Find security vulnerabilities across your entire codebase, not just the files that changed this week.
Security code reviews are one of the most important quality gates in software development, yet they remain one of the most difficult to execute well. A thorough manual security audit of a mid-size application can take a specialist days or weeks, and the cost of hiring an external firm for a single engagement routinely exceeds $30,000.
Even experienced security engineers cannot hold an entire codebase in their heads at once. They focus on areas they know to be risky - authentication flows, input handling, database queries - and may miss vulnerabilities in less obvious places like configuration files, build scripts, or utility modules that quietly expose sensitive data.
Traditional static analysis tools help, but they operate on pattern matching. They can flag a SQL string concatenation or a missing CSRF token, but they struggle with the kind of contextual reasoning that security analysis demands. Is this API endpoint properly authorised? Does this error message leak internal implementation details? Is this cryptographic approach still considered secure? These questions require understanding intent, not just syntax.
VibeRails applies frontier large language models to security analysis across the entire codebase. Rather than relying on a fixed set of rules, the AI reads and reasons about each file the way a security engineer would - understanding the purpose of the code, the data it handles, and the trust boundaries it crosses.
The security-relevant detection categories in VibeRails cover the areas that matter most:
Because the AI understands the semantic meaning of the code, it can identify issues that rule-based tools miss entirely. A function that correctly sanitises SQL input but then passes the result to an unsafe shell command, for example, would be caught by the AI's contextual reasoning but might slip past a tool that only checks for SQL injection patterns.
Security analysis benefits from multiple perspectives. VibeRails supports running reviews with two different AI backends - Claude Code and Codex CLI - in sequence. The first pass performs broad discovery, identifying potential security issues across the codebase. The second pass acts as a verification layer, applying a different model architecture to confirm or challenge the findings.
This cross-validation is particularly valuable for security work, where false positives erode trust and false negatives create real risk. When both models independently flag the same issue, confidence is high. When only one model flags something, it warrants closer human attention during triage.
Each finding includes a severity rating (critical, high, medium, low), the specific file and line range, a description of the vulnerability, and a suggested remediation approach. Critical and high severity security findings are surfaced first in the triage workflow, ensuring that the most dangerous issues get immediate attention.
For teams operating under compliance requirements - SOC 2, HIPAA, PCI-DSS, or internal security policies - the ability to demonstrate that a security review was conducted and its findings were addressed is essential. VibeRails provides the data to support this.
Every review session captures the complete set of findings with their categories, severities, and affected code locations. The triage workflow records which findings were accepted for remediation, which were rejected (with the implicit judgement that they are acceptable risks), and which were deferred for future consideration.
Findings can be exported in JSON format for integration with security information and event management (SIEM) tools, issue trackers, or compliance dashboards. The markdown export creates human-readable reports suitable for sharing with auditors, security teams, or management.
VibeRails runs as a desktop app and does not require uploading your repository to a VibeRails-hosted platform. When you run reviews, relevant source code may be transmitted from your machine to the third-party AI provider you configure (for example, via Claude Code or Codex CLI). VibeRails itself does not receive or store your source code.
Identifying vulnerabilities is only half the job. VibeRails closes the loop by enabling fix sessions for accepted security findings. After triaging the results, you can dispatch AI agents to implement the recommended remediations directly in your local repository.
Each fix is generated as a code change you can inspect, test, and commit or discard. For security fixes, this typically means adding input validation, introducing proper auth checks, removing hardcoded secrets, or replacing insecure cryptographic functions. The AI agent works within the context of the existing codebase, so the changes fit the project's existing patterns and conventions.
This workflow - discover, triage, fix, verify - can be repeated on a regular cadence to maintain security posture as the codebase evolves. Each session builds on the last, and the structured history makes it straightforward to demonstrate ongoing security diligence.
Download VibeRails and run a security review across every file today.
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