Security Audit
Testing your website, app, and AI integrations from an attacker's point of view using the OWASP methodology. A report with priorities and a remediation plan — no scare tactics, no fluff.
What's included
We test your website, app, and AI integrations for vulnerabilities from an attacker's point of view and deliver a report with priorities and a remediation plan. The work includes perimeter reconnaissance — what's visible from the outside with anonymous access — testing against the OWASP methodology, checking permissions and access controls, hunting for leaked keys and secrets in client-side code, and analyzing security headers and configuration. We look specifically at common holes: authorization bypass, access to other users' data via direct links, injections, and insecure storage and API settings. For projects with neural networks, we test the specific risks of AI integrations — leaks through prompts and the model's access to data. What you get is not a list of abstract scanner warnings, but analyzed findings rated by severity with concrete remediation steps.
How the audit really works
The audit runs in two layers — automated and manual. First, scanners sweep the perimeter and build a map: open entry points, component versions, headers, public files and bundles. Then the manual work begins, because a scanner often flags real vulnerabilities as uncertain or misses them entirely: we check whether authorization can be bypassed, whether another user's data can be obtained by swapping an identifier, whether secrets can be pulled from JavaScript, whether the API can be abused. Every finding is checked for reproducibility — whether it can actually be exploited or is a false positive — because a report of a hundred theoretical items is useless. Findings are ranked by severity and likelihood so you fix what's genuinely dangerous first. The audit looks at the system the way an attacker would, not the way its developer intended.
Where security auditing came from
The systematization of vulnerabilities began with the CVE project, which the MITRE corporation launched in September 1999, introducing a single identifier for every known flaw and a common language for the entire industry. Application security for the web was shaped by the OWASP project: it was founded by Mark Curphey on September 9, 2001 as an open community that makes application risks visible. In 2003, OWASP released the first edition of the OWASP Top 10 — a list of the ten most critical categories of web application vulnerabilities, which became the industry standard for testing and is updated as threats evolve. These two pillars — the CVE catalog and the OWASP methodology — set the language and framework that modern auditing rests on. We rely on the current edition of the OWASP Top 10, not on outdated checklists from a decade ago.
Why testing accuracy is critical
In a security audit, both extremes are equally harmful. A missed real vulnerability leaves the door open to a data breach or hack whose cost is incomparable to the price of testing. But a flood of false positives is harmful too: if a report consists of a hundred theoretical items, the team drowns in noise and never fixes what matters. That's why the value of an audit lies not in the length of the list, but in verified, reproducible findings with an honest severity rating. A separate subtlety is access-control testing: many critical flaws are invisible to a scanner and only surface through manual testing of authorization logic. We take every significant finding through to confirmed exploitation and explain how to close it, rather than scaring the client with colorful charts that say nothing.
What stack and methodology we work with
The baseline framework is the OWASP methodology and its Top 10 in the current edition, supplemented with checks for APIs and AI integrations. We run reconnaissance and scanning with a toolkit for analyzing the perimeter, headers, component versions, and client-side bundles, while the key part — testing authorization, access to other users' data, and logic bypass — is done manually. We separately test what an attacker sees with anonymous access: which API endpoints are open, what data leaks without authentication, whether there are secrets in published JavaScript. For projects on cloud databases, we check row-level access rules and permission separation. We select tools to fit your stack, and we deliver conclusions in plain language with priorities, not as a raw scanner dump.
When the key standards appeared
The reference standards for auditing took shape at the turn of the century. The CVE vulnerability catalog was launched by the MITRE corporation in September 1999 and became the industry's common language. The OWASP community was founded on September 9, 2001. The first edition of the OWASP Top 10 came out in 2003 and has been updated regularly ever since as the threat landscape shifts — from classic injections to problems of access control and insecure configuration. Separate methodologies for mobile applications and, later, for the risks of large language models built out this framework to cover new classes of systems. We work to the current editions of these standards, because auditing against a decade-old checklist misses entire classes of modern attacks.
Why you can trust us with this
Our team's combined IT experience exceeds 45 years, and we run audits on our own practice: we test our own projects from the outside — database access permissions, API protection, behavior under anonymous access — with the same attacker's eye. We approach the task like engineers: we take findings through to reproduction, rank them by real danger, and explain how to close them, rather than handing over a raw scanner report. We're honest about the boundaries of the work: an audit shows the state at the moment of testing, and we say plainly what needs re-testing after fixes. We don't sell fear or pad the list for volume — the priority is what is genuinely dangerous to your business. As a result, you get a clear picture of the risks and a concrete plan to close them, not a reason to panic.
What's included
How we work
A clear picture of the risks and a concrete plan to close them. Verified findings, not a raw scanner list.
FAQ
How is this different from a scanner?+
We take every finding through to reproduction and rank it by real danger, rather than handing over a raw dump.
Do you test AI integrations?+
Yes — the specific risks of neural networks: leaks through prompts and the model's access to data.
What happens after the audit?+
We deliver a remediation plan and help with the fixes; after the changes, re-testing is needed.