If you want to go deeper after this article, also read the step-by-step cybersecurity and OSINT roadmap and the DevSecOps transition guide for QA, Dev, and SysOps.
Today attackers rarely “break the fence around the office”—they break the web application: payment and access logic, social login, the dependency chain, and data exchange between services. Perimeter controls still matter, but the main punch lands in code and API configuration. Companies want people who can verify security before release and ship a clear plan, not only watch lectures.
This article is engineer to engineer: no motivational speeches. The goal is to show how Application Security reviews access rights, validation, and DoS safeguards, and how that becomes regression in CI. If you come from automation, the long read on SDET and Security Testing explains why “200 OK” can be a security defect. The full mentorship program lives on Application Security Engineer.
Who this mentorship is for
The primary focus is QA. Examples and homework are mostly from testing: negative paths, system boundaries, login and permission checks, and the mindset that “a vulnerability is still a product defect”. If you are a developer or admin, you are welcome—you may need to get comfortable with tester language first: clear repro steps, what you saw in the response and logs, and what it means for the business.
Manual QA. You already ask “what if the user does something wrong”—add “what if someone abuses the API on purpose”, for example swapping another user’s or order’s ID (BOLA/IDOR in professional jargon). The goal is to present findings so the team accepts them.
Test automation (SDET and adjacent roles). You know CI and flaky tests—that is a strong base: you can lock security checks into Playwright and stop catching the same holes after every merge.
Developers. You know code—we add the “other side” lens: where authorization was forgotten, what looks suspicious in a pull request, and how manual review pairs with automated code analysis—without abstract “hacking for hacking”; everything ties to your product’s behavior.
System administrators. You are strong in OS, networking, and logs—we map that to web apps: how to read traces when a service misbehaves, and not stop at “we tightened the server” when the bug is in API logic.
DevOps and SRE. You run Docker and secrets—we show how that connects to web app security: images, where libraries come from, and agreements with development without endless “you only slow us down” conflict.
Other engineers. If your role is different but you have time and discipline, we discuss entry: what to refresh on HTTP, APIs, and reporting culture. If pace or background does not fit, I will say so directly.
Why a mentor if courses already exist
Mass-market courses optimize for one stack and one average pace. Mentorship optimizes for your product surface: the APIs you ship, the CI you already run, and the evidence format your team trusts.
You get feedback on real attempts—not only “answers from a workbook”: how you wrote the repro, how you argued risk, and why a noisy assertion can undermine trust in the entire suite.
Comparison: mass courses vs mentorship, QA vs web app security
| Dimension | Mass courses | Individual mentorship |
|---|---|---|
| Feedback | Chat or forum; rarely a personal review of your work | Review of your reports, repro attempts, and how you argue risk |
| Depth of practice | Same lab tasks for everyone | Tasks closer to your stack and to defect types you see in real life |
| Job preparation | Generic interview tips and resume templates | Typical question drills and review of your practice or GitHub examples |
| Real stack | One training stack for the whole cohort | Your CI (Jenkins, GitHub Actions, and so on), your APIs, agreements under NDA |
| Dimension | QA automation | Web application security testing |
|---|---|---|
| Salary (Europe/US, very rough) | Strong QA automators face a lot of competition; growth is not unlimited | Web app security specialists are rarer; scarcity and responsibility pay more |
| What you own | Release quality, regression, APIs matching product expectations | Who may access what, login and sessions, user data, incident triage |
| Tools | Playwright or Cypress, builds, test metrics | OWASP ZAP, JavaScript/TypeScript scripts, Playwright, code and service scanners when needed, Docker, basic WAF rule intuition |
Why Application Security is not a fad—it is a people shortage
Employers rarely need someone who only recites OWASP. They need someone who can show where an attack came from, what broke in logic, what it costs the business, and what to do next. From feature design to post-release monitoring—that is a normal security loop, not a one-off “check before release”.
- Money: preventing personal-data leaks or mass access to other people’s orders is cheaper at development time than firefighting scandal, fines, and refunds.
- Technology: more services and internal APIs; a common mistake is “the user logged in” without checking whether they may see someone else’s data.
- Career: people who connect code, infrastructure, and clear explanations for management match the web application security engineer profile.
Typical web and API problems and how we hunt them
Below is not a decorative OWASP checklist—it is how products actually break and how a tester or engineer approaches it.
Access to other people’s data via API (BOLA / IDOR)
Services return links with user, order, or document IDs in the path. Teams often verify “is the person logged in?” but not “are they the owner of this record?”. In multi-service systems, the edge may block a stranger while an internal service trusts another service and skips re-checks.
For the company that means personal-data leaks, wrong-party payments, and regulator attention. For an attacker, incrementing IDs and watching responses is often enough.
How we test: a role×object matrix, two test users, response comparison; we also look at bulk operations and GraphQL, where authorization is easy to forget.
Login tokens (JWT): signature exists, semantics are wrong
JWT is popular because payload lives in the token. Typical mistakes: weak secrets, wrong signing algorithms, effectively infinite lifetime, mixing “who logged in” with “what they may do” in one pile of fields.
Token bugs hit money: mass session takeover, privilege escalation; fixes are expensive—reissue everything and rework refresh flows.
How we test: careful experiments on staging with headers and keys, scenarios where one service accepts a token and another rejects it, and checks that the token is actually bound to a client if the product claims that.
Injections into queries and commands
SQL injection often hides in sorting, reports, and search—not only “obvious forms”. NoSQL has its own variants. Another class is user-controlled text reaching a system command (PDF generation, file processing).
Impact ranges from reading other people’s data to full control of the host running the service—an incident with response deadlines, not a “small bug”.
How we test: meaningful bad inputs with encodings and edge protections in mind, places that bypass normal DB libraries, and secondary fields in JSON and XML.
SSRF: the server fetches a URL for the attacker
Risky wherever the backend downloads a user-supplied URL: link previews, webhook tests, PDF generation, integrations. In cloud setups, one hole often yields internal metadata credentials.
Cost: secret theft and lateral movement from a single mistake.
How we test: controlled internal addresses on staging, attention to redirects and “blind” cases where the response is hidden but server behavior changes.
XSS: malicious script on a page
Rarely “just a popup” today—usually a path to session theft and actions on behalf of the user. Modern headers and browser policies help, but they can be misconfigured too.
For the business: fraud, reputation damage, support load. In training we focus on a clear proof of “what an attacker can actually do”, not an empty alert.
How we test: different injection contexts (HTML, URL, styles), typical filter bypasses, careful chaining with other page mechanics where your staging allows it.
Example: a naive API and a Playwright detector
A simplified teaching example: the same API call as two users. If the server returns user B’s order to user A, the test fails—useful for catching regressions in CI. Real projects add retries, user data from files, and CI reports.
GET /api/v1/orders/78421 HTTP/1.1
Host: api.example.com
Authorization: Bearer <TOKEN_USER_A>
200 OK
{ "orderId": 78421, "ownerUserId": 9912, "total": 120.5 }import { test, expect } from '@playwright/test';
test('orders: cross-user access should be denied', async ({ browser }) => {
const userA = await browser.newContext({ storageState: 'auth/userA.json' });
const userB = await browser.newContext({ storageState: 'auth/userB.json' });
const orderOwnedByB = '78421'; // from a legitimate userB response
const resA = await userA.request.get(`/api/v1/orders/${orderOwnedByB}`);
expect(resA.status(), 'User A must not read user B order').toBe(403);
await userA.close();
await userB.close();
});Six months: plan and what you have at the end
This matches the course page program (“Program” section): five large blocks spread across six months (about ten hours per week total, including homework—not only live calls), so each month ends with something concrete: a report, a request collection, or a test. Everything is QA-first; developers and admins follow the same stages with an adjusted start for your background.
Month 1 — foundations. How requests and encryption work in the browser, login and sessions, logs, Linux and the shell, databases and SQL with a security lens, API exploration in Postman or Insomnia (including races and permission mistakes), HTML and a little JavaScript for typical bug patterns. Outcome: a saved request collection and a short “what I clicked—what I saw in response and logs” report.
Month 2 — threats and trust. Who can do what in the product, which abuse scenarios matter most, how findings become dev tasks; encryption and signatures explained plainly; social login, tokens, common session mistakes. Outcome: an abuse-scenario table for one service and a report on login and token bug classes.
Month 3 — OWASP-style issues in code. Injections, XSS, request forgery, SSRF, and more—as in the public program, focused on “what it looks like in a real web app”. Outcome: several written findings with repro steps and fix ideas.
Month 4 — API security. REST, GraphQL and SOAP when needed; login and permissions; tokens, OAuth, API keys. Outcome: a report on one chosen API, including cross-user data access and common misconfigurations.
Month 5 — process and automation. How to agree on checks in merge requests, how not to drown in false positives from code scanners and external service scans; Docker basics and AWS or Azure around a web app. Outcome: a draft pipeline rulebook and agreements with development.
Month 6 — containers, secrets, builds, portfolio. Accidentally committed secrets, container images and permissions, dependencies; Playwright in Jenkins or GitHub Actions as a fast post-change check; what to do when a build fails; a few interview-ready stories in English (and Russian if you need). Outcome: a working or agreed CI step and two to three “for the interview” stories.
What we use: OWASP ZAP, JavaScript, Playwright
OWASP ZAP is an open proxy and scanner for manual HTTP work: replay with different parameters, scripts and plugins, compare responses when testing access to other users’ objects. In mentorship, ZAP is the primary tool for intercepting and scenario-based attacks on web traffic.
JavaScript and TypeScript—small scripts against requests and API descriptions, plus Playwright tests so the same authorization mistakes do not return after every code change.
Playwright is convenient because one tool covers browser and raw HTTP, fits what QA automators already use, and plugs into normal CI jobs.
About ten hours a week: how the process works
“About ten hours per week” is total load, not ten consecutive hours of live calls only. It includes calls, self-study, staging tasks, report writing, and code experiments—without that, mentorship does not become a durable skill.
Each week ends with something tangible: report text, a repo test, a scanner rule draft, a roles table. We loop: what can go wrong → how to check → what we found → how we know it is fixed → how we keep it from regressing in the next build.
Individual track
We start from what you already know (JavaScript, builds, Docker) and move it into security tasks without hundreds of “just in case” pages.
Russian and English
If your target is international companies, we tighten resume wording and typical interview answers; we touch ISO-style certificates only where they actually matter for your role.
Why mentorship is more practical than a huge cohort
Cohorts sell hours of recordings. Here the anchor is your product and your tasks: the same six-month depth target, but not an abstract textbook lab—review of what you hit at work.
FAQ: 14 questions
1. Who is this mentorship primarily for?
Primarily testers and anyone already working with web or APIs who wants to grow deliberately into web application security. Developers and admins are welcome; we tune the start to your background.
2. How much time per week should I plan?
About ten hours per week in total: not only live mentor calls, but also self-study, exercises, and packaging results. I refine the estimate after a short conversation about your schedule.
3. Can I combine it with a full-time job?
Yes—the format is for busy people: sessions and assignments are planned not to wreck your work rhythm, but discipline still matters.
4. Do I need strong programming experience?
There is no “senior-only gate”. What matters is willingness to reason step by step and handle data carefully. What you need from code and scripts accumulates during the program—including from zero where the syllabus expects it.
5. I come from another field—is entry realistic?
Realistic if you have motivation and time. At the start we honestly assess your entry point: what you already have and what we add first so months are not wasted.
6. How is mentorship different from a typical course?
On a course you often follow a group pace. Here it is your situation, your tasks, and review of your reports and artifacts—not only “correct answers from a workbook”.
7. How are sessions held—online only?
Yes, fully remote: calls, chat, materials, and homework review. Works from any city and time zone by agreement.
8. Do you help with job search?
I do not promise “guaranteed placement”, but I help package experience: how to talk about projects, what to show an employer, how to prepare for typical conversations.
9. What language do we use—Russian or English?
Primary communication can be English for this international page; if you prefer Russian sessions, say so when you reach out. We add English resume and interview phrases when you target global roles.
10. How long are the program and mentorship?
The baseline route is about six months of steady work. If life changes, early blocks can be discussed separately—no rigid one-size-fits-all.
11. Why are slots limited?
Because mentorship is personal attention per track. Too many parallel learners inevitably reduces review depth and feedback quality.
12. What if I cannot keep up with the pace?
Say so early: we adjust load or priorities. Stable progress beats burning out in a month.
13. How do I sign up and what do I need?
Message on Telegram with a short intro: your role, stack, weekly time budget, and goal (for example, “shift from QA to security checks in CI”). I reply with whether the track fits and suggested next step—no empty promises.
14. Is there a certificate?
Do not optimize for paper. Interviews reward artifacts: reports, tests, pipelines, role policies, reproducible PoCs.
What’s next
If the mix of security learning, hands-on practice, and one-to-one mentorship resonates, message on Telegram—we will discuss your entry, expectations, and the first step without hype.
Important: I keep a limited number of parallel mentees—that is a condition of honest mentorship, not a marketing trick.
A strong next step from here is how QA and SDET engineers can move toward security testing plus a practical Playwright-based security testing example.
