# How Replit Secures AI-Generated Code [white paper]

AI security scans alone aren't enough. New research shows hybrid approaches combining static analysis with LLM reasoning are essential for vibe coding.

**Featured image unavailable: Cover artwork for the article “How Replit Secures AI-Generated Code \[white paper\]”.**

[Open original](https://replit.com/api/storage/public-objects/article-media/securing-ai-generated-code/sanity-813313dd805f5a309ac8bae51630466424143638.a958d0804680b2bd.webp)

- Published: 2026-01-15T01:00:00.000Z
- Authors: Dawei Feng
- Canonical: https://replit-engineering-blog.pages.dev/securing-ai-generated-code/

---

**AI-generated code is changing how software is built, but securing that code raises new challenges.** This research explores whether **AI-driven security scans are sufficient for vibe coding platforms**, or whether they risk asking models to audit their own output.

Through controlled experiments on React applications with realistic vulnerability variants, we compare **AI-only security scans** with Replit’s **hybrid approaches** that combine deterministic static analysis and dependency scanning with LLM-based reasoning. Along the way, we examine how prompt sensitivity, nondeterminism, and ecosystem awareness affect real-world security outcomes.

We show that **functionally equivalent code can receive different security assessments** depending on syntactic form or prompt phrasing. Issues like hardcoded secrets may be detected in one representation and missed in another. More critically, **dependency-level vulnerabilities and supply-chain risks remain largely invisible** without traditional scanning infrastructure.

The takeaway is not that LLMs are ineffective, but that they are **best used alongside deterministic tools**. While LLMs can reason about business logic and intent-level issues, static analysis and dependency scanning are essential for establishing a reliable security baseline.

**Key Findings:**

- **AI-only security scans are nondeterministic**: Identical vulnerabilities receive different classifications based on minor syntactic changes or variable naming

- **Prompt sensitivity limits coverage**: Detection depends on what security issues are explicitly mentioned, shifting responsibility from tool to user

- **Dependency vulnerabilities go undetected**: Without continuous vulnerability feeds, AI cannot reliably identify version-specific CVEs

- **Static analysis provides consistency**: Rule-based scanners deliver deterministic, repeatable detection across all code variations

- **Hybrid architecture is essential**: Combine deterministic baseline security with LLM-powered reasoning for comprehensive protection

If you’re interested in the methodology, experiments, and detailed analysis behind these findings, **[read the full white paper](https://securing-ai-generated-code.replit.app/)**.