ZeroPath AI-Native SAST

ZeroPath AI-Native SAST is a static application security testing tool that uses artificial intelligence to identify vulnerabilities in application code. The tool performs source-to-sink taint analysis to track untrusted data through applications and detect injection, deserialization, and command execution vulnerabilities. The platform identifies business logic flaws and authentication issues including missing authentication checks, IDOR vulnerabilities, race conditions in payment flows, authorization bypass paths, and weak cryptography. It supports detection of emerging vulnerability classes such as prompt injection and training data exposure in AI applications. ZeroPath combines SAST with software composition analysis to assess exploitability of vulnerable dependencies within the application context. The tool generates vulnerability reports with CVSS 4.0 severity scores and provides remediation guidance with code fix suggestions that can be submitted as pull requests. The platform supports over 15 programming languages including Python, JavaScript, TypeScript, Java, C#, Go, Ruby, PHP, Rust, Swift, Kotlin, Nim, Scala, C, C++, Dart, and Elixir. Users can write custom security rules using natural language policies. Pull request scanning completes in under 60 seconds on average. The tool integrates with version control systems for automated security analysis during development workflows. Detection capabilities update automatically as the underlying AI models evolve, enabling identification of new vulnerability types without manual rule updates.