ai-code-review

To scan for prompt injection risks and PII leakage, run Whitney via the CLI or Python module fallback against your codebase. The scan identifies AI security issues, returning severity-grouped findings with CWE and OWASP mappings for actionable remediation planning.

The best way to review code for hardcoded credentials and unguarded agent behavior is to analyze your repository using an AI security scanner like Whitney. It generates structured findings categorized by criticality, providing plain-English explanations of risk impact and concrete remediation steps.

Yes, you can use this AI security review tool if Whitney is not installed because it provides a Python module fallback path. This ensures you can still execute end-to-end scans across your codebase to detect AI security vulnerabilities without needing the Whitney CLI.

The code review tool maps AI security findings to standard vulnerability frameworks by categorizing detected issues like prompt injection and hardcoded keys using CWE and OWASP mappings. This structured output groups findings by severity to prioritize your remediation workflow.

Yes, the Whitney scanner supports analyzing both single repositories and entire directories for AI risks. You can run it using the command line or Python fallback to perform deep security scans and return severity-grouped findings for your remediation planning.