01
STRUCTURE
Unsafe Types and Fragile Logic
Type bypasses, unchecked assumptions and shallow generated fixes can make apparently stable features fail elsewhere.
The Vibe Code Forensic Audit: an independent examination that answers one narrow question. Not “is this good code?”, but “is there objective evidence this software does what it is said to do?”
AI-generated software can pass a quick smoke test while serious risk remains hidden beneath the interface. The question is not only whether the application runs. It is whether its assumptions, boundaries and failure paths can withstand real use.
AI makes code creation cheap. It does not make software trust cheap.
Type bypasses, unchecked assumptions and shallow generated fixes can make apparently stable features fail elsewhere.
Unreachable functions, abandoned components and repeated logic create hidden drift and unnecessary maintenance risk.
Exposed configuration, leaked credentials and outdated packages can turn a polished application into an immediate commercial liability.
Fragile authentication state, unsafe client/server boundaries and unverified API responses may expose data or privileges.
The demo may show the ideal journey while errors, rejected inputs, interrupted workflows and edge cases remain untested.
Some findings require human judgement. Honest assurance declares coverage limits instead of presenting scanner noise as certainty.
The audit does not begin with a scanner. It begins with permission, a defined scope and a frozen baseline. Every stage records what was examined, what was excluded, what was found and what still requires human judgement.
We establish whether the codebase is a good fit, how AI was used, what risks matter, and whether the audit is supporting launch, funding, acquisition, handover or production approval.
The codebase is mapped before findings are counted. We record the repositories, file types, detected stack, excluded folders and the snapshot or hash that defines the audit baseline.
Diagnostic tools are used within the authorised scope, but tool output is not treated as proof by itself. Findings are classified, located and reviewed wherever judgement is required.
The final report explains what can be trusted, what cannot yet be trusted and what should be fixed first. After remediation, an optional retest compares the new result against the original baseline.
We do not write to your repository, expose secret values or run live probes against your application unless they are separately authorised in writing.
See Who the Audit Is ForYou commissioned it. You have seen it work. The invoice for the balance is in your inbox, and once you pay it, the software is yours.
It is about to carry real customers, real money and real data, and the demonstration was not run under those conditions.
You have inherited a codebase, acquired a product, or the person who built it is leaving.
You built it quickly and you need to know what you are asking other people to trust. Keep the speed. Add the evidence.
The audit is designed for both technical and business readers. Engineering teams receive located findings and remediation detail. Founders, executives and investors receive enough clarity to make a defensible decision.
A recorded snapshot of what was examined, including files, languages, repositories, stack, exclusions and baseline reference.
A structured record of located issues grouped by risk class, programme type and the part of the codebase affected.
Findings are prioritised according to likely impact, evidence strength and whether additional human review is required.
The report explains what the evidence can prove, what it cannot prove and where scanner output requires judgement.
A clear explanation of what can be trusted, what cannot yet be trusted and what decision the current evidence supports.
The most important findings are arranged into a practical order so the team knows what should be addressed first.
After remediation, the updated codebase can be compared against the original baseline to show what changed.
A report that helps technical teams act and gives decision-makers a clearer basis for launch, funding, acquisition or handover.
WHAT MAKES THIS DIFFERENT
The individual tools used in software assurance are not the whole product. The difference is how evidence is gathered, bounded, interpreted and turned into a decision your technical and commercial teams can defend.
Examines the failure patterns common in AI-assisted and vibe-coded applications, including fragile happy paths, unsafe type bypasses, duplicated logic, dead code and missing product obligations.
The authorised scope and baseline are recorded before examination. The audit is read-only by default, so the evidence relates to a defined body of software rather than a moving or altered target.
Coverage gaps, exclusions, possible false positives and areas requiring human judgement are declared. An untested or unsupported claim is recorded as unverified, not quietly converted into a pass.
Engineers receive located findings, evidence and remediation priorities. Founders, executives and investors receive a clear, bounded explanation of what can be trusted, what cannot yet be trusted and what decision the evidence supports.
AI-specific risk framing, a controlled chain of evidence, explicit uncertainty and decision-ready reporting—assembled into one defensible evidence record.
Most scanners try to look certain. We do not. If coverage is partial, we say so. If a tool cannot prove a class of issue, we say so. If a finding requires human judgement, we mark it accordingly.
We do not write to your repository or alter the codebase being examined.
Tool output is reviewed rather than automatically presented as final truth.
The report explains what was examined, excluded and still remains uncertain.
Secret values are redacted and live probing requires separate written authorisation.
Every audit begins with a private fit check, an agreed scope and explicit permission. Access, exclusions, live testing and deliverables are defined before any private code is examined.
The audit is intended for software materially generated, modified or assembled with AI-assisted development tools. That may include SaaS products, client applications, internal tools, dashboards, workflow systems and data applications.
The private fit check confirms whether the application, stack, risk profile and decision you need to make are suitable for the audit.
Access can be provided through temporary read-only GitHub or GitLab access, a read-only deploy key, a dated code snapshot or a client-run evidence bundle where the code remains inside your own environment.
The access method, included repositories and exclusions are agreed before examination begins.
No. The forensic audit is read-only by default. We do not write to your repository, alter the codebase or apply fixes during the evidence examination.
This preserves the integrity of the baseline used for the audit and any later retest comparison.
No. The core service is a structured evidence audit of the authorised codebase. It examines technical risk, code structure, dependencies, boundaries, failure paths and coverage limitations.
Runtime or behavioural testing may be included only when separately scoped and authorised. The audit should not be presented as a full penetration test unless that work has been explicitly commissioned.
Live probes are not performed as part of the default read-only code audit. Any runtime testing of authentication, access control, data integrity or role boundaries requires separate written authorisation.
The authorised scope must define the environment, permitted actions and testing boundaries before live behaviour is examined.
An optional assurance retest can compare the updated codebase with the original baseline. It shows which findings were fixed, which remain open and whether new obvious issues were introduced.
A retest is not treated as a completely new audit unless the scope, architecture or codebase has changed substantially.
Every AI-assisted application deserves independent examination before anyone is asked to ship it, fund it, buy it, hand it over or depend on it.
Tell us what the application does and what decision the evidence needs to support. We will confirm whether it is suitable for a Trust Before Ship forensic audit.
Your findings are not published without permission.
We do not write to or alter your repository.
No live testing occurs without written authorisation.
Clear answers about audit scope, code access, evidence, limitations and what happens after issues are found.
An AI-built software audit is an independent review of software materially generated, modified or assembled using AI coding tools.
Trust Before Ship examines the agreed codebase and records what the available evidence supports, what remains uncertain and what should be addressed next.
AI-generated software can appear to work while still containing unsafe assumptions, fragile logic, duplicated code, vulnerable dependencies, weak access boundaries or untested failure paths.
An independent audit provides bounded evidence before the software is launched, funded, handed over, acquired or placed into production.
Depending on the agreed scope, the audit may examine code structure, type safety, duplication, dead code, dependency health, secret exposure, authentication assumptions, access boundaries, failure paths and evidence coverage.
Automated results are reviewed in context. A tool result is evidence; it is not the whole audit.
The audit is intended for software materially generated, modified or assembled using AI-assisted development tools.
This can include SaaS products, customer portals, internal systems, workflow applications, dashboards, marketplaces and data-driven tools. A private fit check confirms whether the application and decision point are suitable.
Access may be provided through temporary read-only GitHub or GitLab access, a read-only deploy key, a dated code snapshot or a client-run evidence bundle where the code remains within the client’s environment.
The repositories, permissions, exclusions and access period are agreed before the examination begins.
No. The forensic audit is read-only by default. Trust Before Ship does not write to the repository, alter the codebase or apply fixes during the evidence examination.
This protects the integrity of the recorded baseline used for the audit and any later comparison or retest.
No. The core service is a structured evidence audit of the authorised codebase. It examines technical risk, code structure, dependencies, business obligations, access assumptions, failure paths and coverage limits.
Runtime or behavioural testing is performed only when it is separately scoped and authorised in writing.
You receive an evidence report containing the agreed scope, recorded baseline, findings, severity and confidence notes, coverage boundaries, remediation priorities and an executive summary.
The report explains what the current evidence supports, what cannot yet be trusted and what remains unverified.
An optional retest can compare the updated codebase against the original baseline. It records which findings were addressed, which remain open and whether obvious new issues were introduced.
A substantially changed architecture, scope or codebase may require a new audit rather than a limited retest.
Trust Before Ship reports what the agreed evidence supports. It does not provide regulatory certification, legal advice or a guarantee that software is secure, complete or defect-free.
Tell us what was built, how AI was used and which decision you need the evidence to support.