How Agencies Can Security-Check AI-Built Client Sites
A practical agency workflow for checking AI-built client websites before handoff: review secrets, auth, headers, dependencies, and agent permissions.
· 8 min read
Agencies should security-check AI-built client websites before handoff by reviewing secrets, auth, headers, dependencies, forms, and any agent or plugin access that can touch the app or repo. The fastest way to do that is a pre-launch workflow: inspect the codebase, verify risky defaults, test common injection paths, and run a scanner like CyberLens AI scan before the client goes live.
Why AI-built client sites need a different delivery check
AI-assisted builds move fast, but speed creates a specific kind of launch risk. The site may look finished while still shipping exposed API keys, weak auth flows, overbroad admin access, insecure headers, or dependencies that were never reviewed. Agencies also inherit the trust problem: if the site was assembled with generated code, plugin automation, or agent assistance, you need to verify what was actually shipped, not just what the prompt intended.
That matters even more when the client expects you to hand off a production-ready site. A design review is not a security review. A working checkout is not a secure checkout. For agencies, the goal is simple: catch the issues that are cheap to fix before launch, then keep a repeatable scan workflow for retainer monitoring.
What to check before handoff
Start with the parts of the app that are most likely to break in vibe-coded or AI-generated projects. These are the areas that often look fine in staging but create real exposure after deployment.
| Area | What to look for | Why it matters |
|---|---|---|
| Secrets and config | API keys, tokens, webhook secrets, private URLs in env files, config, or client bundles | Leaked secrets can expose third-party services, data, or production systems |
| Authentication | Weak login logic, missing session checks, exposed admin routes, insecure password reset flows | Auth mistakes are common in generated code and expensive after launch |
| Input handling | Unsanitized form fields, query params, rich text, file uploads, and URL-based inputs | Injection and XSS often start in simple forms or search boxes |
| Security headers | Missing CSP, HSTS, X-Frame-Options, and related browser protections | Headers reduce the blast radius of browser-side attacks |
| Dependencies | Old packages, risky transitive modules, suspicious install scripts, overused plugins | AI code often pulls in packages without a proper review |
| Agent and tool access | Filesystem, shell, browser, network, MCP tools, or build hooks with too much power | Overbroad automation can turn a small mistake into a full compromise path |
Agency delivery checklist for AI-built websites
Use this as a handoff gate before launch or client acceptance. It is intentionally practical and focused on the stuff agencies can verify quickly.
- Check for secrets in code and config. Review environment files, server code, build output, and client-side bundles for exposed tokens or keys.
- Review auth and role boundaries. Confirm admin routes, protected pages, and API actions are actually guarded.
- Inspect forms and user inputs. Look for XSS, SQL injection patterns, unsafe redirects, and file upload handling.
- Verify security headers. At minimum, check CSP, HSTS, and clickjacking protections.
- Review dependencies and install scripts. Watch for outdated packages, risky postinstall behavior, and unnecessary plugins.
- Check agent or automation permissions. If the build used OpenClaw, CLAW, Hermes, MCP, or similar tooling, review what can access files, shell, browser, and network.
- Confirm logging does not expose sensitive data. Error logs and analytics should not leak tokens, passwords, or personal data.
- Validate production config. Make sure debug mode, test endpoints, and staging defaults are disabled.
- Document what was reviewed. Agencies should keep a short record of what was checked, what was fixed, and what remains accepted risk.
Where AI-generated code usually hides launch risk
AI-built sites often fail in familiar patterns, but the failure mode is different because the code can be broad, confident, and inconsistent. A generated login flow might work perfectly while still exposing a route that bypasses authorization. A landing page might be clean while the form handler sends data to an unvetted endpoint. A checkout app might use a secure frontend but rely on a backend package with a known issue.
Watch especially for these patterns:
- Secrets in config files: tokens committed to the repo, copied into example files, or embedded in frontend code.
- Insecure defaults: debug flags, permissive CORS, open admin endpoints, or overly broad test credentials.
- Missing security headers: especially on marketing sites, where teams assume the risk is low.
- Possible SQL injection patterns: string-built queries, raw interpolation, or unreviewed database helpers.
- Dependency review gaps: packages added by the model because they were convenient, not because they were vetted.
If the site was built with an AI workflow that also used repository automation or local agents, the risk is not just in the app code. It is also in the surrounding permissions. A tool that can write files, run commands, or browse the network can introduce changes the agency never intended to approve.
Run This Scan Before You Hand It Over
Before client delivery, run a focused security pass with CyberLens AI scan. Treat it as the first-pass filter for launch risk, not the final word. The goal is to catch obvious exposure quickly so a developer or security reviewer can fix it before the client sees it.
- Scan the repository or deployed site for secrets, auth issues, weak headers, and dependency risk.
- Review any findings tied to forms, API routes, admin access, and browser-facing code.
- Check whether the app was built with agent tools, OpenClaw/CLAW skills, or MCP integrations that widen access.
- Confirm the scan results against the actual deployment environment, not just local dev settings.
- Fix the high-confidence issues first, then rerun the scan before handoff.
If you are comparing tooling for this step, the most useful supporting page is repository security scanners for AI code. That helps agencies decide whether they need a quick pre-launch pass, deeper repository review, or both.
How agencies should structure the workflow
The best agency process is simple enough to repeat on every project. If your team has to invent the process each time, it will not survive deadline pressure.
1. Intake
Ask how the site was built. Was it hand-coded, AI-assisted, or assembled with agents and plugins? Did the build use OpenClaw skills, MCP servers, or automation that touched the repo or deployment environment? The answer changes what you inspect first.
2. Pre-launch review
Run a code and config review for secrets, auth, input handling, headers, and dependency signals. If the project includes generated code, review the most sensitive paths first: login, payments, forms, file upload, admin actions, and any server-side automation.
3. Scan and verify
Use CyberLens AI on the repo or site to surface common launch risks. Then verify the findings manually in the context of the client’s stack. Agencies should not rely on a single tool output as proof of security, but a scanner is a fast and useful gate before handoff.
4. Fix and retest
Close the obvious issues, rerun the scan, and document the remaining accepted risk. If the client wants a retainer, keep the same workflow for monthly or quarterly checks so new plugins, package updates, and content changes do not quietly reintroduce exposure.
What to tell clients without sounding alarmist
Clients do not need a lecture on attack chains. They need a clear explanation of why the agency is adding a security step. The simplest framing is: AI speeds up delivery, but it also increases the chance of missing a basic security control. A short pre-launch scan reduces avoidable risk and helps protect the launch date, the brand, and the support queue.
That framing works well because it is practical. It does not claim the site is unsafe by default. It just acknowledges that generated code and fast delivery deserve a tighter release checklist.
Use CyberLens as part of a repeatable agency boundary
For agencies, the real win is not a one-time scan. It is a delivery boundary that says: if the site was AI-built, it does not ship until the obvious security checks are done. That boundary helps with scope control, client trust, and fewer late-night fixes after launch.
Start with CyberLens AI scan for the pre-launch pass, then use the workflow again whenever the client adds a new integration, plugin, or agent-powered feature. If your team builds a lot of AI-assisted sites, keep the process close to your delivery checklist and pair it with the security scanner for AI builders page when you need to explain the workflow internally or to a client.
FAQ
What should agencies check first on an AI-built client website?
Start with secrets, authentication, security headers, input handling, and dependencies. Then review any agent or automation access that can write files, run commands, or touch the network. Those are the most common launch-risk areas in AI-built sites.
Can CyberLens AI replace a manual security review?
No. CyberLens AI is best used as a fast first-pass scanner for launch risk. Agencies should still verify findings manually and review the app in context, especially for auth, business logic, and deployment-specific issues.
Why do AI-generated websites need extra security checks?
AI-generated code often ships quickly and may include insecure defaults, exposed secrets, weak auth logic, risky dependencies, or missing security headers. The speed of generation makes it easier to miss these issues before launch.
What is the best pre-launch workflow for agencies?
Use a repeatable gate: intake the build source, review sensitive code paths, scan for common risks, fix high-confidence issues, rerun the scan, and document what was accepted. That keeps delivery consistent across client projects.
Should agencies scan OpenClaw or MCP-based projects differently?
Yes. Add a permission review for files, shell, browser, and network access, plus any skill or tool manifests. Those integrations can widen the blast radius even when the website itself looks normal.