Smart Contract Vulnerabilities You Can’t Afford to Ignore

Smart contracts have become an integral component of blockchain ecosystems, supporting automated transactions and ensuring the security of the decentralized system. As smart contracts deployed on the blockchain are irreversible, even minor Smart Contract Vulnerabilities can result in major financial loss for the organization. Thus, smart contract security has become more critical than ever. A compromised smart contract results in malicious transactions that cannot be reversed.

While the project manager and their subordinate create a detailed document highlighting coding guidelines, strategies to prevent smart contract security flaws, and solutions to handle risk, there can still be gaps during smart contract development, their deployment, and execution.

This article aims to highlight the key smart contract vulnerabilities and guidelines to ensure the secure deployment of smart contracts on the blockchain. It covers real-world examples to highlight the significance of effective smart contracts development and secure execution.

Smart Contracts Development- Key Challenges & Practical Solutions

Smart contract development requires the involvement of multiple parties, such as stakeholders, software developers, and lawyers, who define the rules and obligations that give the contracts. The whole process is iterative and involves multiple rounds of negotiations. 

After the final agreement, the initial contractual agreement is drafted. Developers convert these agreements into code known as smart contracts. It undergoes three stages–

• Design: Define Logics 
• Implementation: Write clean code
• Validation: Test the code 

It is the fundamental steps that help determine the success of smart contracts. Hence, it is important to consider the following vulnerabilities associated with smart contract creation. 

Readability

Smart contracts are written in different languages, such as Solidity, Go, Kotlin, and Java, throughout their development. These codes are later compiled before final execution. As a result, the same contracts may exist in various forms over their lifecycle. Thus, it is challenging to ensure that the program is readable and understandable in every format– be it source code, bytecode, or compiled output.  

You can overcome readability challenges with:

• Recover Source Code: It is a reverse engineering approach to analyze the compiled smart contracts. The engineering tool is used to convert hex-encoded contracts into human-readable pseudo code.
• Human- Readable Code: A semi-automated translation system converts human-readable contract representations into computational programs.

Human–Readable Execution

Smart contracts are written in high-level languages such as Solidity. However, once they are compiled, they are converted into low-level bytecode that runs on the Ethereum Virtual Machine (EVM). As these bytecodes are non-readable by humans, developers may struggle to interpret the operations the smart contract executes.

To address the above problem, Intermediate- Level Language or IELE was proposed. Intermediate- Level Language has syntax similar to a low-level programming framework that makes smart contract execution easier to verify. It provides the compiler with information such as–

• run time
• compile time
• link time
• idle time

This enables easier analysis and audit of smart contracts.  

Functional Issues

There are several functional issues associated with smart contracts. Let’s discuss them one by one.

Reentrancy

In smart contract development, one contract can be called an external contract. Reentrancy risk occurs when the target contract is called recursively by an external contract before its state is updated after the initial execution. This results in an inconsistent state.  

Let’s understand it with the help of an example-

Suppose Account A has $1000. The system calls transfer( ), and the vulnerable smart contracts transfer the entire amount without updating the balance.

The victim’s smart contracts still think that there is $1000 in the account to be sent, and it sends another set of $1000.

Now, if the external smart contract is malicious, it will once again call transfer( ) immediately before the account balance is updated to 0. The loop continues until the account is completely drained.

Block Randomness

In blockchain, it is difficult to achieve randomness, as all nodes must agree on the same value using a consensus mechanism. In blockchain, this random value includes the block timestamp or blockhash, which can be predicted. The attacker can take advantage of this vulnerability to manipulate the outcome.

Let’s take an example to understand this–

Smart contracts can be used in lotteries, loyalty programs, rewards, or betting systems to determine random winners.  Smart contract developers try to generate this random number using values such as block timestamp or nonce. The problem with this situation is that any malicious miner can modify the timestamp and even choose to withhold a block to compromise the pseudo-random number. This can result in a favorable outcome to announce the winner.

Computational Cost 

While ensuring the efficiency of smart contracts, developers must consider one of the essential parameters for an organization, which is the execution costs. Most smart contract developers suffer due to gas fees on the blockchain.

One way to solve it is to use an effective tool to detect gas-intensive code patterns and automatically replace under-optimized bytecode with more efficient versions.

Deploy secure smart contracts to automate business process transitions and enhance trust and transparency within the organization. With more than a decade of experience in smart contracts deployment, the team of PrimaFelicitas can help you build a secure digital solution. Contact us today!

Smart Contracts Deployment- Common Challenges and How to Resolve Them

Before the smart contracts are deployed on the blockchain, they must be carefully checked to detect any bugs in the code. The developers must also check for its interaction with external sources to detect anomalies or any malicious behaviors. Let’s discuss some of the vulnerabilities associated with smart contract deployment and its solutions. 

Smart Contract Correctness

Once smart contracts are deployed on the blockchain, they cannot be revised even if any errors are detected at later stages of execution. Hence, it is crucial to verify it thoroughly to ensure its correctness before the final deployment on the blockchain. 

The following approaches are proposed to ensure smart contract correctness.

Byte Code Analysis: It involves analyzing the bytecodes to detect smart contract security threats. For this, advanced tools are used to detect security bugs in smart contracts. These tools help identify–

• mishandled exceptions
• re-entrancy 
• timestamp dependence
• transaction-ordering problems

Rule-Based Bytecode Analysis: It is another method that generates rule-based patterns to detect vulnerabilities at a higher level. It helps automate the identification of common bug patterns within the bytecode.

Token Sequence Analysis: Researchers proposed a platform to analyse smart contracts by identifying the irregular bytecode token sequences. The process helps detect unusual or suspicious byte patterns.

Opcode-Level Analysis: Researchers proposed Long Short-Term Memory (LSTM) networks, a deep learning model to analyze opcode sequences. It helps detect smart contracts security threat based on the order and combination of opcodes in the smart contracts. 

Dynamic Control Flow

The way smart contracts interact with each other must be designed carefully when developing the contracts. While smart contracts are immutable, determining their behavior at runtime can be challenging as —

• The sequence of execution of tasks or the dynamic control flow can vary depending on the response of the external contracts.
• As the number of contracts that interact with each other on the network increases, it becomes quite challenging to predict how contracts will behave in different scenarios.
• A malicious external contract may behave unpredictably, subsequently compromising the entire network.

Most tools integrated with the system focus on detecting errors in code. What is missing is determining if the execution time is reliable or not. But it is equally important to analyze the execution environment to ensure that it is secure, consistent, and trustworthy. Here are some of the approaches–

Graph-Based Analysis

It is an analysis approach to predict how smart contracts interact with each other. In this technique, the interaction between contracts is represented in the form of a graph where nodes represent contracts, and their edges represent calls or the transfer of information. Now, the researchers use advanced mathematical tools to analyze the graph to determine specific patterns and predict how contracts will interact in the future. This approach helps identify risks and unexpected results before they occur.

Path-searching

This approach is used to detect smart contract vulnerabilities after a long sequence of invocations. Depth-first search (DFS) algorithms determine all possible execution paths to determine if any path violates any smart contract properties. This approach identifies specific issues, such as determining which contracts permanently lock funds, transfer funds to unauthorized users, and more.

Execution Environment: 

It is a technique to determine smart contract vulnerabilities in the execution environment, such as the  Ethereum Virtual Machine (EVM). In this approach, seed contracts or the automatic sample smart contracts are generated to test how different EVM implementations behave.

The data is collected to compare and analyze discrepancies in the execution results, identify inconsistencies, and detect faults that may not be otherwise visible through standard contract-level analysis. 

Smart Contracts Execution- Risks & Solutions 

Once the smart contracts are deployed, they must be regularly monitored and evaluated to detect the following smart contract vulnerabilities.

Ensure Trustworthy Oracles

Smart contracts need accurate real-world data to function appropriately. For example–

Let’s say, for a customer loyalty programme system, the smart contracts need to release rewards automatically. For this, it must know the result for the same. However, as smart contracts operate in a sandboxed environment, they cannot access the external data directly. For this, they rely on oracles, which act as a trusted source to deliver real-world data to a smart contract. 

However, it is often challenging to determine a trustworthy oracle. Here are some of the methods to ensure the trustworthiness of oracles.

Town Crier (TC)

Town Crier is a system that helps address the above challenge by providing an authentic data feed to smart contracts. TC fetches data from reliable websites and feeds it to smart contracts. The data is released in the form of a datagram, which includes the data source and the specific timestamp. All operations by TC are performed within a secure and tamper-resistant execution environment called the Software Guard Extension (SGX) enclave. This helps ensure integrity and protect against malicious interference.  

Decentralized Oracles

Decentralized oracles are based on a voting mechanism among stakeholders and are an incentive-based approach.  In this model, the voters act as decentralized oracle nodes who contribute data. 

The eligible participants stake a certain amount of tokens to cast a vote based on a randomly selected proposition. If the voting aligns with the weighted majority of votes, it receives the reward; otherwise penalized. This approach encourages honest participation. 

Another approach is to use multiple Oracle providers to ensure fairness and reliability. A reliable smart contract keeps a record of every oracle provider. Based on the accuracy of past responses, consistency, timeliness, and behavior, the higher reputation oracles are used, whereas the dishonest oracles are penalized.

Transaction Ordering Vulnerability

Users send transactions that trigger functions in a smart contract. Miners group these transactions into blocks. However, the exact ordering of transactions on the blockchain is not guaranteed, as two miners may simultaneously solve a block and broadcast it on the network. Let’s understand this with the help of an example–

Suppose a smart contract has variable X. User A sends a transaction to raise its value by 1. Another user sends another translation to multiply 10 by x. Depending on the sequence, the value of X can be either X+ 1 or X × 10.

To resolve the simultaneous updates issue, the following approaches may be useful–

Sequential Execution

A smart contract-transaction counter feature was proposed to record the transition number as a parameter that determines the number of transactions in each function. Its value increases after each transaction. Analyzing the transition number helps solve the inconsistency problem.  

Predefining Contracts

To avoid transaction ordering vulnerability caused by unpredictable transaction ordering, researchers propose to shift the logic from individual transactions to smart contracts. In this approach, instead of relying on the order in which transactions occur, smart contracts enforce the correct sequence.

Let’s take an example–

Suppose Miner A wants the operation X+ 1 to occur only after User B’s operation X × 10. To ensure this, a special function, say, AddIfMultiplied ( ), will be called. This function checks if multiplication is done before performing addition. This approach ensures that the activities are executed in the desired sequence, irrespective of how miners order the transactions.

Execution Efficiency

Miners on the blockchain execute the smart contracts serially, i.e., a smart contract cannot be executed until the current contract is completed. However, the serial execution of smart contracts slows down the system. 

Concurrent transactions of smart contracts can help improve performance, but it is quite difficult to implement as smart contracts read or write shared data. What if two contracts try to edit data simultaneously? It can result in conflicts and inconsistent results. 

Another challenge is to access and inspect the internal data of smart contracts. What if the developer adds new functions to the code that expose data? They will have to redeploy the smart contracts, which is impractical as well as costly. 

There are few ways in which we can resolve this: 

Execution Serialization

The Software Transactional Memory (STM) approach treats each contract execution as atomic and speculative. Miners execute multiple contract calls in parallel. If two executions try to modify the same data or cause a conflict, the system simply rolls back and retries safely. This approach ensures that multiple transactions run at the same time while maintaining accuracy.

Inspection of Contracts 

One of the challenges with developers is when they are asked to add conditions that are not mentioned in their initial requirements. Once deployed, the smart contracts cannot be modified. One approach is to redeploy the smart contracts, which is a costly affair. 

To avoid redeployment, researchers proposed the following techniques-

Memory Layout Reification: This method analyzes the compiled smart contracts’ binary structure and reconstructs the memory usage for data storage. This enables developers to determine the internal state of the contract. 

Mirrors for Introspection: The mirrors approach–

• Examines the current state of the deployed contract
• Interprets stored values
• Accesses internal structures 

The above approach enables developers to inspect internal data using techniques that decode or reconstruct the memory layout of smart contracts.  

Secure your business environment with a blockchain solution. Connect with blockchain experts at Primafelicitas to discuss business-specific smart contracts development.

Smart Contracts Completion- Challenges & Solutions

Once smart contracts come into use, their state keeps updating based on their usage. These updated states are stored on blockchains, which also include the transfer of digital assets. As a sequence of transactions is executed and stored on the blockchain, it is important to determine the authenticity, smart contract security, and privacy of data. Let’s discuss some of the challenges in this stage of the  smart contracts lifecycle and its solutions–

Privacy and Security

Most Smart contracts on blockchain lack privacy-preserving mechanisms to ensure the privacy of all transactions. All transactions, be it a sequence of operations, financial exchanges, or asset movements, are broadcast on the public network. While the identity of users is not revealed by using a cryptographic public key, other data, such as transaction history, balances, and activity patterns, remains publicly visible.

In fact, even the smart contract code may contain software vulnerabilities that the attackers can exploit. To overcome these challenges, the following approaches are proposed.

Decentralized Smart Contract System

Hawk is a system design that automatically converts smart contracts into a cryptographic protocol to ensure users’ privacy. Hers is how it works–

Hawk’s compiler splits the compiler into parts–private and public. 

Private: sensitive computation is done confidentially. For example, it encrypts transaction details to ensure that the financial transactions are not revealed.

Hawk encrypts transaction details such as balances and inputs so they cannot be viewed publicly.

Public: Uses zero-knowledge proofs (ZKPs) to protect users’ data and ensure that the system functions correctly without revealing the actual data.

Scams

Blockchain and smart contracts are evolving technologies; hence, they are vulnerable to malicious attacks through scams. Detecting smart contract scams is crucial for organizations to avoid termination of investment in major blockchain projects and bear unwanted losses. 

Ponzi Scheme on Ethereum

A Ponzi scheme is a financial fraud in which the early investors are paid using the money collected from later investors. The scammer promises high returns with almost no risk. However, if the investments slow down, they fail to pay the existing investors, and the scheme collapses. 

According to an analysis of over 16 million Ethereum transactions from July 2015 to May 2017, over 17,777 transactions were linked to Ponzi schemes. The scammers collected over  $410,000 in two years.

The following techniques were proposed to detect Ponzi schemes–

Account Features- Behavior Patterns

This approach determines how accounts behave on the blockchain. It includes–

• Frequency of sending and receiving funds
• Determining the users who deposit small amounts at frequent intervals
• If the withdrawals go to inactive participants on the network 
• Time taken for fund transactions 
• Ratio of incoming and outgoing transactions

This approach helps detect suspicious financial transactions and address the issue 

Opcode Features- Contract Execution Instructions

Smart contracts are compiled into low-level instructions called Opcodes. It defines how the contract behaves at runtime.

Researchers proposed an opcode to analyze common structural patterns in Ponzi schemes, such as–

• Logic that enables money distribution
• Functions that release payouts to old users from the existing new users’ accounts
• Detect codes that do not perform any real business logic
• Repeated operations that continue to transfer funds 

Conclusion 

In recent years, the adoption of smart contracts across industries has increased. With its wide adoption, the researchers are focusing more on smart contract vulnerabilities and solutions to overcome smart contract security.

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