Code Hunter Windows 3.1.85: AI code audit needs evidence, not just alerts

Security teams do not need another scanner that stops at a long queue of alerts. They need a way to decide which risks are supported by code evidence, which ones still need validation, and which findings are ready for an engineering owner. Code Hunter 3.1.85 is published as signed Windows x64 Personal and Team installers for that workflow.

The release matters because it puts the Code Hunter audit loop into a desktop installer instead of leaving it as an abstract demo. A reviewer can start from a local project, run AI-assisted code audit, inspect evidence, and turn confirmed risk into a report or a Team governance action.

What the Windows installer is for

The Windows 3.1.85 installer is aimed at teams that need AI code audit on real source projects, especially where traditional SAST output is not enough. Code Hunter is built around project understanding, multi-model review, missing-control discovery, and auditor-grade reporting.

The practical target is not only syntax-level bugs. Code Hunter is designed for logic flaws, authorization gaps, validation gaps, tenant-boundary failures, unsafe business workflows, and release decisions that need reviewable security evidence.

There are two public Windows x64 installer paths in the current download manifest: Personal for an individual audit workstation and Team for a governance workspace. Both Windows entries are listed as version 3.1.85 signed EXE installers with SHA-256 metadata.

The audit shape: source, transit, sink, control failure

A useful AI code audit report should tell a reviewer why a finding deserves attention. Code Hunter keeps the key evidence together: source, transit, sink, failed or missing control, impact, confidence, and remediation direction.

That structure is important for AppSec teams because it separates a raw suspicion from a reviewable finding. A weak alert might say that input reaches a sensitive API. An auditor-grade finding should explain where the input enters, how it moves, where it lands, what control should have stopped it, and why the evidence is enough to act.

This is also where multi-model review becomes useful. Different models can behave differently: one may discover a broader candidate set, while another may apply a stricter confirmation threshold. Code Hunter treats that variation as part of the audit posture rather than hiding it.

Why ASPM teams care about evidence

ASPM work breaks down when evidence gets separated from ownership. A finding may appear in one system, a remediation task in another, a release decision in a third, and the original proof disappears into a report attachment. Team workflows need the evidence chain to stay attached to the governance record.

Code Hunter Team is positioned around baselines, iterations, owner tasks, SCA and CI evidence, accepted-risk notes, and release policy gates. That means confirmed code risk can become accountable work instead of a static PDF that nobody can trace back to a source path.

For buyers and operators, the distinction is simple: a dashboard is not enough. The report and governance trail must show what was reviewed, what was confirmed, what remains likely, what still needs evidence, and what was used to make the release decision.

What this release does not claim

The Windows installer does not turn every AI observation into a proven vulnerability. Runtime conditions, deployment controls, exploitability, and business policy still matter. Code Hunter is most useful when the reviewer uses confirmation tiers and evidence chains to decide what is ready for remediation and what needs follow-up validation.

The public download manifest also keeps platform status explicit. Windows x64 is published as version 3.1.85. Existing macOS packages remain available at their listed 3.1.74 manifest versions until the next macOS release. That distinction is intentional and should not be flattened in outreach copy.

Where to start

Start with the Code Hunter overview if you want the product framing, then use the download page for the current installer links. For technical proof, review the published sample reports and model-comparison research posts. They show the difference between broad discovery, strict confirmation, and evidence-led reporting.

Links:

Code Hunter overview: https://www.arvantacyber.com/code-hunter/

Windows installer download page: https://www.arvantacyber.com/code-hunter/download/

Sample report: https://github.com/SEc-123/codehunter-docs/blob/main/docs/examples/codehunter-3.1.75-personal-audit-report.md

Disclosure: Code Hunter is an Arvanta Cyber product.

Code Hunter Windows 3.1.85: AI Code Audit Needs Evidence, Not Just Alerts