Rafter - Code Vulnerability Scanner — Find Flaws Before Deployment

A code vulnerability scanner analyzes your source code for security weaknesses before you ship. It catches SQL injection, cross-site scripting, hardcoded secrets, and insecure dependencies — the flaws attackers exploit first. Without one, you're relying on manual review to spot problems that automated tools find in seconds.

Rafter's scanner is built for modern development workflows, including AI-generated code that traditional static analysis tools weren't designed to handle.

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How a Code Vulnerability Scanner Works

A code vulnerability scanner performs static application security testing (SAST) by reading your source code without executing it. It parses your codebase into an abstract syntax tree, traces data flows from user input to sensitive operations, and flags patterns that match known vulnerability signatures.

Rafter goes further. It combines pattern matching with AI-powered contextual analysis to understand what your code is trying to do — not just what it looks like. This catches vulnerabilities that rule-based scanners miss, especially in AI-generated code where insecure patterns are syntactically correct but logically flawed.


// ✗ Vulnerable — unsanitized input in query
const query = `SELECT * FROM users WHERE id = ${req.params.id}`;

// ✔ Secure — parameterized query
const query = 'SELECT * FROM users WHERE id = $1';
const result = await db.query(query, [req.params.id]);

A good scanner flags the first pattern and explains why the second is safer, giving developers remediation steps they can act on immediately.

Why Code Vulnerability Scanning Matters

The 2024 Snyk State of Open Source Security report found that 80% of application codebases contain at least one known vulnerability. AI-assisted development accelerates this problem — LLMs trained on public repositories reproduce insecure patterns at scale, and developers who trust the output skip the review that would catch them.

Vulnerabilities that reach production cost 30x more to fix than those caught during development. A code vulnerability scanner shifts detection left, catching flaws when they're cheap to fix and before they become incidents.

AI code generators produce syntactically valid but security-blind code. A code vulnerability scanner is the minimum viable safety net for any team using Copilot, Cursor, or vibe coding platforms.

Key Capabilities to Look For

Language coverage — Supports your stack across frontend, backend, and infrastructure-as-code
CI/CD integration — Runs automatically on every pull request and blocks merges on critical findings
AI-generated code detection — Identifies insecure patterns specific to LLM-produced code
Actionable remediation — Provides fix suggestions with code examples, not just CVE references
Low false-positive rate — Contextual analysis reduces noise so developers trust the results

Start securing your code — free →

How a Code Vulnerability Scanner Works

// ✗ Vulnerable — unsanitized input in query const query = `SELECT * FROM users WHERE id = ${req.params.id}`; // ✔ Secure — parameterized query const query = 'SELECT * FROM users WHERE id = $1'; const result = await db.query(query, [req.params.id]);

A good scanner flags the first pattern and explains why the second is safer, giving developers remediation steps they can act on immediately.

Why Code Vulnerability Scanning Matters

AI code generators produce syntactically valid but security-blind code. A code vulnerability scanner is the minimum viable safety net for any team using Copilot, Cursor, or vibe coding platforms.

Key Capabilities to Look For

Language coverage — Supports your stack across frontend, backend, and infrastructure-as-code

CI/CD integration — Runs automatically on every pull request and blocks merges on critical findings

AI-generated code detection — Identifies insecure patterns specific to LLM-produced code

Actionable remediation — Provides fix suggestions with code examples, not just CVE references

Low false-positive rate — Contextual analysis reduces noise so developers trust the results