Secure Source Code Review
White-box analysis that reaches the code paths testing can't
Black-box testing only reaches what's reachable. A code review sees every path, including the error branch that skips the authorization check and the admin function nothing links to. We combine automated semantic analysis and taint tracing across the codebase with manual review of the modules that matter most — authentication, session handling, payment — then prove exploitability against a test instance rather than handing you a list of theoretical concerns.
What we cover.
- .NET, Java, PHP, Python, and Node.js codebases
- Injection, XSS, deserialization, access control, SSRF, path traversal, and cryptographic flaws
- Taint and data-flow tracing across the full codebase
- Secret detection and dependency scanning
- Deep manual review of authentication, session management, and payment handling
- Aligned to OWASP Top 10 and ASVS, CWE Top 25, and SANS secure coding guidance
How the engagement runs.
Introduction
A secure source code review is a white-box assessment in which we examine the application's source directly to find vulnerabilities, insecure design patterns, and logic flaws. Rather than attacking a running system from outside, we read the software's DNA — which gives complete visibility into every code path, including those that are difficult or impossible to reach through black-box testing.
The objective is to eliminate vulnerabilities at the source, as early in the lifecycle as possible. Fixing a flaw in code is dramatically cheaper and safer than remediating it in production after a breach. You get a prioritized, developer-ready set of findings mapped to exact file and line locations.
Why we lead with automated analysis
Modern codebases are too large and change too fast for line-by-line human review to scale. We lead with automated semantic analysis across the entire codebase, then apply senior human expertise where it actually matters — validating exploitability, understanding business context, and eliminating false positives.
Analysis reads every file, every time. That removes sampling bias, where only the files somebody already considered high-risk get read, and it keeps pace with continuous delivery. The result is broader coverage, faster turnaround, and very little noise.
OWASP Top 10 and ASVS
Application Security Verification Standard levels give the review measurable coverage and rigour.
CWE Top 25 and SANS
The most dangerous software weaknesses, plus established secure coding guidance.
Language-specific practice
Framework-aware review for .NET, Java, PHP, Python, and Node.js — the idioms differ, and so do the traps.
Review phases
Six phases. Automation carries the breadth of detection in phase 2; our specialists carry the depth of validation from phase 3 onward — a deliberate human-in-the-loop design.
Planning and scoping
- Objectives — for example achieving ASVS Level 2, auditing the authentication and payment modules, or acting as a pre-release security gate for a new feature.
- Identify the most sensitive components: authentication, authorization, cryptography, payment, and data handling.
- Scope — repositories, branches, and services in scope, the languages and frameworks in use, and whether this is a full-codebase review or targeted at specific modules or a single pull request.
- Access — read access to repositories, build configuration, and dependency manifests; where useful, design documents and a runnable build so findings can be confirmed dynamically.
- Rules of engagement — confidentiality terms for the source itself, which is among your most sensitive assets, and written agreement on where analysis happens: inside approved isolated environments, never retained, never used for model training.
Automated code analysis
The engine ingests and reasons over the entire codebase before any manual review begins.
- Ingestion and mapping — the codebase is parsed into abstract syntax trees and code property graphs so structure, control flow, and cross-file relationships are understood. Entry points, trust boundaries, untrusted input sources, and sensitive sinks are mapped automatically.
- Static analysis — detecting injection (SQL, command, LDAP), cross-site scripting, insecure deserialization, broken access control, server-side request forgery, path traversal, and weak or misused cryptography. This is semantic analysis, reasoning about what the code intends, not signature matching — so it catches context-dependent flaws rather than only known patterns.
- Taint and data-flow analysis — tracing tainted data from source to sink across function, class, and file boundaries, and flagging missing or ineffective sanitisation along each path.
- Secret and dependency analysis — hardcoded API keys, credentials, tokens, and private keys detected across the code and its history; third-party libraries checked against known vulnerabilities and risky licences.
- Triage and prioritisation — findings correlated and de-duplicated, then ranked by likely exploitability and business impact rather than theoretical severity. Low-confidence and unreachable findings are filtered out, leaving a high-signal queue for expert review.
Expert manual review and validation
Automation provides breadth; our specialists provide judgement. Every finding that reaches the report has been reviewed by a person — that step is what separates a credible review from a raw scanner dump.
- Validating findings — confirming each issue is real and reachable, discarding false positives, and inspecting surrounding code and configuration where context is needed to establish true impact.
- Business logic and design review — the flaws tools cannot infer: broken workflows, insecure state transitions, authorization gaps, and abuse of functionality that works exactly as written. Plus architectural weaknesses such as insecure defaults and trust-boundary errors.
- Deep-dive audit — focused line-by-line review of the critical modules identified in scoping, hunting for subtle backdoors and chained weaknesses that only appear when a human connects several small issues.
Exploitation and proof of concept
- Confirming exploitability — where a runnable build exists, validated findings are demonstrated against a test instance so there is no argument about whether they matter.
- Proof-of-concept development — for significant findings, a safe reproduction case with captured evidence, so developers have something unambiguous to work from.
Reporting
- Risk assessment — every validated finding scored with CVSS v4.0 and assigned Critical, High, Medium, Low, or Informative.
- Executive summary of business risk, plus a detailed technical section for engineers.
- Per finding: exact file and line references, a clear explanation of root cause and impact, and specific remediation including corrected code where appropriate.
- Findings tracked through Open, Closed, Partially Remediated, or Risk Accepted.
Remediation and re-review
- We support your engineers through the fixes and answer questions on the recommended approach.
- Once applied, we re-review the affected code to verify the vulnerability is genuinely resolved and no regression was introduced.
- For teams shipping continuously, the analysis integrates into CI/CD to catch new issues on every pull request — turning a point-in-time review into continuous assurance.
What you get at the end.
Who it's for
Engineering teams shifting security left, and organizations pursuing a specific assurance level such as ASVS Level 2.
Need a scope for this engagement?
Tell us what's in your environment and we'll come back with a scoped plan.