Turn a concern in the code into a verifiable finding
Connect your project and define the audit scope: code, dependencies, configuration or a particular data flow. Ask for confirmed issues to be separated from hypotheses.
Example: access to another customer’s order
An orders API supports several roles. Check that a user cannot retrieve another customer’s data.
- Trace authorization from the route to the data query.
- Confirm the finding with a reproducible example in a test environment.
- Prepare a fix and repeat the check across roles.
What you get
A report with code evidence, reproduction conditions and a proposed fix.
What to prepare
Provide code, permitted configuration and context about architecture and access models. Exclude secrets that the task does not need. Use Desktop to inspect a local project.
What to expect
Findings tied to available code, an explanation of risk, priority and a way to verify each issue. Ask which areas were not reviewed and what needs further investigation.
How to validate
Reproduce important findings in an authorized environment, prepare changes and repeat the checks. An AI audit helps find problems; it does not prove the absence of all vulnerabilities.
Security
Our team observed an audit of a project with roughly 2500 files using about 130 glyphs. Usage depends on context, mode and actions. Project size does not imply every file was reviewed; fixes and repeat checks are separate tasks.
Start with your team's project
Download Desktop, connect your project and start with a task whose result you can verify.