01
STRUCTURE
Unsafe Types and Fragile Logic
Type bypasses, unchecked assumptions and shallow generated fixes can make apparently stable features fail elsewhere.
AI-generated software can look complete, demo well and still conceal serious trust debt. Our structured, read-only forensic audit gives you independent evidence of what can be trusted, what cannot, and what must be fixed before you ship, fund, acquire or hand it over.
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Exposed configuration, leaked credentials and outdated packages can turn a polished application into an immediate commercial liability.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 ForThe Vibe Code Forensic Audit is designed for commercially important software where hidden technical debt, fragile assumptions or security exposure could delay a launch, weaken a deal or damage customer trust.
You built quickly with AI and now need confidence before launch, funding, enterprise demonstrations or customer onboarding.
You are delivering AI-assisted applications for clients and need an independent evidence layer before the project is handed over.
Your engineering workflow includes AI-generated modules, refactors, tests or integrations and hidden trust debt may be accumulating faster than it is being reviewed.
You are reviewing a product where AI-assisted code may conceal technical, security or maintainability risk that is not visible in the demonstration or pitch deck.
Internal teams are using AI to create tools, workflows, dashboards and data applications that may soon handle customer information, permissions or business-critical processes.
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.
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-built application deserves a forensic audit 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 the codebase is suitable for a Vibe Code 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.
Share enough information for us to understand the application, its exposure and the decision you need to make.
Independent evidence of what your AI-built software is hiding.
CODE — AUDIT — SHIP