Web Application Penetration Testing
Manual testing of web applications and the APIs behind them: authentication, authorization and tenancy, business logic, and injection, with every finding verified by hand.
Service 14
Source code review
Some vulnerabilities are far easier to find from the inside: an authorization check missing from one endpoint out of two hundred, a secret committed years ago, a dangerous function reachable through three layers of helpers. A source code review finds them directly.
Overview
The problem we are solving
Two approaches are available, and many engagements combine them. A manual review focuses a consultant's attention on the code that carries the most risk: authentication, authorization, data handling, and the business logic unique to your application. An AI-assisted review uses directed, model-driven analysis to cover the whole codebase quickly (mapping entry points, tracing untrusted input to dangerous sinks, comparing access checks across similar endpoints), with every result triaged by a human and false positives discarded before anything reaches you.
A candidate issue becomes a finding only once it is confirmed. Wherever an environment is available, that means demonstrating it against the running application, which is why this review works best paired with a web application or API test. Code-only findings that cannot be demonstrated are clearly labeled as such, with the reasoning shown.
For AI-assisted work, your code is handled with the same isolation as our AI-assisted testing: an isolated Amazon Bedrock deployment in a dedicated AWS environment, or a private model on in-house bare metal when code cannot leave controlled infrastructure. It is never sent to a shared consumer product or used to train anything.
Coverage
What the review looks for
Scope is agreed up front; these are the areas an engagement typically covers.
- Authentication & authorization logic
- Missing or inconsistent access checks, tenant isolation gaps, and endpoints that skip the checks their neighbors perform.
- Injection & unsafe input handling
- Untrusted input traced to SQL, command, template, and deserialization sinks, along with the validation that should have stopped it.
- Secrets & sensitive data
- Hard-coded credentials, keys, and tokens, sensitive data in logs, and weak handling of cryptographic material.
- Insecure configuration & dependencies
- Framework security settings, debug features left enabled, and vulnerable dependencies that are actually reachable from your code.
- Business logic
- State and workflow assumptions, race conditions, and trust placed in client-supplied values, highlighted for confirmation against the running application.
Methodology
How the engagement runs
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01
Scoping & secure handoff
Agree repositories, languages, priority areas, the mix of manual and AI-assisted review, and how your code will be handled, then receive it through a secure channel.
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02
Manual & AI-assisted analysis
Manual review of the highest-risk code paths, and directed model-driven analysis across the wider codebase, in whatever mix was agreed during scoping.
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03
Human triage
Every candidate issue is reviewed by a person. False positives are discarded, and duplicates and variants are grouped by root cause.
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04
Confirmation
Remaining issues are confirmed against the running application wherever an environment is available. Anything that cannot be demonstrated is labeled as code-only, with the reasoning shown.
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05
Reporting & debrief
Findings with file and line references, a clear explanation of the flaw, and specific fix guidance, walked through live with your developers.
Deliverables
What you receive
- Findings with file and line references and an explanation of each flaw
- Clear labeling of which findings were confirmed against the running application
- Remediation guidance written for your language and framework
- Validation steps for every finding, so your team can confirm and close out each fix on its own
- Live debrief with your developers
Aligned to
- OWASP Application Security Verification Standard
- OWASP Top 10
- CWE Top 25
Questions
Frequently asked
Manual or AI-assisted: which should we choose?
Manual review gives the deepest look at a focused area, such as authentication or a payment flow. AI-assisted review gives broad coverage of a large codebase quickly. For most applications, a combination works best: AI-assisted coverage across the codebase, with manual depth on the highest-risk code. We will recommend a mix during scoping.
How is this better than running a static analysis tool?
Static analysis tools match patterns and produce long lists, many of them false positives. Manual and AI-assisted review can follow logic across files and reason about authorization and business rules, and human triage means you only receive issues that were actually checked.
Where does our code go?
Code is received through a secure channel and kept only for the engagement. For AI-assisted analysis, it goes to an isolated Amazon Bedrock deployment in our dedicated AWS environment, or to a private model on in-house bare metal if your code cannot leave controlled infrastructure. It is never sent to a shared consumer AI product or used for training.
Should we pair this with a penetration test?
Usually, yes. Code review finds issues a black-box test can miss, and a live test confirms which code issues are actually exploitable. Together they give the most complete picture.
Related
Often scoped together
AI-Assisted Penetration Testing
Agentic AI accelerates reconnaissance, enumeration, and evidence correlation across an engagement, run through isolated infrastructure: Amazon Bedrock in a dedicated environment, or a private model on in-house bare metal when nothing can leave the building.
Get started
Ready to scope source code review?
Scoping conversations are free and usually take twenty minutes. You will speak directly with the consultant who would do the work.
Request an assessment
or reach out directly, (608) 520-0719 sales@infocus-security.com