Introduction
Ethereum, launched in 2015, significantly advanced blockchain technology by introducing the Ethereum Virtual Machine (EVM), which enables developers to create complex, Turing-complete smart contracts. While Bitcoin pioneered basic blockchain functionality, Ethereum expanded its potential, becoming a cornerstone for decentralized application (dApp) development. This evolution has led to a surge in smart contract deployment—self-executing agreements that automatically enforce terms based on predefined conditions.
However, the rapid growth and increasing complexity of these contracts have created critical challenges. Manual inspection is no longer feasible given the scale of over 100,000 unique contracts analyzed in this study. High-profile incidents like the 2016 DAO hack—where a vulnerability led to the theft of $50 million worth of Ether—highlight the urgent need for systematic methods to categorize and secure smart contracts.
These challenges underscore a growing gap: without effective classification and vulnerability analysis, it becomes difficult to identify risky or malicious contracts, assess ecosystem-wide trends, or guide secure development practices. This study addresses these issues by proposing a data-driven taxonomy of Ethereum smart contracts and analyzing the correlation between application categories and security vulnerabilities.
Our research is guided by three core objectives:
- Develop a comprehensive taxonomy reflecting the current state of the Ethereum ecosystem.
- Track the evolution of smart contract applications over time.
- Identify patterns linking specific contract types to known security risks.
👉 Discover how blockchain analytics can uncover hidden vulnerabilities in real-world smart contracts
Methodology: A Data-Driven Approach to Smart Contract Classification
To build an accurate and scalable classification system, we integrated data from three major repositories: SmartBugs, SmartCorpus, and SmartSanctuary. After removing duplicates, our final dataset comprised 100,040 verified Ethereum smart contracts, offering one of the largest samples used in such studies to date.
We employed Latent Dirichlet Allocation (LDA)—a powerful topic modeling technique—to automatically categorize contracts based on their source code and developer comments. Unlike traditional rule-based classification, LDA identifies latent themes in text data by analyzing word usage patterns across documents. In our case, it revealed functional categories embedded within Solidity code.
To enhance accuracy, we used seeded LDA, incorporating domain-specific keywords (e.g., “auction,” “token,” “lock”) to guide the model toward meaningful topics. This hybrid approach combined unsupervised learning with expert knowledge, ensuring both flexibility and relevance.
Before modeling, we preprocessed the code by:
- Tokenizing Solidity syntax
- Handling naming conventions (camelCase, snake_case)
- Removing non-informative programming keywords
- Applying stemming to group similar terms
- Using TF-IDF vectorization to prioritize significant terms
The optimal number of topics was determined using topic coherence scores, balancing granularity with interpretability. After iterative refinement and manual validation by three researchers (achieving a Cohen’s kappa score of 0.76), we finalized 11 distinct smart contract categories.
Smart Contract Taxonomy: 11 Key Application Categories
Based on our analysis, we identified the following primary categories:
- Bank: Contracts allowing users to deposit and withdraw Ether at any time.
- Bid: Auctions, ICOs (Initial Coin Offerings), and crowdsales for fundraising or asset sales.
- Certification and NFT (CNFT): Digital ownership verification and non-fungible tokens.
- Chain Management (CM): Tools simplifying blockchain interactions.
- Ether Lock / Time Constraints (ELTC): Time-bound deposits where funds are locked until a specified date.
- Gambling: Games of chance relying on randomness.
- Game: Skill-based or role-playing games using blockchain assets.
- Money Investment (MI): Financial schemes including legitimate investments and Ponzi scams.
- Token: Contracts implementing fungible tokens with operations like minting and burning.
- Wallet: Digital wallets managing transactions and balances.
- Unknown: Contracts with unclear or minimal functionality (e.g., “Hello World”).
These categories were further grouped into six macro-categories:
- Financial (Bank, Bid, ELTC, MI)
- Token
- Game (Gambling, Game)
- Notary (CNFT)
- Blockchain Interaction (Wallet, CM)
- Unknown
Our findings show that Token and Certification and NFT are the most prevalent application domains—reflecting the rise of tokenization and digital collectibles since 2017.
👉 Learn how advanced analytics can help detect risky smart contract patterns before deployment
Evolution of Smart Contract Applications Over Time
Tracking deployment trends from 2017 to 2024 reveals key shifts in developer focus:
- Token contracts show consistent growth, underpinning DeFi platforms, governance systems, and utility tokens.
- A sharp rise in NFT-related contracts began after the launch of CryptoKitties in late 2017, peaking in 2021 during the NFT boom.
- Early interest in gambling and gaming dApps has evolved into more complex ecosystems combining finance and entertainment.
- The Financial category—including ICOs and investment schemes—saw spikes during bull markets but remains a dominant use case.
This temporal analysis illustrates how Ethereum has matured from experimental financial tools into a diverse ecosystem supporting digital art, identity verification, decentralized finance, and more.
Vulnerability Analysis: Linking Contract Types to Security Risks
We analyzed vulnerabilities using the Osiris tool, detecting eight common weaknesses across our dataset:
- Time Manipulation (TM) – Exploiting block timestamp dependencies
- Arithmetic Overflow/Underflow (A) – Integer math errors
- Bad Randomness (BR) – Predictable pseudo-random number generation
- Unchecked Low-Level Calls (ULLC) – Unverified external calls
- Access Control (AC) – Weak authorization checks
- Denial of Service (DoS) – Gas exhaustion attacks
- Concurrency (C) – Transaction ordering exploits (e.g., front-running)
- Reentrancy (R) – Recursive external calls before state updates
Despite limitations in tool compatibility with newer Solidity versions, we identified vulnerabilities in 3,114 smart contracts.
A chi-square test confirmed a statistically significant association between contract categories and vulnerability types (χ² = 131.54, p < 0.001), rejecting the null hypothesis of independence.
Key Correlations Between Categories and Vulnerabilities
| Category | Most Common Vulnerability | Explanation |
|---|---|---|
| Gambling | Bad Randomness | Reliance on predictable on-chain randomness |
| Unchecked Low-Level Calls | Complex payout logic using unsafe call() functions | |
| CNFT | Concurrency | High-value transactions vulnerable to front-running |
| ELTC | Reentrancy | Withdrawal functions susceptible to recursive calls |
| Bid / ICO | Access Control | Misconfigured ownership roles in fundraising contracts |
For example:
- Gambling contracts contributed 25.15% of all Bad Randomness cases due to reliance on block hashes or timestamps.
- ELTC contracts showed strong ties to Reentrancy risks—mirroring the logic exploited in the DAO attack.
- CNFT projects faced high Concurrency risks during high-demand mints where transaction order affects outcomes.
These findings demonstrate that certain application domains inherently carry higher risks due to their design patterns and operational logic.
Frequently Asked Questions
What is a smart contract taxonomy?
A smart contract taxonomy is a structured classification system that organizes contracts based on their functionality, such as tokens, auctions, or identity verification. It helps developers, auditors, and researchers understand ecosystem trends and security profiles.
Why are NFT contracts prone to concurrency attacks?
NFT minting often involves limited-edition drops with high demand. Attackers exploit transaction ordering (front-running) to secure rare items before others—a risk amplified when gas fees determine inclusion speed.
How does seeded LDA improve classification accuracy?
Seeded LDA uses expert-defined keywords to guide topic discovery. This ensures the model identifies meaningful categories (like "gambling" or "token") rather than abstract clusters, improving relevance and interpretability.
Can developers use this taxonomy in practice?
Yes. Knowing their contract’s category allows developers to proactively address known vulnerabilities. For instance, a gambling dApp team should prioritize secure randomness solutions and audit low-level call usage.
What tools can detect these vulnerabilities?
Tools like Osiris, Slither, and Mythril automate vulnerability detection. However, they work best when combined with contextual knowledge—such as expected risk patterns per category—to reduce false positives.
Is reentrancy still a major threat today?
Absolutely. Despite increased awareness since the DAO hack, reentrancy remains common—especially in finance-related contracts handling fund withdrawals without proper state updates.
Discussion: Implications for Developers and Auditors
Our taxonomy reveals that smart contract risks are not randomly distributed—they cluster around specific application types due to shared design patterns.
For example:
- Financial contracts often reuse similar withdrawal logic—making them collectively vulnerable to reentrancy if not properly secured.
- NFT platforms frequently implement batch minting functions that consume variable gas—increasing DoS risks.
- Gambling apps relying on block variables for randomness expose predictable outcomes.
This means developers can adopt proactive security strategies tailored to their contract’s category. Instead of generic audits, teams can focus on known weak points—for example:
- Using Chainlink VRF for randomness in gambling dApps
- Implementing reentrancy guards in time-lock contracts
- Adopting commit-reveal schemes to prevent front-running in NFT sales
Furthermore, auditors can use this framework to benchmark risk levels across projects and prioritize testing efforts accordingly.
Limitations and Future Work
While our approach offers valuable insights, several limitations exist:
- LDA depends on descriptive naming; poorly documented contracts may be misclassified.
- Tool compatibility issues limited vulnerability detection coverage.
- Findings are specific to Ethereum and may not generalize to other blockchains.
Future research could:
- Integrate static and dynamic analysis for deeper code inspection
- Expand the taxonomy to include emerging categories like AI-driven dApps
- Develop IDE plugins that warn developers about category-specific risks during coding
Conclusion
This study presents a comprehensive taxonomy of Ethereum smart contracts derived from over 100,000 real-world examples. By linking application categories to security vulnerabilities, we reveal predictable risk patterns that empower developers to build safer systems.
Key takeaways:
- Smart contract risks are category-dependent, not random.
- NFTs and gambling apps face unique threats tied to transaction timing and randomness.
- Financial contracts remain highly exposed to reentrancy and access control flaws.
- Proactive classification can guide secure development long before deployment.
As Ethereum continues to evolve, such data-driven frameworks will be essential for maintaining trust, security, and innovation across the decentralized ecosystem.
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