cyber Security 101 learning path covers networking, Linux and Windows fundamentals, web application basics, and offensive and defensive security. Working through a structured path gives each new tool a purpose and helps you understand the evidence it produces.
After a lab, write down what you changed, what happened and why. Then repeat the task with your notes closed. Being able to explain and reproduce the result is a useful way to check your progress.
Use AI to support your learning
AI can help explain an unfamiliar error, suggest questions to investigate or review the clarity of your notes. You will get more useful practice if you attempt the task first and give the model a specific point of confusion.
After inspecting a lab request, for example, ask the model to explain how the session cookie relates to the logged-in account. If you are stuck, ask for one hint and describe what you have already tried. Then test your next step and explain why it worked.
Finish the session by doing the task again without assistance. If you can explain which evidence supports the finding and what would disprove it, you have learned something you can use beyond that exercise.
Make every report possible to reproduce
Imagine an authorized training application with two test accounts. Changing an invoice identifier returns a successful response. To establish an access control flaw, you still need to show that one account can access the other account’s private test invoice and that this violates the application’s intended permissions.
Record the behavior you can demonstrate, including the conditions required for it to happen. Keep any claim about wider impact tied to evidence.
Before submitting the finding, check that your report includes:
- The affected asset or software version, with confirmation that your testing falls within the program's scope.
- The setup and exact steps needed to reproduce the issue, including any required account permissions.
- The expected behavior and the actual result, supported by relevant requests, responses or other evidence with sensitive information removed.
- The demonstrated security impact, with any assumptions or untested possibilities clearly identified.
Give your first month a clear purpose
A month of focused practice can help you establish useful habits. Adjust the pace to your starting point and measure progress by what you can explain and repeat:
- In week one, work through networking, operating system and web basics. Draw a simple request flow and explain where identity and permissions are checked.
- In week two, choose one vulnerability type and practise it across several authorized labs. Keep a short record of the evidence behind each result.
- In week three, use AI for hints after making your own attempt. Revisit an earlier lab without help, then examine the logs or other evidence a defender could use to spot the activity.
- In week four, write a complete report for a lab finding. Ask a peer to reproduce it from your instructions, or follow them yourself from a fresh setup and fix anything missing.
By the end, you should have a record of your reasoning and a report you can improve. Continue practicing wherever you still need help. Readiness for a live program depends on your skills and its rules, so use the month as a starting point.
Keep your career options open
Bug bounty work is one way to apply security skills. The same habit of checking evidence matters in security operations, incident response and penetration testing.
When you investigate a lab, practice explaining how a defender could detect the issue and what a developer could change to fix it.
Bounty income depends on a finding being valid, eligible and covered by the program’s payment rules.
Learning will be easier to sustain if you judge progress by the skills you are building. Save clear lab notes and share write-ups where the training platform permits them, so you have concrete work to discuss as you explore security roles.
For your next session, choose one exercise, make your own attempt and keep the evidence that explains the result.
And if you need guidance along the way, TryHackMe offers the ultimate path to get you started just as you would in an actual security environment.
Sponsored and written by TryHackMe.