Source Code Vulnerability Scanner: Find Flaws Before They Ship
Written by the Rafter Team
A source code vulnerability scanner reads your codebase and identifies security weaknesses before a single line reaches production. It parses syntax trees, traces data flows, and flags patterns that match known vulnerability classes — injection, broken authentication, hardcoded secrets, insecure deserialization. The scan runs at build time, meaning every finding is actionable while the code is still under active development.
This matters more now than it did two years ago. AI-assisted coding tools generate thousands of lines per day, and developers accept those suggestions faster than they can review them. A source code vulnerability scanner is the backstop that catches what speed-first workflows miss.
Scan your repo with Rafter — results on your first commit, no configuration required.
Why Source Code Vulnerability Scanners Matter for AI-Generated Code
AI code generators optimize for function, not security. They produce working code that passes tests but routinely introduces patterns a security engineer would reject: unsanitized query parameters, overly permissive CORS headers, secrets interpolated into configuration strings. Studies from 2025 showed that LLM-generated code contained exploitable vulnerabilities in roughly 40% of security-sensitive completions.
A source code vulnerability scanner treats AI-generated code the same as human-written code. It traces taint from input to sink regardless of who authored the function. This is the correct approach — vulnerabilities don't care about authorship.
AI coding assistants reproduce vulnerability patterns from their training data. Without automated scanning, these flaws enter your codebase at the speed of autocomplete.
What a Source Code Vulnerability Scanner Detects
Static analysis at the source level catches structural flaws with high confidence:
- Injection vulnerabilities — SQL, command, LDAP, and XPath injection where user input flows into interpreters unvalidated
- Authentication and session flaws — missing checks, weak hashing algorithms, tokens with insufficient entropy
- Secrets in source — API keys, database credentials, and private keys committed to version control
- Insecure data handling — cleartext storage of sensitive fields, missing encryption at rest
- Access control gaps — IDOR patterns, missing authorization checks on privileged endpoints
- Cryptographic weaknesses — deprecated algorithms, static initialization vectors, predictable randomness
Source-level scanning cannot replace runtime testing entirely. Business logic flaws and environment-specific misconfigurations require dynamic analysis or manual review.
How Rafter Scans Source Code
Rafter connects to your repository and runs on every commit and pull request. It performs multi-pass analysis: first a fast pattern scan for secrets and known-bad API usage, then deep taint analysis that follows data across function boundaries, modules, and even inter-service calls in monorepos.
Results appear inline in your pull request with specific line references and fix suggestions. Critical and high findings block the merge by default; informational findings are surfaced without blocking. This keeps your security baseline enforced without creating friction on low-risk changes.
For AI-generated code specifically, Rafter flags patterns that LLMs commonly produce — parameterless query construction, over-permissive file operations, and authentication shortcuts that work in demos but fail in production.
Start scanning your source code with Rafter — setup takes under two minutes.