Gate Research Institute: Is Vibe Coding the cure for efficiency or the poison for safety?

Summary

• Vibe Coding is a programming practice that primarily takes natural language intent as input, with an AI system automatically generating system-level code structures, emphasizing rapid trial-and-error and result usability as the main validation standards.
• Existing research generally indicates that AI programming tools can significantly improve code generation speed, task completion time, and developer subjective satisfaction.
• Vibe Coding weakens the depth of code understanding and validation strength, amplifying security risks in irreversible, asset-bound blockchain systems.
• In high-risk blockchain systems, Vibe Coding is more suitable for prototyping, non-core logic implementation, and experimental development scenarios, rather than directly applying to core contracts controlling high-value assets.
• The development efficiency gains based on Vibe Coding must be combined with stricter security audits, formal verification, and testing mechanisms to compensate for the decline in code understanding depth during development.
• In the highly sensitive technological environment of blockchain, the real issue is not whether to use Vibe Coding, but whether sufficient restraint and governance over risks are maintained while pursuing efficiency.

Introduction

1.1 Background

In recent years, large language models (Large Language Models, LLMs) have rapidly expanded their applications in the field of software engineering, promoting the development of a new programming paradigm driven by natural language code generation. Developers no longer rely solely on line-by-line coding but describe target functions, system behaviors, or design intentions, and AI systems automatically generate executable code. This “feels right” approach, emphasizing rapid feedback and iteration, has gradually been summarized by the industry as Vibe Coding.

Compared with traditional software engineering, Vibe Coding significantly lowers the barrier to programming, accelerates prototyping and feature implementation, and is widely used in startups, individual developers, and rapid experimentation scenarios. However, this paradigm also weakens developers’ comprehensive understanding of underlying implementation details, boundary conditions, and exception paths, leading to ongoing debates about code quality, security, and responsibility.

Blockchain systems, especially decentralized applications (Decentralized Applications, DApps) based on smart contracts, provide a highly tensioned scenario for Vibe Coding. On one hand, blockchain development faces long technical barriers, lengthy development cycles, and costly audits; theoretically, Vibe Coding can significantly improve development efficiency and accelerate innovation. On the other hand, once deployed, blockchain code is difficult to modify, and it often directly controls high-value digital assets, meaning security flaws can cause irreversible economic losses. In this context, any development paradigm that reduces developers’ “code understanding depth” may amplify systemic risks.

Therefore, Vibe Coding exhibits a contradictory characteristic in the blockchain field: it can be a “cure” for development bottlenecks or a “poison” that weakens system security.

1.2 Research Questions

Although research on AI-assisted programming is increasingly rich, existing literature mainly focuses on productivity improvement, developer experience optimization, and general software engineering scenarios, with insufficient attention to its impact in high-risk, irreversible systems. Particularly in the blockchain environment where “code is law,” whether Vibe Coding changes the risk distribution structure remains lacking systematic empirical analysis.

Based on this, this paper explores the following core research questions:

• Efficiency: Does Vibe Coding significantly shorten development cycles, reduce manpower costs, and accelerate project deployment in blockchain application development?
• Security: In smart contracts and blockchain infrastructure, do codes generated and deployed rapidly exhibit higher vulnerability rates, earlier attack windows, or larger economic losses?
• Structural relationship: Is the improvement in development efficiency statistically correlated with increased security risks? Do they form some kind of “efficiency-security trade-off”?
• Engineering and governance implications: Under conditions where Vibe Coding cannot be completely avoided, how should blockchain systems design technology, processes, and institutions to mitigate potential risks?

1.3 Methodology and Data Overview

To answer these questions, this paper adopts a data-driven empirical analysis approach, combining descriptive statistics, control analysis, and correlation analysis to systematically study the impact of Vibe Coding in the blockchain domain.

Specifically, the data sources include:

• Blockchain security incident data: time series data on the number of smart contract vulnerabilities, attack event frequency, and financial losses;
• Open-source code repository data: analyzing smart contract code structure features, commit patterns, and development cycles to construct proxy indicators for Vibe Coding;
• Smart contract audit report data: used to compare vulnerability density and audit pass rates under different development modes;
• Blockchain project development data: used to measure development efficiency, team size, and deployment speed.

Since it is currently impossible to directly observe whether developers use AI programming tools, this paper uses indirect indicators such as code similarity, commit behavior, and development rhythm to approximate practices related to Vibe Coding. It is important to emphasize that this study focuses on statistical correlations and structural trends rather than causal judgments about individual projects or developer behaviors.

Definition of Vibe Coding

2.1 Conceptual Definition of Vibe Coding

With the widespread application of large language models in software engineering, a new development practice driven by natural language code generation has gradually formed. Although “Vibe Coding” is not originally a strict academic term, its features in engineering practice constitute a representative paradigm shift in programming.

This paper defines Vibe Coding as:

A programming practice that takes natural language intent as the main input, with AI systems automatically generating system-level code structures, emphasizing rapid trial-and-error and result usability as the main validation standards.

Under this paradigm, developers no longer need to build step-by-step, rely on formal reasoning, or fully understand code logic. Instead, they iteratively approach target functions through a “generate—run—correct” cycle. The correctness of code is judged more by whether the runtime results meet expectations rather than through systematic validation of implementation details, boundary conditions, and exception paths.

2.2 Differentiation Between Vibe Coding and Related Programming Paradigms

To avoid conceptual confusion, it is necessary to distinguish Vibe Coding from existing software development paradigms.

2.2.1 Difference from AI-assisted programming

Existing research on AI-assisted programming generally assumes developers remain the primary interpreters and controllers of code logic, with AI tools mainly providing code completion, error prompts, or local optimization. In this mode, the overall system structure and key logic are still dominated by developers.

In contrast, in Vibe Coding, AI systems often directly participate in generating system-level code structures, with developers mainly playing roles in validation and correction. This difference leads to significant variations in risk distribution: errors in AI-assisted programming are usually localized, whereas errors in Vibe Coding are more likely to have systemic and chain effects.

2.2.2 Difference from low-code / no-code development

Low-code and no-code platforms reduce programming barriers through graphical components, predefined templates, and strongly constrained development environments, with security and compliance partly guaranteed by platform mechanisms. However, this approach often sacrifices flexibility and scalability.

Vibe Coding does not rely on fixed templates or closed platforms but leverages the generalization ability of language models to generate highly flexible code structures. This feature makes it significantly more capable in functional expression than low-code platforms but also lacks the built-in security constraints and engineering norms of the latter.

2.2.3 Difference from Agile development

Agile emphasizes iteration, feedback, and continuous delivery, assuming the development team has a clear understanding of system architecture and key logic. Vibe Coding further shifts some engineering cognitive burdens to automated generation systems, making iteration speed no longer linearly related to human understanding of system complexity.

Therefore, Vibe Coding is not just a simple extension of Agile but a development practice with a significant change in engineering cognitive structure.

2.3 Engineering Characteristics and Risk Structure of Blockchain Development

Blockchain systems, especially decentralized applications based on smart contracts, have fundamental differences from traditional software systems in engineering attributes.

First, once smart contract code is deployed on the blockchain network, it is usually difficult to modify or revoke. This irreversibility means that any defect may exist long-term and be exposed in adversarial environments.

Second, blockchain code often directly controls digital assets with real economic value. Security vulnerabilities are not only functional errors but can also be actively exploited for economic gain. Studies show that logical errors, permission configuration flaws, and state management issues are primary causes of major security incidents. Moreover, blockchain systems generally operate in highly adversarial environments. Attackers can continuously monitor on-chain states, quickly replicate attack strategies, and automate attacks, making the early deployment phase a period of high risk.

These characteristics collectively form a highly sensitive engineering environment regarding code quality and security, where any development paradigm that reduces code understanding or validation strength may magnify systemic risks.

2.4 Literature Review

Existing research generally indicates that AI programming tools can significantly improve code generation speed, task completion time, and developer subjective satisfaction. These studies support the potential efficiency advantages of Vibe Coding. However, most focus on short-term development tasks or controlled experimental environments, with limited discussion on long-term maintainability and security impacts in complex systems.

Research on blockchain security mainly concentrates on vulnerability classification, attack pattern analysis, and defense mechanisms, providing mature theoretical frameworks for smart contract security. However, there is little focus on how development paradigms themselves influence vulnerability distribution and risk structure, especially lacking systematic empirical studies on AI-driven development practices.

In summary, current research has notable gaps:

• Lack of systematic definition of the emerging programming paradigm Vibe Coding;
• Lack of empirical analysis in high-risk blockchain scenarios;
• Lack of unified quantitative frameworks integrating development efficiency and security risks.

This paper aims to fill these gaps through multi-source data analysis, exploring the structural relationship between efficiency benefits and security risks of Vibe Coding in blockchain development, providing empirical support for engineering practices and governance mechanisms.

Research Methodology

3.1 Design Approach

This paper adopts a quantitative empirical research approach, systematically analyzing the impact of Vibe Coding on development efficiency and potential security risks in blockchain development. Since Vibe Coding as a development practice cannot be directly observed, proxy variables that are quantifiable are constructed to approximate its features, and the statistical relationships between these variables and security risk indicators are examined.

The overall research design follows these steps:

• Construct quantitative indicators reflecting blockchain development efficiency and code generation features;
• Build project—contract-level samples based on multi-source data;
• Use descriptive statistics and control analysis to examine overall trends;
• Conduct correlation analysis to investigate the structural relationship between development efficiency and security risks.

This study focuses on statistical correlations and systemic trends rather than causal inference about specific tools or causal mechanisms.

3.2 Data Sources

3.2.1 Blockchain Security Incident Data

Security incident data are used to measure explicit security risks in blockchain systems, including the occurrence time, attack types, and economic losses of smart contract attack events.

This dataset includes:

• Attack event dates
• Project or contract identifiers
• Vulnerability type classifications
• Economic loss amounts

3.2.2 Open-source Code Repository Data

Selected blockchain projects with public code repositories are analyzed, collecting their smart contract code and commit history. This data is used to characterize development pace, code structure features, and potential signs of automation.

Collected dimensions include:

• Contract lines of code (LOC)
• Cyclomatic complexity
• Code similarity among contracts
• Commit timestamps and sizes

Data Description and Sample Statistics

4.1 Dataset Overview

The dataset combines multiple publicly verifiable sources, covering blockchain security incidents, open-source repositories, smart contract audit reports, and project-level development information. Data are aggregated at the contract level, spanning recent years of rapid blockchain application development.

In constructing the sample, the following principles are followed:

• Only retain data traceable to specific projects or contracts;
• Remove samples with missing key information or unmatched across sources;
• Identify and handle outliers to reduce the influence of extreme events.

Initial samples are from public blockchain projects and their code repositories, covering various application types such as DeFi, NFTs, and DAOs. The initial dataset includes project-level records and contract-level code and commit histories.

4.3 Descriptive Statistical Results

4.3.1 Descriptive Statistics of Development Efficiency Indicators

The table summarizes descriptive statistics of variables related to development efficiency, including development cycle length, commit frequency, and large commit proportions. Overall, the projects show significant heterogeneity in development pace. Some projects have extremely short times from first code submission to mainnet deployment, reflecting highly compressed development processes; others have longer cycles and more dispersed commit patterns.

4.3.2 Descriptive Statistics of Code Structure Indicators

The table presents statistical features of smart contract code structure metrics, including lines of code, cyclomatic complexity, code similarity, and duplicate code proportion. Results show significant differences across projects. Some samples exhibit highly similar contract structures and high duplication, especially in multi-contract projects.

4.3.3 Descriptive Statistics of Security Risk Indicators

The table summarizes descriptive statistics of security risk variables, including attack frequency, economic loss scale, and time to first attack.

Findings include:

• Security incidents are unevenly distributed across samples;
• A small number of attacks cause substantial economic losses;
• Most attacks occur within a short window after project deployment.

In summary, the data show significant heterogeneity in development efficiency, code structure, and security risks. This heterogeneity provides the necessary conditions for analyzing the relationship between Vibe Coding features and security risks.

The descriptive statistics reveal:

• Development pace varies greatly across blockchain projects;
• Code structure features differ markedly among projects;
• Security risks tend to concentrate in certain time and scale dimensions.

Building on this, the next chapter will further analyze the efficiency benefits of Vibe Coding in blockchain development, while Chapter 6 will focus on its potential security risks.

Empirical Analysis of Development Efficiency

Using the development pace and code generation feature indicators constructed in Chapter 3, this section empirically analyzes the development efficiency of blockchain projects. Descriptive statistics show that some projects have significantly shorter development cycles, often from first code submission to mainnet deployment, indicating highly compressed development practices characteristic of automation and rapid iteration in blockchain contexts.

Further analysis of commit behavior reveals that high-efficiency projects tend to have higher commit density and larger single-commit sizes. This pattern suggests that code generation favors centralized output and holistic modifications rather than incremental building. Combining project team size data, it is observed that development cycles shorten without proportional increases in manpower, indicating that efficiency gains are more likely due to tool use and automation rather than team expansion.

From project type distribution, efficiency improvements are not uniform across different blockchain applications. Projects with more standardized functions and clearer business logic tend to adopt more compressed development modes, whereas projects relying heavily on security and system robustness tend to be more cautious. This indicates that high-efficiency practices are somewhat scenario-dependent.

Overall, the analysis confirms that practices related to Vibe Coding can significantly enhance development efficiency in blockchain projects, reflected in shorter development cycles and higher productivity per developer. However, the impact on overall system quality and security remains to be further examined. The next chapter will explore this issue in depth.

Empirical Analysis of Security Risks

Building on the efficiency analysis, this section investigates whether practices associated with Vibe Coding introduce higher security risks in blockchain projects. Security incident occurrence, vulnerability counts, and economic loss scales are used as risk indicators, analyzed against development pace and code structure proxies.

First, projects with shorter development cycles are more prone to security incidents. Compared to longer-cycle projects, high-efficiency projects tend to experience higher attack rates early after deployment. This suggests that rapid deployment in adversarial environments shortens the window for attackers to discover and exploit vulnerabilities.

Second, code structural features correlate with vulnerability counts. Higher code similarity and duplication are associated with more vulnerabilities, indicating that template-based and homogeneous code structures may propagate systemic flaws across multiple contracts.

Third, economic loss analysis shows that higher development efficiency correlates with larger potential losses once security breaches occur. Although not all high-efficiency projects suffer attacks, those that do tend to incur more substantial damages, exhibiting a “low frequency—high impact” risk pattern.

Overall, the findings demonstrate a structural trade-off: while Vibe Coding can improve development speed and reduce costs, it also amplifies security risks, especially in the sensitive blockchain environment. This supports the notion that “efficiency is the antidote, but also the poison” in this context.

Conclusion

This paper systematically analyzed the emerging paradigm of Vibe Coding, examining its efficiency benefits and security risks in blockchain development through multi-source data empirical analysis. The results show that Vibe Coding indeed significantly shortens development cycles and reduces manpower costs, demonstrating clear efficiency advantages. However, this efficiency gain is associated with increased security vulnerabilities, higher likelihood of vulnerabilities, and larger potential economic losses once breaches occur.

The empirical evidence indicates that the practice of automating code generation and rapid iteration, characteristic of Vibe Coding, weakens developers’ comprehensive understanding of system logic and validation, thereby magnifying systemic security risks in irreversible, asset-bound blockchain systems. The risk structure exhibits a “low frequency—high impact” pattern, emphasizing the importance of balancing efficiency with rigorous security measures.

In practice, Vibe Coding is more suitable for prototyping, non-core logic, and experimental scenarios rather than critical contract development controlling high-value assets. To mitigate risks, development processes must incorporate stricter security audits, formal verification, and testing, and organizations should clarify responsibility boundaries for AI-generated code.

The study also highlights that the core issue is not whether to adopt Vibe Coding but whether sufficient risk governance is maintained while pursuing efficiency. Future research could involve direct developer surveys, controlled experiments, and automated security analysis tools to deepen understanding of the risk mechanisms involved.

This research underscores that in the highly sensitive blockchain environment, the key is not just technological adoption but also governance and restraint to prevent efficiency-driven vulnerabilities from becoming systemic threats.

References

• Google, https://cloud.google.com/discover/what-is-vibe-coding?hl=zh-CN
• Apifox, https://apifox.com/apiskills/what-is-vibe-coding/
• Ey, https://www.ey.com/en_cz/services/cybersecurity/solidity-smart-contract-security-review
• Audita, https://audita.io/blog-articles/vibe-coding-smart-contracts-vibe-coding-in-crypto
• Meduim, https://medium.com/thecapital/the-vibe-coding-renaissance-why-crypto-is-the-only-monetisation-model-that-fits-eb57fa57b738
• Blockchain council, https://www.blockchain-council.org/ai/end-of-vibe-coding/
• Tradingview, https://www.tradingview.com/news/cointelegraph:3c30759c9094b:0-ai-vibe-coding-what-it-is-why-it-s-risky-and-how-to-stay-safe/

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