02 / DEVELOPERS & DEVSECOPS TEAMS

Security review. Inside your pull request.

Give your coding agent and pipeline a mobile security specialist. Scan source before a build, connect vulnerabilities across files, and turn the finding into a patch your team can review.

Start freeExplore the workflow
DJINI AI SAST + YOUR CODING AGENT
  1. 01
    CI/CD / SOURCECommit submitted for security review
  2. 02
    CROSS-FILE ANALYSISDeepLink.kt → WebView.kt → Bridge.java
  3. 03
    FINDINGSource location + impact + remediation
  4. 04
    CODING AGENTProposed patch → review PR → rescan

AI SAST & FIX WORKFLOWSSource reviewCI/CD gatesFix PR workflow

You ask Djini

“Scan this mobile repository before we merge. Explain the connected attack paths, propose a fix, and open a pull request for review.”

What your team gets

Source findings with file and line context, remediation, and structured results for your coding agent and delivery pipeline.

Access & scope

Available today. AI SAST uses your model key; the coding agent prepares the patch and PR.

THE WORK BEHIND THE OUTCOME

The finding should lead straight into the work.

A scan is useful when a developer can understand the risk and act on it in the same development cycle. Djini reads the source, follows interactions across files, and returns the context a coding agent or reviewer needs to prepare a fix.

  1. 01

    Scan where you build

    Submit a source ZIP or public Git repository. Invoke the source scan from your coding agent or CI/CD workflow, using your chosen model provider.

  2. 02

    Trace more than one file

    Review mobile security categories and connected attack paths. Follow untrusted input into the functions and privileges that make a bug matter.

  3. 03

    Bring findings into the repo

    Use JSON or SARIF results in your workflow. Set a severity policy for the pipeline and give developers the affected location and remediation context.

  4. 04

    Prepare and verify the patch

    Let your coding agent propose a focused fix and pull request. Review the diff, rescan the changed code, and use dynamic testing when runtime proof is needed.

INSIDE DJINI

The exact code. The reason to fix it.

The source findings view keeps the affected code beside the analysis. Use that context to review an attack chain and prepare the developer handoff.

Djini AI SAST findings for the InsecureShop training app, ranked by severity with their CWE mapping

WHAT YOUR TEAM TAKES AWAY

Review before a build

Find source-level risks while the code is still in development.

A finding a developer can use

Keep the location, reasoning, and remediation together.

A controlled fix workflow

Propose changes through your coding agent, with code review and merge decisions staying in your repository.

A few good questions

Before you go deeper.

What is included for free?

Each account gets 10 AI SAST source scans per month. Configure your own model key for source analysis. Model inference is billed by the provider you choose.

Can we use OpenRouter or a local LLM?

Yes. Source scans use the OpenAI-compatible provider setting. Configure a supported endpoint such as OpenRouter or your own reachable local model endpoint, then select the model.

Does a source finding prove exploitation?

A source scan identifies the code path and explains the risk. Use Djini’s runtime workflow to reproduce the behavior when you need exploitation evidence. A proposed fix still needs review and retesting.

PUT DJINI TO WORK

Give your next PR a security review.

Book a demo Start free

10 free AI SAST scans per month. Bring your own model key.

THIS WORKFLOW, IN YOUR INDUSTRY