BadgerBeta

Stop shippingAI-built apps blind.

AI builders can turn an idea into a working app in hours, but the security review still lands on you. Badger turns a GitHub repo into an evidence-based AppSec report so you can spot exposed secrets, missing auth, risky AI endpoints, unsafe tool calls and supply-chain issues before customers or maintainers do.

No install. Paste a public repo without GitHub login.Server-side. GitHub tree and blob APIs, no browser repo reads.Hybrid review. Deterministic checks first, AI triage second.
The report

Know what is risky, what is noise, and what deserves action.

Badger separates vulnerabilities from hardening and posture debt, then shows the evidence behind each call: file and line references, confidence, risk category, AI triage, and conservative fix paths that still require human review.

badger-security.vercel.app/report/{scanId}
scan complete
Badger real scan output report interface

Security report · GitHub tree + blobs

Share Issue body Fix previews
Overall posture
62/ 100
Needs attention before deploy
Critical
2
Block deploy · exposed secret, open route
High · Medium
2 / 4
Auth gaps & unsafe env usage
Low · Info
9
Hygiene & best-practice notes

Findings

All 15Critical 2AI risks 4Secrets 3Routes 6
critical
Secret exposure rule
committed env values:1
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critical
Missing auth rule
sensitive API routes:1
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high
AI abuse guard rule
model endpoints:1
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high
Unsafe tool dispatch rule
agent/tool handlers:1
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medium
Public env misuse rule
NEXT_PUBLIC_* values:1
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medium
Input validation rule
JSON request handlers:1
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low
Production hardening rule
Next.js/Vercel signals:1
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What it reviews

Built for teams moving faster than their security review process.

Badger checks the places where AI-built apps usually break security: auth, secrets, agent tools, data access, dependencies and remediation workflow.

Server-side repo scan

Reads supported GitHub files through the API without cloning, installing dependencies or running untrusted code.

AI and agent risk

Reviews AI routes, tool calling and MCP surfaces for weak auth, missing limits and overly broad execution paths.

Secrets and env vars

Detects committed credentials, risky env files and dangerous browser-exposed NEXT_PUBLIC_* contracts with context-aware filtering.

Routes and auth

Maps route handlers, Server Actions, validation calls, auth signals and database writes so sensitive code gets reviewed first.

Tool and MCP review

Flags shell tools, MCP process spawning, full environment inheritance and tool calls without clear boundaries.

Reports and fixes

Produces evidence-based reports and conservative fix drafts that stay reviewable instead of creating noisy public PRs.

How it works

Four steps from prompt to security review.

01

Paste a repo or connect GitHub

Scan public GitHub URLs without logging in. Connect GitHub only when you want account repositories, private repos or PR creation.

02

Run the harness

The server reads GitHub metadata, tree entries and selected blobs, then runs deterministic analyzers for secrets, routes, dependencies, AI endpoints and repo posture.

03

Review evidence

The report ranks findings with file:line references, confidence, risk breakdown and AI triage so you can tell signal from noise.

04

Fix what is safe

Generate review-required patch previews and PR-ready hygiene changes only when the evidence supports them.

Free in beta

Turn an AI-built repo
into a security decision.

Scan your first repo without installs, ZIP uploads or running untrusted code.