prbl-scanner 0.1.0

AI code security scanner — finds what AI coding tools miss

prbl-scanner

Open source vulnerability scanner for AI-generated code.

Prbl finds the security vulnerabilities that AI coding tools produce systematically — the patterns that exist because of how LLMs were trained, not because of developer mistakes.

What it detects

• PRBL-C001 — Hardcoded credentials and fallback secrets
  CWE-798 · OWASP A07 · #7 most critical web security risk
  Detects API keys, passwords, and tokens hardcoded directly in source code. Also catches the AI-specific fallback pattern: process.env.SECRET || 'default_value' where the fallback becomes the live secret for any deployment missing the environment variable.

• PRBL-R001 — Weak randomness in security contexts
  CWE-338 · OWASP A04 · #4 most critical web security risk
  Flags Math.random(), random.random(), and related functions when used to generate tokens, session IDs, passwords, or OTPs. These functions are not cryptographically secure — their output is predictable.

• PRBL-I001 — SQL injection including multi-line patterns
  CWE-89 · OWASP A05 · #5 most critical web security risk
  Detects user input concatenated or interpolated into SQL queries, including multi-line query construction patterns that most scanners miss.

• PRBL-I002 — Command injection
  CWE-78 · OWASP A05 · #5 most critical web security risk
  Detects user input passed to shell commands via exec, spawn, system, popen, subprocess.run, and shell=True.

• PRBL-I003 — Code injection (eval/exec)
  CWE-94/95 · OWASP A05 · #5 most critical web security risk
  Detects user input passed to eval(), exec(), new Function(), or compile(). Gives an attacker full code execution on the server.

• PRBL-A001 — Missing access control including serverless handlers
  CWE-862 · OWASP A01 · #1 most critical web security risk
  Detects route handlers and serverless functions that perform sensitive operations (database access, payment processing, user data) with no visible authentication or authorization check.

• PRBL-P001 — Hallucinated package references
  Emerging — no CWE · OWASP A03 · Supply Chain Failures
  Detects imports of packages that do not exist on PyPI or npm. AI models invent plausible-sounding package names. An attacker who registers the name with a malicious payload gets code execution on every machine that runs install.

Security Standards Mapping

Every Prbl rule maps to established security standards. When a developer asks an AI tool "how serious is this finding?" — the CWE and OWASP category give it the full context to answer accurately.

Rule	Name	CWE	OWASP 2025	OWASP Rank
PRBL-C001	Hardcoded Credentials	CWE-798	A07 — Authentication Failures	#7
PRBL-R001	Weak Randomness	CWE-338	A04 — Cryptographic Failures	#4
PRBL-I001	SQL Injection	CWE-89	A05 — Injection	#5
PRBL-I002	Command Injection	CWE-78	A05 — Injection	#5
PRBL-I003	Code Injection	CWE-94/95	A05 — Injection	#5
PRBL-A001	Missing Access Control	CWE-862	A01 — Broken Access Control	#1
PRBL-P001	Hallucinated Packages	Emerging — no CWE	A03 — Supply Chain Failures	#3

Why PRBL-P001 has no CWE

Hallucinated package references are a new vulnerability class created by AI coding tools. Standard CWE scanners cannot detect this by definition — there is no CWE entry because this failure mode did not exist before LLMs generated code at scale. Prbl is the only scanner that catches it.

PRBL-P001 maps to OWASP A03 (Supply Chain Failures) because a malicious actor can register the hallucinated package name on PyPI or npm — turning every project using that AI-generated import into an unintentional malware distribution point.

Why open source

Security tools that scan your code should be auditable. These are the exact rules Prbl uses. Nothing hidden.

Install

pip install prbl-scanner

Usage

prbl-scanner scan ./myproject

JSON output

Every finding includes CWE and OWASP fields for downstream tooling:

{
  "rule_id": "PRBL-C001",
  "title": "Hardcoded credential: Stripe live secret key",
  "cwe": "CWE-798",
  "owasp_category": "A07 — Authentication Failures",
  "owasp_rank": 7,
  "severity": "HIGH",
  "file": "auth/stripe.py",
  "line": 14,
  "detail": "...",
  "fix": "..."
}

Validated against

74 public repos across Django, FastAPI, Express, NestJS, and full-stack codebases. 6.25% false positive rate.

Contributing

Found a new AI vulnerability pattern? Open a PR. Rule format is documented in CONTRIBUTING.md.

License

MIT