Code Hunter Personal

Agentic AI SAST for project-aware code security audit.

Personal packages AI as a security audit agent: activate the license, configure a trusted model provider, import a local project, run deep review, promote validated findings, and export auditor-grade evidence from one operator workflow.

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Agentic audit workflowLogic and control gaps60-70% review-noise targetReport and fix output

Code Hunter Personal project overview — **Project profile first**Framework, entry points, trust boundaries, permissions, data surfaces, and integrations anchor the audit.

Code Hunter Personal findings center — **Reviewed findings only**Severity, confidence, evidence, rejected states, and remediation direction stay connected.

Personal agent loop

From product activation to evidence-backed report in one AI security agent.

The Personal workflow keeps setup, model assurance, project understanding, vulnerability review, and final deliverables connected. The videos below show the real desktop flow from first activation to completed findings and report output.

01Activate

License binding and access validation establish the operator identity before audit work begins.

02Configure

Model providers, endpoints, keys, and assurance mode are selected before the agent runs analysis.

03Run

The local codebase is imported, profiled, and reviewed through project-aware audit depth.

04Deliver

Validated findings become evidence-backed reports and scoped remediation output.

First-run activation and audit configuration.

The agent moves from license activation to model-provider setup, connection validation, local project import, audit depth selection, and first run. This is the operational entry point for a Personal audit.

Completed findings, report preview, and output assets.

After the audit completes, the workspace shows confirmed risk, review state, selected finding detail, report generation progress, exported report assets, and fix-queue actions.

Personal capabilities

Built for security-auditor-grade work, not raw alert volume.

Project-aware audit

The audit starts with project profiling and function inventory so findings are judged against product behavior, not isolated snippets.

Logic and missing-control risks

AI review looks for absent authorization, missing validation, broken business rules, tenant-boundary gaps, and other control failures.

False-positive workload reduction

Weak hypotheses are challenged through reachability, affected behavior, control evidence, confidence, and human accept or reject decisions.

Audit deliverables

Reviewed findings become professional reports and scoped fix packages with assumptions, rollback notes, tests, and remediation direction.

Personal workflow

Context, proof, decision, and deliverable in one operator loop.

Function context — 01Function-level contextMap product behavior, call chains, sensitive flows, trust boundaries, and downstream sinks before judging risk.

Proof evidence — 02Source-to-sink proofEach promoted finding carries source, transit, sink, failed or missing control, product impact, and remediation direction.

Report preview — 03Security-auditor-grade reportOnly reviewed findings move into the report, with rejected and unreviewed material kept out of customer-facing output.

Scoped repair package — 04Scoped fix packagesFix generation is scoped to accepted findings so unrelated changes stay out of the repair package.

Personal buyers

For independent developers, consultants, and security owners.

Individual code auditRun professional review on a local project before release, delivery, investment diligence, or customer security review.

Defensible findingsAcceptance, rejection, downgrade, source-transit-sink evidence, control failure, and remediation direction remain explicit.

Personal turns one operator into an AI security audit agent.

Choose monthly or yearly billing on the plan page. The same Personal license connects checkout, activation, installer download, and audit workflow.

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