VPS 4 Builder
136 lines
4.6 KiB
Markdown
136 lines
4.6 KiB
Markdown
# Security Audit Playbook for Antigravity
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## Goal
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Audit a Next.js or React codebase for common security issues, with extra attention to AI-assisted application patterns such as LLM endpoints, agent flows, tool execution, and prompt injection risks.
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## Inputs
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- the repository currently open in the IDE
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- optional scope from the user, such as a folder, route group, PR diff, or branch comparison
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- optional prior report for before/after comparison
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## Outputs
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Produce a markdown report with these sections:
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1. Executive summary
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2. Scope and framework detection
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3. Findings grouped by severity
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4. Manual review items
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5. Remediation roadmap
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6. Before/after deltas if a baseline exists
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Each finding must include:
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- title
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- severity: critical | high | medium | low
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- confidence: high | medium | low
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- status: confirmed | probable | manual-review
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- evidence
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- impact
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- affected files
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- recommended fix
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## Workflow
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1. Detect framework and project shape.
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- If `app/` and `next.config.*` exist, inspect App Router paths.
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- If `pages/` exists, inspect Pages Router paths.
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- If `src/`, `public/`, `vite.config.*`, `react-scripts`, or `index.html` exist without Next.js markers, inspect as React SPA.
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- In monorepos, identify the active app package before scanning.
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2. Prioritize high-risk surfaces.
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- API routes and route handlers
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- auth and session code
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- database access and raw queries
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- LLM, tool, and agent endpoints
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- upload handlers, webhooks, admin routes
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- client storage of tokens or secrets
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3. Run deterministic scan.
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- Execute `node scripts/security-audit.js .`
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- Review the JSON findings
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- Do not present raw script output without checking context
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4. Validate findings against source context.
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- Confirm whether the evidence really indicates a vulnerability
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- Downgrade noisy heuristics to probable or manual-review
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- Prefer confirmed findings only when code evidence is strong
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5. Produce report.
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- Use `templates/security-report.md`
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- Keep the report concise, actionable, and evidence-based
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6. If asked for fixes, propose minimally invasive remediations.
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- Prefer framework-native solutions
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- For Next.js, consider middleware, route handlers, server-only boundaries, secure cookies, and validation at the server boundary
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- For React SPA, prefer backend token storage, CSP, input sanitization, route guards, and safe rendering
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## Coverage
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### Next.js Pages Router
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Inspect:
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- `pages/api/**/*`
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- `pages/**/*`
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- `lib/**/*`
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- `middleware.*`
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- `next.config.*`
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### Next.js App Router
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Inspect:
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- `app/api/**/route.*`
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- `app/**/*`
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- server actions
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- `middleware.*`
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- `next.config.*`
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- `src/app/**/*`
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### React SPA
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Inspect:
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- `src/**/*`
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- `public/**/*`
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- `vite.config.*`
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- `webpack.config.*`
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- `index.html`
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- API client wrappers and auth state management
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## Security priorities
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### Critical
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- confirmed exposed secrets
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- raw SQL with direct user interpolation
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- unsafe privileged tool execution from user-controlled prompt or input
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- authentication bypass in admin or privileged routes
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- unsigned or unverified webhook processing for sensitive actions
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### High
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- missing validation on server boundary
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- prompt injection exposure in LLM endpoints
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- SSRF patterns in server-side fetches
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- token persistence in localStorage or sessionStorage
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- dangerous HTML rendering with untrusted input
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### Medium
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- missing rate limiting on auth or expensive endpoints
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- weak cookie flags
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- permissive CORS for sensitive routes
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- missing CSRF controls where cookie auth is used
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- overexposed `NEXT_PUBLIC_*` or client config leakage
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### Low
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- missing security headers
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- weak logging hygiene
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- best-practice gaps without direct exploit path
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## Guardrails
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- Never claim a finding is confirmed without source evidence.
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- Separate severity from confidence.
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- If a pattern might be framework-supported elsewhere, mark manual-review.
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- Do not treat the existence of `.env` as critical by itself; only escalate if it appears tracked, exposed, copied to client code, or committed.
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- For React projects, assume browser code is untrusted and prioritize secret exposure and token handling.
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## Common manual review prompts
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- Verify whether admin routes are protected by upstream middleware.
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- Verify whether a raw query builder safely parameterizes values.
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- Verify whether sanitization exists in a shared utility instead of the local file.
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- Verify whether LLM tool calls enforce allowlists, authz, and argument validation.
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## Example agent request
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"Audit this repository using ANTIGRAVITY.md. Start with API, auth, secrets, and AI integration surfaces. Run the scanner, review the top findings manually, and generate SECURITY_AUDIT_REPORT.md."
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