Vibe_Coding_Vulnerabilities_Report
01 // IDENTIFYING_GHOST_LOGIC_IN_AI_CODE
In the realm of vibe coding security, the most elusive threat is Ghost Logic. Because AI models are trained to be helpful, they often over-deliver. When you ask an AI to "create a middleware for logging," it might proactively add a feature to redact certain headers—but if that redaction logic is incomplete, it might inadvertently log plaintext passwords while the developer assumes safe passage.
02 // SHADOW_APIS_AND_UNDOCUMENTED_ROUTES
AI-first tools like Lovable or Replit often scaffold entire backend architectures from a single prompt. This frequently results in Shadow APIs—endpoints that the developer didn't realize were created. For example, a prompt for a "blog system" might generate an \`/api/debug/posts\` route that returns full database objects, including draft content and internal IDs, with no authentication.
03 // THE_CONVENIENCE_OVER_SECURITY_BIAS
AI models prioritize "making it work" over "making it secure." If a security policy (like CORS or CSP) interferes with a request during a "vibe coding session," the AI may suggest loosening the policy (e.g., using \`Access-Control-Allow-Origin: *\`) as a fix, rather than implementing a secure whitelist.
04 // CASE_STUDY: THE_VIBE_CODED_LEAK
A FinTech startup built their customer portal using Cursor in under a week. The AI generated a custom hooks system for data fetching but included a 'refresh' token logic in the client-side localStorage without encryption. This was discovered during a SentinelAIGuard audit, revealing that any XSS vulnerability would lead to full account takeover.
05 // SYSTEMATIC_DETECTION_PROTOCOLS
Detecting ai coding flaws requires a dual-track strategy:
- > STAGE_1: Source-to-Sink Analysis of all AI-generated routes.
- > STAGE_2: Automated Dynamic Analysis (DAST) to find the "Ghost Endpoints".
- > STAGE_3: Rigid policy enforcement using SentinelAIGuard PRO.