Builds a global view of routes, trust boundaries, sensitive flows, and likely attack paths.
Local AI-powered code review
Autonomous code review that keeps your source private.
Stormhold reviews repositories, pull requests, and high-risk code paths with local AI agents, deterministic validation, and human security judgment. The goal is proof over probability: fewer noisy guesses, more findings your developers can reproduce and fix.
Review focus
Auth Logic Access Control Data Flow Injection Paths Secrets Code Privacy
Local review architecture
Creative discovery. Deterministic validation. Private code.
Local-only
Short-lived local agents review narrow objectives without sending code to public model pipelines.
Findings are checked against reachability, impact, exploitability, and developer fix paths.
Tenant boundary bypassValidate ownership checks
Unsafe object accessTrace request path
Secret or token exposureConfirm reachable leak
Dependency riskPrioritize exploitability
Proof over probability
Designed for teams that need more than checklist scanning.
Traditional scanners and single-pass AI reviews tend to produce volume: suspicious snippets, generic warnings, and findings developers still have to prove. Stormhold uses a local review foundry model that separates exploration from validation so review output is tied to real application behavior.
01
Define scope and context
Set repository boundaries, branches, languages, frameworks, sensitive flows, test accounts, and what should never leave the review environment.
02
Deploy focused local agents
Use local AI workers to inspect specific objectives such as auth logic, object ownership, data flow, injection paths, secrets, and dependencies.
03
Validate before reporting
Promote only findings that can be explained, traced, reproduced, or paired with application behavior for developer-ready remediation.
Stormhold Review Foundry
A repeatable local pipeline for security-critical code.
- Ingest only approved repositories, branches, pull requests, and architecture context
- Break review work into focused agent objectives instead of one long-running prompt
- Correlate source findings with routes, roles, permissions, and data flows
- Use deterministic checks and human review to decide what becomes a finding
- Preserve source-code privacy by keeping analysis local or private
- Produce remediation output designed for developers, not just security teams
What gets reviewed
Security review for the code paths attackers actually care about.
The service is strongest when paired with architecture notes, threat concerns, deployed URLs, recent pull requests, and the workflows your business cannot afford to expose.
Auth and Access Control
Review role checks, object ownership, tenant isolation, password reset, invitations, admin workflows, and privilege boundaries.
Injection and Unsafe Inputs
Trace input handling into SQL, NoSQL, shell calls, templates, file paths, deserialization, SSRF, and outbound integrations.
Secrets and Dependencies
Review exposed credentials, package risk, build configuration, environment assumptions, and sensitive logging paths.
Agent and Tool Boundaries
Review AI tool permissions, retrieval access, prompt-injection exposure, untrusted content handling, and auditability.
Workflow Abuse
Look for payment, approval, dispatch, case, patient, client, or admin workflow flaws that static rules often miss.
Reachability and Impact
Separate theoretical warnings from reachable weaknesses that matter in the actual application design.
Deliverables
Developer-ready findings with evidence, impact, and fix direction.
- Risk-ranked findings mapped to files, functions, routes, and workflows
- Evidence and reproduction notes where behavior can be confirmed
- Safer implementation patterns and remediation guidance
- Executive summary for stakeholders who need business impact
- Retest support after fixes are applied
- Optional pairing with live web app and API pentesting
Source-code privacy by design
Local AI analysis for proprietary codebases.
The review model is designed around local or private processing so source code, application context, intellectual property, and sensitive client materials are not routed through public model pipelines.
Code review request
Bring a repository, pull request, or high-risk feature.
Stormhold can review the code locally, validate the risk, and help your developers fix the issues that matter before attackers find them in production.
Questions buyers ask
Service-specific FAQ.
Short answers for scoping, privacy, authorization, deliverables, and production safety.
Does Stormhold send source code to public AI models?
No. The AI code review service is positioned around local or private processing so source code, application context, and intellectual property are not routed through public model pipelines.
What access does Stormhold need for code review?
Stormhold scopes access before work begins. Depending on the engagement, review may use a repository export, a specific branch, selected pull requests, architecture notes, or a local review environment.
Is this a replacement for human security review?
No. Local AI improves coverage and reasoning, but findings are validated by human security judgment before they are reported.
What do developers receive?
Developers receive prioritized findings mapped to files, functions, routes, workflows, impact, evidence, and remediation guidance.
Local code review intake
Share the repo, branch, pull request, or risky workflow.
This form starts scoping only. Stormhold confirms authorization, local processing expectations, and access boundaries before review begins.