Other scanners were built
for code humans wrote.
Yours is written by AI.
165+ security rules. 21 LLM-specific attack vectors. 15 AI vibe smell patterns. Zero install. Results in under a second.
The blind spot nobody's talking about
AI writes the code. The scanner doesn't know that.
You're building faster than ever. Cursor drafts your routes, Copilot fills in your handlers, your AI assistant sketches the auth flow. The code mostly works. But “mostly works” and “mostly secure” are very different problems - and your existing tools were built before AI-generated code existed.
Security tooling hasn't caught up with where vibe coders actually work
CLIs, security libraries, and repo scanners assume you're at a terminal with a full project checked out. But vibe coders paste from ChatGPT, Cursor, and Claude directly into their editor, then ship. The existing security ecosystem wasn't built for that workflow. SecurePilot is.
Generic scanners predate LLMs
Traditional SAST tools were trained on CVEs from 2010. They know what SQL injection looks like. They have no concept of a prompt injection vector, a hallucinated import with a known malware twin, or an API key being bundled into client-side JavaScript. These aren't edge cases - they're patterns AI produces every single day.
Your AI assistant optimizes for “it works”
Copilot and Cursor are autocomplete engines. Their job is to produce valid, runnable code - not hardened code. They've shipped auth stubs that always return true, LLM API keys inside React components, and user input wired directly into system prompts. They don't warn you because that's not what they were built to do.
IDE security hints miss the attack surface
ESLint security plugins and basic linters catch obvious syntax-level mistakes - like a bare eval() call. They won't catch the business logic flaw where a price can be set to -$50, or the RAG pipeline that trusts user documents as system context. You need a scanner that understands intent, not just syntax.
Real examples. Real risk.
Things other tools missed this week
These aren't theoretical. These are patterns AI assistants produce routinely.
ALWAYS_TRUE_AUTH · CRITICAL
Always-true auth stub
function isAuthenticated() {
return true; // TODO: implement
}AI assistants write placeholder auth to get the app running. They mean to come back. They never do. Every protected route in your app just became public.
LLM_PROMPT_IN_CLIENT_CODE · HIGH
LLM API key in client bundle
const openai = new OpenAI({
apiKey: process.env
.NEXT_PUBLIC_OPENAI_KEY,
dangerouslyAllowBrowser: true
});NEXT_PUBLIC_ variables ship to every user's browser. That key gets scraped. Your OpenAI bill hits $4,000 overnight. Not hypothetical.
RAG_UNSANITIZED_RETRIEVAL · CRITICAL
User doc wired into system prompt
const doc = await getDocument(id);
await openai.chat({
messages: [
{ role: "system", content: sys },
{ role: "user", content: doc }
]
});That retrieved document is now part of your system context. An attacker who can write to that doc controls your AI. Classic indirect prompt injection.
Page 1 of 5 · 15 examples total
The complete picture
Security that speaks AI.
SecurePilot was designed from day one for the way AI tools write code. Not retrofitted. Not “good enough.” Every rule maps to a real attack vector that AI assistants produce - because we studied them, ran them, and built detectors specifically for them.
Here's how that stacks up:
How it compares
Built different. For the AI era.
| Feature | Generic SAST | AI Coding Assistant | AI-Native Scanners | SecurePilot ✦ |
|---|---|---|---|---|
| SQL / XSS / Injection detection | Partial | |||
| LLM Prompt Injection detection | Partial | |||
| AI Code Smell detection | ||||
| Business Logic Vulnerabilities | ||||
| OWASP LLM Top 10 2025 coverage | Partial | |||
| 🔌 MCP & Agent tool security | ||||
| 👻 Package hallucination detection | Partial | |||
| Zero install, browser-based | ||||
| Free forever (transparent pricing) | Partial | |||
| AI fix explanations | Partial | |||
| Under 1 second results | ||||
| No code storage or logging | Partial | |||
| 📊 Live daily findings (public proof) | ||||
| ⚡ Deterministic rule engine (no hallucinations) |
“Generic SAST” refers to traditional static analysis tools. “AI Coding Assistant” refers to AI-powered autocomplete tools with security awareness. “AI-Native Scanners” refers to newer AST-based tools built specifically for AI-generated code. All comparisons reflect publicly documented capabilities as of early 2026.
A fair question
Can you trust AI to catch what AI broke?
It's a legitimate concern. If your AI assistant wrote insecure code, and you're using another AI tool to review it - what stops the reviewer from hallucinating findings? Or worse, missing real ones?
How SecurePilot actually works
Your code goes in
Paste, drag file, or load from GitHub URL. No account required.
Rules engine runs - no AI involved
165+ deterministic rules, hand-authored, versioned. Each rule either fires or it doesn't. No model, no randomness, no drift.
AI explains what the rules found
SecurePilot AI describes why the flagged line is dangerous and how to fix it. It can't invent findings - it can only explain what the rules already confirmed.
Detection is deterministic.
Explanation is AI-assisted.
They never overlap.
Why this matters
Zero false positives from hallucination
Every finding traces back to a specific rule ID (e.g. LLM_PROMPT_IN_CLIENT_CODE), a line number, and a CWE/OWASP reference. You can verify every single one independently.
Rules are static, versioned code
Detection rules live in inspectable TypeScript. They run identically every time. No model drift, no prompt sensitivity, no randomness - the same code gets the same result, always.
AI explanations are advisory, not authoritative
When SecurePilot AI explains a finding, it's describing a real vulnerability the rules engine already confirmed. It can't flag something that didn't trigger a rule - and it can't un-flag something that did.
“The judge is deterministic. The lawyer speaks English.”
The hard numbers
165+ rules. Built for today.
Enterprise-ready
Designed for how teams build today.
When your team ships AI-assisted code at scale, every finding needs to map to something your security team can act on immediately.
Compliance references on every finding
Every vulnerability SecurePilot detects ships with standards references. Your security team can triage immediately. Your audit trail writes itself.
Standards we cover
Zero onboarding friction
No CI/CD integration required to start. Devs paste code and get results instantly. Team-wide adoption in minutes, not sprints.
Your code stays private
Your code is never shared, sold, or used to train models. Scan results are tied to your account only and nothing is exposed beyond you.
Team & Pro plans coming
Batch scanning, shared dashboards, CI integration, and white-label reports. Join the waitlist for early access and 20% off launch pricing.
See PricingStart scanning free.
Scale when you're ready.
Free forever for individual devs. Pro and Team plans for when you ship at scale.