Claude hallucinates packages that don't exist. Cursor skips input validation. Copilot suggests vulnerable patterns. Pinata finds what they leave behind.
One command: npx pinata-security-cli analyze . --verify
AI coding assistants ship code fast. They also ship vulnerabilities. These are real patterns we've seen in production codebases.
AI suggests npm packages that don't exist. Attackers register them. Your app installs malware. 35% of Claude suggestions reference non-existent packages.
Malicious instructions hidden in code comments, variable names, or Unicode. When AI reads and executes, it follows the hidden commands.
AI prioritizes "working code" over secure code. Input validation, auth checks, and error handling are often missing or incomplete.
Training data cutoffs mean AI suggests deprecated APIs and known-vulnerable patterns. MD5 for passwords. eval() for parsing. HTTP for APIs.
AI writes tests that pass by testing the implementation, not the behavior. Mocks that return hardcoded values. Assertions that never fail.
AI copies example code with placeholder API keys that look real. Or generates .env files without .gitignore. Your keys end up on GitHub.
Multiple detection methods that reinforce each other. When Layer 1 flags something, Layers 2-5 verify it. False positives drop to near zero.
47 detection categories with regex and AST patterns. Casts a wide net across your codebase.
Auto-detects CLI, web server, library, serverless, etc. Adjusts rules - blocking I/O is fine in CLI, not in Express.
Fast rules that eliminate obvious false positives. Comments, test files, dead code.
Claude analyzes each match in context. Is this actually exploitable? What's the attack vector?
AI writes complete, runnable test files that target your specific vulnerable code. Not templates. Real tests that fail until you fix the vulnerability.
Runs exploit tests in a Docker sandbox. If the exploit succeeds, the vulnerability is confirmed.
Other scanners give you a report. Pinata gives you a test file that blocks your CI until the vulnerability is fixed.
Reads the full function, its imports, your test framework, database type, and existing test style. Not a 5-line snippet. The whole picture.
Not templates with placeholders. Real imports, real function calls, real payloads targeting your specific code. Matches your project's test style.
Generated tests are run against your current code. If the test passes, it's useless and gets regenerated. A security test that doesn't catch the vulnerability is a lie.
Stryker mutates your code and checks if the test catches it. 100% mutation kill rate on Pinata's own test suite. The only honest metric for test quality.
Add to your GitHub workflow. Block PRs with vulnerabilities. Upload SARIF to GitHub Security.
One command. Zero config. Find out what your AI left behind.
Add --verify for AI verification. Prompts for API key on first run.