VibeSec.sh Alternative - VibeEval Comparison

What each product actually is

VibeSec.sh is a curated set of security instructions - vulnerability types, bypass techniques, framework-aware rules - that you install as a project skill or rules file. When Claude Code, Cursor, or Copilot generate code in that repo, they are more likely to avoid classic mistakes: raw SQL concatenation, missing auth middleware, open CORS, hard-coded secrets patterns. The file sits in the repo next to your other agent instructions (.cursorrules, CLAUDE.md, skill packs) and becomes part of the context the model sees when it drafts a route or a database call.

VibeEval is a deployed-app security scanner. You paste a live URL. Autonomous agents probe exposed keys, missing Row Level Security, auth bypasses, BOLA/IDOR across user roles, open storage, and broken API authorization. Output is a findings report with evidence and fix prompts - not better autocomplete. The scanner never claims to rewrite how your IDE thinks; it claims to show what an attacker can still do after the code shipped.

They sit on opposite sides of the same problem: vibe coding ships insecure defaults. One product tries to write fewer bugs. The other tries to prove which bugs still made it to production. Confusing them is how people buy a markdown file and then wonder why their Supabase project is still world-readable.

The generation-vs-verification split

Think of every AI-built feature as two clocks:

1. Generation clock - the model invents files, policies, and configs while you watch the chat. Rules files, system prompts, and skill packs live here. VibeSec.sh is this clock.
2. Verification clock - after deploy, something hostile hits the live surface: the anon key, a second user session, a forged webhook body. Scanners and pentests live here. VibeEval is this clock.

You can improve either clock without the other, but the failure modes are orthogonal. A perfect rules file does not re-enable RLS on a table the model forgot yesterday. A perfect scanner does not stop the next completion from pasting origin: true into CORS. The teams that stop getting surprised treat both clocks as mandatory, not optional nice-to-haves.

Where VibeSec.sh wins

• Lifetime $19 with no subscription - pure prevention cost for solo builders
• 30 vulnerability types and 140 bypass techniques baked into prompts
• Framework-aware: Next.js, Express, Flask, React, Supabase patterns
• 100% local - code never leaves your machine; nothing phones home
• Works offline once installed; no account required for the rules file itself
• Shifts left - influences the first draft, not a post-deploy cleanup
• Composable - drops into multi-tool stacks (Cursor + Claude Code + Copilot) without a SaaS login
• Low friction - one install, then every subsequent generation session inherits the baseline

If your workflow is “I live in one editor and I want every completion slightly safer,” VibeSec.sh is correctly scoped. For pre-product founders who have not deployed yet, generation-time quality is the only lever that exists.

What “framework-aware” actually buys you

Generic “write secure code” prompts under-specify. Framework-aware rules call out the real APIs:

• Next.js: Server Actions need auth() inside the action, not only middleware on page routes
• Express: middleware order matters; app.use(auth) after public routes is a classic miss
• Supabase: RLS is not optional decoration; service role bypasses every policy
• React: dangerouslySetInnerHTML and URL-derived href are XSS defaults in training data
• Flask: debug=True and open SECRET_KEY patterns appear constantly in generated snippets

A rules file that names those surfaces reduces how often the model re-derives the insecure tutorial pattern from memory.

Where VibeSec.sh falls short alone

Why models drift even with good rules

Long agent sessions accumulate pressure to “make the feature work.” When a failing test or a broken preview stands between the model and task completion, security constraints lose priority. Multi-file refactors also drop context: the rules file is still in the prompt, but the model is deep in a payment webhook and “temporarily” skips signature verification to unblock the demo. Human reviewers miss those one-line omissions; scanners that hit the live endpoint do not.

Another failure mode: the rules apply to code the model generates, not to dashboard toggles. Creating a Supabase table via the dashboard UI, cloning a Firebase project with test-mode rules, or flipping a storage bucket to public never passes through the rules file at all.

Concrete gaps prompts cannot close

1. RLS disabled on a new table - the model created orders yesterday with policies; today it added invoices without them. A rules file does not re-check the live project.
2. Service role in the browser - an Edge Function or env mis-wire can expose god-mode keys. Only a live bundle/API probe finds that.
3. BOLA on generated CRUD - the model “followed” auth rules but forgot ownership checks on GET /api/items/:id. Runtime ID swapping is the only honest test.
4. Public storage after a feature ship - “make avatar upload work” often ends as a public bucket. Prompts do not walk your Storage console.
5. Webhook signature skipped - Stripe/GitHub handlers that accept any body. Needs request-level testing, not editor guidance.
6. Preview env with production secrets - Vercel/Netlify/Railway previews inherit prod keys; the rules file never sees the host config panel.
7. CORS opened “just for local testing” and left in production middleware.
8. Source maps public on the production CDN, amplifying any secret that slipped into a chunk.

Feature comparison

How the failure modes differ

Generation-time failures (VibeSec.sh territory)

• Model suggests allow read, write: if true style Firebase rules “to get it working”
• Completes a login form without server-side session checks
• Pastes example API keys from training data patterns
• Uses dangerouslySetInnerHTML for “rich content”
• Opens CORS with origin: true to silence a browser error
• Builds SQL with string templates because the open file already did it once
• Suggests a package name that does not exist (hallucination / typosquat risk)
• Adds a debug /admin route and forgets to strip it before merge

A strong rules file reduces how often those patterns appear in the first draft. It does not guarantee they never appear after five agent sessions, three “just fix the build” prompts, and a late-night merge.

Deploy-time failures (VibeEval territory)

• Tables exist with RLS off in production Supabase
• Anon key works as a full database client against production data
• Admin route returns 200 without a session
• Changing user_id in a path returns another tenant’s JSON
• Secret keys ship in the JS bundle or source maps
• Storage objects are world-readable by URL
• Webhook endpoint accepts unsigned bodies and marks orders paid
• Preview deployment serves the full product without Deployment Protection

These only show up when something hits the live URL with hostile assumptions. That is scanner work. For the ownership-check class specifically, see BOLA in AI-generated CRUD.

A realistic workflow that uses both

1. Install VibeSec.sh (or equivalent security rules) in the repo so Composer / Claude Code start from a safer baseline.
2. Ship to a preview URL on Vercel, Netlify, Railway, or Lovable hosting.
3. Run VibeEval against that URL on every meaningful deploy - especially after agent-driven refactors.
4. Paste fix prompts from the scan back into the same IDE that uses the rules file.
5. Rescan until critical findings close.
6. Repeat after the next feature prompt - rules do not prevent regression on new tables and routes.

Month-one cost: ~$19 + one Pro month (or trial). Still cheaper than a single hour of freelance review, and you keep continuous coverage after the rules file stops being “new.”

What the closed loop looks like in practice

# After install: rules live in-repo
ls .cursor/rules/  # or skills / CLAUDE.md includes security pack

# Agent builds feature → push preview
git push origin feature/invoices

# Verify the live surface that the rules cannot see
# (VibeEval UI or CLI against the preview URL)
# Critical: invoices table answers without auth

# Paste fix prompt into Cursor / Claude Code
# Agent adds RLS + ownership checks with rules still in context

# Rescan same URL until clear, then merge

The rules file makes the second generation more likely to emit correct policies on the first try. The scanner is the only thing that proves the policies are actually active on the project you deployed.

When to pick only VibeSec.sh

• You are pre-production and optimizing how the model writes
• Solo dev, one editor, tight budget, not yet on a public URL
• Compliance forbids sending app traffic to third-party scanners (rare for public SaaS; more common for air-gapped)
• You already have an internal DAST / pentest program and only need generation-time lift

When to pick only VibeEval

• You already shipped and need exploit evidence, not better prompts
• Stack is Supabase, Firebase, or multi-tenant SaaS where RLS/authz is the risk
• Multiple people / agents touch the repo and you need a gate, not guidance
• You want findings you can show a customer, investor, or buyer during diligence
• You tried “better prompts” for a month and still keep finding open tables

When to pick both

• Indie hackers shipping Lovable / Cursor / Bolt apps to real users
• Agencies delivering client apps who need a before/after security story
• Anyone who has already been burned by “the AI said it was fine”
• Teams where junior builders accept agent diffs without deep review
• Product velocity that ships weekly - prevention alone cannot keep up with regression

Common misconceptions

“If the AI follows security rules, I don’t need a scan.”
Models drift. Features land without re-applying rules. Config lives outside the code the model sees. Dashboard-created tables never read your skill file.

“A scanner replaces secure coding guidance.”
Scanners find what’s broken; they don’t stop the next insecure completion. Prevention + detection is the adult setup. Detection alone means you always discover problems after they hit a URL.

“$49/mo is expensive next to $19 lifetime.”
Different jobs. One is a document. One is ongoing verification of a running system. Compare VibeEval to a junior QA hour or a pentest quote, not to a markdown file. A single avoided key leak often pays for a year of scanning.

“I already run SAST in CI, so I’m covered.”
SAST is generation-adjacent static analysis. It still cannot prove RLS is on, that Preview is protected, or that user B cannot read user A’s invoice. Pair SAST with live probes; see Between SAST and Pentest.

“The rules file is outdated after six months.”
True for any static document. Revisit when you change stacks (new auth provider, new host). The bigger risk is not stale rules - it is assuming rules still equal production safety without a re-scan after every agent session.

Example: same app, both tools

Imagine a Lovable/Cursor SaaS with Supabase:

1. With VibeSec.sh installed, the model is more likely to emit auth middleware and warn about secrets when generating a new route.
2. You ship anyway under deadline. A new invoices table lands without RLS.
3. VibeEval against production shows anonymous select on invoices.
4. You paste the fix prompt into the IDE (still using the rules file) and rescan until clear.

Neither step alone would have been enough: the rules file reduced odds; the scan caught the miss.

A second scenario: webhook trust

1. Rules file encourages “verify Stripe signatures.” Model still ships a handler that logs the body and marks paid=true to pass a local test.
2. No amount of IDE guidance detects that production accepts unsigned POSTs.
3. VibeEval (or a manual signature-skip test) fails the endpoint; fix prompt restores stripe.webhooks.constructEvent.
4. Rescan confirms only signed events update order state.

See Stripe webhook and paid trust for the full pattern.

A third scenario: service role leak

1. Model needs an admin report. Rules say “never put service_role in the client.” Model creates an Edge Function correctly - then a later “show the report in the dashboard” prompt wires the service role into a Vite env var “so the chart can fetch.”
2. Rules file cannot un-inline a build-time secret after the fact.
3. Token Leak Checker / VibeEval surface scan finds service_role in the bundle; you rotate, move fetch server-side, rescan.

Side-by-side economics

Rough cost of the pair over a year: $19 + 12×$49 ≈ $607. One freelance security review hour in many markets already exceeds that. Continuous coverage is the difference: the rules file does not re-run after deploy #40.

Migration / switching notes

• From VibeSec.sh-only → add VibeEval: no migration; paste URL, run scan, keep the rules file.
• From other SAST tools → VibeEval: keep SAST for code patterns; add DAST for deploy URL.
• From pentest-only → continuous: use VibeEval between annual tests so AI velocity does not reopen holes.
• From “we prompt carefully” → rules product: VibeSec.sh packages what careful teams reinvent; still add a scanner when the URL is public.
• From Burp/ZAP manual only: keep human creativity for business logic; automate the AI-shaped checklist so you stop re-finding the same open RLS every quarter.

Installing rules without cargo-culting

Buying a rules file is not magic. Practices that make it stick:

• Keep the security skill in a path every agent actually loads (project rules, not a random markdown folder nobody references).
• Re-assert critical constraints in long chats: “RLS on every new table; no service_role in browser.”
• When the model proposes a temporary security exception, reject it in the same session - temporary becomes permanent in git history.
• After large refactors, run git diff main...HEAD --stat and re-scan; scope creep is where rules get skipped.
• Treat fix prompts from VibeEval as first-class inputs back into the same rules-aware IDE so remediation stays consistent with generation guidance.

Who should not buy either

• You have no deployed URL and no plan to ship (wait).
• You need a formal accredited pentest PDF for enterprise procurement (buy a pentest; use VibeEval between cycles).
• Your app is fully air-gapped with no external scanning allowed (use internal DAST + rules files only).
• You only need a marketing site with no backend (threat model is different - custom code XSS still matters, but neither product is your primary tool).

Related products in the same confusion set

Several tools share the “vibe” or “sec” naming and solve different problems:

• Prompt / skill packs (VibeSec.sh class) - generation guidance
• SAST / lint for AI code - static patterns in source
• Live app scanners (VibeEval class) - runtime evidence on a URL
• Enterprise AppSec platforms - broad CI/CD programs for large orgs

Pick by job-to-be-done, not by brand overlap. See the alternatives hub for more pairings, including other vibe-named scanners and traditional SAST.

Bottom line

VibeSec.sh is a cheap, local, lifetime lever on how models write. VibeEval is a recurring lever on what actually runs. They are not substitutes. If you only buy one, buy the one that matches your current bottleneck: pre-ship generation quality, or post-ship exploit proof. If you ship weekly with AI agents, buy both and close the loop with fix prompts until the live URL stays clean.

Related

• All alternatives - full comparison hub
• Vibe Coding Security Risks
• Vibe Code Scanner
• Is Lovable Safe?
• Supabase RLS Checker
• OWASP Top 10 for AI Code
• Secure AI Coding Practices