VIbeGuard

What if the code you ship in minutes can expose your users for years? AI-assisted development has made building software dramatically faster. Tools such as Cursor, Claude Code, Lovable, Replit, and Bolt can turn an idea into a working application in hours. But security hasn't kept pace. Developers can quickly generate thousands of lines of functional code without having the time or expertise to understand its security posture. VibeGuard closes this gap by combining the strengths of traditional security scanners with AI's contextual reasoning. Traditional SAST and DAST tools are good at detecting suspicious patterns and runtime behavior, but their findings often require significant manual investigation. On the other hand, simply asking an LLM to scan an application can produce convincing but unreliable conclusions because the model may misunderstand context or treat suspicious code as proof of a vulnerability. VibeGuard takes a different approach: deterministic scanners find evidence; an LLM investigates, correlates, explains, and helps determine what that evidence actually means. A project can be analyzed through static code evidence and controlled runtime checks. When a scanner identifies something suspicious, the AI security analyst can inspect relevant files, search the codebase, review dependencies, and examine supported security configurations through a restricted set of read-only tools. It then re-evaluates the finding using the additional context rather than blindly trusting the initial scan. The result is a structured security finding that clearly separates potential, likely, verified, and false-positive issues, while preserving the evidence behind the conclusion. When independent static and runtime evidence support the same issue, VibeGuard can correlate them—for example, connecting a missing ownership check in source code with runtime evidence showing one user accessing another user’s data. This makes VibeGuard useful for developers, security teams, and teams building applications rapidly with AI. Instead of overwhelming them with a list of warnings, it aims to answer the question that actually matters: “What does this evidence mean, and how confident should I be that it represents a real security problem?” What makes VibeGuard unique is its emphasis on evidence over confidence. The LLM is not given unrestricted access or allowed to invent conclusions. Its investigation is bounded by registered tools, scanner evidence is kept separate from AI confidence, and verification requires actual supporting evidence. VibeGuard is designed to make security analysis more contextual, explainable, and trustworthy, helping developers keep the speed of AI-assisted development without leaving security behind.