Full Report
New research reveals four common security risks systematically affecting vibe-coded applications - with remediation strategies curated together with Lovable.
Analysis Summary
# Research: Wiz Research Finds Risks in 20% of Vibe-Coded Apps
## Metadata
- Authors: Gal Nagli, Alon Schindel
- Institution: Wiz
- Publication: Wiz Blog
- Date: September 18, 2025
## Abstract
Wiz Research investigated the security posture of applications generated via "vibe coding"—the practice of creating applications using natural language prompts on specialized platforms (e.g., Lovable). The research found systemic security risks resulting from common misconfigurations in these rapidly deployed applications. Specifically, one in five organizations building on these platforms were inadvertently exposed to risk due to insecure authentication practices inherited from the generation process. The study details these common pitfalls and provides actionable remediation to secure these applications.
## Research Objective
To identify and analyze emerging security risks associated with applications rapidly developed and deployed using natural language prompt-based, or "vibe-coded," development platforms by organizations building internal tools and solutions.
## Methodology
### Approach
The research involved an analysis of the architecture and code structure of applications generated by vibe-coding platforms, focusing on identifying common patterns related to security-critical components like authentication.
### Dataset/Environment
The analysis focused on applications deployed by organizations utilizing leading vibe-coding platforms, particularly those handling sensitive corporate data or used for critical internal tasks (e.g., chatbots, automations). The scale indicates that **1 in 5 organizations** were found to be utilizing applications exhibiting systemic risks.
### Tools & Technologies
The study relied on Wiz's capability to analyze cloud environments and application codebases to identify misconfigurations and insecure coding patterns inherent in the vibe-coded outputs.
## Key Findings
### Primary Results
1. **Widespread Adoption:** A significant portion (one in five) of organizations are building solutions utilizing platforms that enable vibe coding.
2. **Systemic Authentication Flaws:** A primary security risk observed is the implementation of authentication logic entirely on the client side (in the browser).
3. **Exposed Secrets:** When authentication is client-side, necessary secrets (like passwords) are embedded directly within the downloaded JavaScript files, accessible upon source code inspection.
### Supporting Evidence
- Examples provided explicitly show hardcoded secrets (e.g., `"welcometoredacted"`) or secrets stored in local variables within client-side code blocks.
- Session management often relies simply on setting a predictable flag in `LocalStorage` (e.g., `"authenticated"`), which an attacker can manually set after reading the expected value from the code.
### Novel Contributions
- Identification and quantification of security risks specifically tied to the emerging "vibe coding" paradigm.
- Documentation of concrete, common insecure coding patterns resulting from automated, prompt-driven application generation, moving beyond traditional developer error analysis.
## Technical Details
The flawed authentication mechanism involves:
1. **Client-Side Validation:** The application relies solely on front-end JavaScript to check user credentials against a hardcoded value or variable defined locally within the client bundle.
2. **Secret Exposure:** Attackers can easily access these secrets by inspecting the application's source code in browser developer tools.
3. **Session Hijacking:** Authentication bypass is simplified by manually replicating the expected state in browser `LocalStorage` (e.g., setting a specific token or flag), circumventing the login form entirely.
**Solution Focus:** The core technical remediation requires enforcing robust, stateful **Server-Side Authentication**, ensuring all validation and session establishment occur exclusively on a secure backend resource.
## Practical Implications
### For Security Practitioners
Security teams must recognize that assets built using low-code/no-code or vibe-coding tools carry inherent, predictable risks that differ from traditionally developed applications. Visibility into code structure, even for automated builds, is critical.
### For Defenders
Actionable step: Review authentication flows for all shadow IT or internally developed applications, especially those flagged as being generated via LLM/AI code assistants. Mandate that all authorization checks must occur server-side, never relying on client-side logic for validation or session persistence.
### For Researchers
This highlights a new category of risk vector related to the democratization of development via LLMs. Future research should focus on developing automated scanners capable of identifying inherent trust assumptions (like client-side secret storage) within these novel application generation pipelines.
## Limitations
The summary primarily focuses on authentication flaws due to the provided excerpt. The full research likely covers other systemic risks mentioned in the overview (four common risks identified in total). The study is less focused on the *generative model* itself and more on the *output structure* prevalent in these platforms.
## Comparison to Prior Work
This work addresses risks emerging from the contemporary trend of "vibe coding," differentiating it from traditional insecure coding practices (like hardcoding secrets in traditional source control). It analyzes risks embedded in accessibility-driven development paradigms fostered by platforms like Lovable, providing specific guidance tailored to this environment.
## Real-world Applications
- Organizations using vibe-coding platforms for generating mission-critical or data-handling internal applications must prioritize immediate security auditing.
- Platform providers (like Lovable) gain insight into necessary security guardrails and default configurations to prevent systemic risk generation.
## Future Work
1. Comprehensive analysis of the other three prevalent security risks found in vibe-coded applications.
2. Developing platform-specific security policies and scanning rules optimized for compiled JavaScript artifacts from these generative environments.
## References
- [Lovable Documentation/Platform Reference (Inferred from context)]
- [Wiz Research on Emerging Cloud and AI Risks (General Tag)]