5 Smart Contract Vulnerabilities You Need to Know About: Protect Your Funds and Assets with These Tips

Paulina Lewandowska

23 Dec 2022
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In smart contracts, the details of the agreement between the buyer and seller are directly encoded into lines of code. These contracts self-execute. On a blockchain network, the code and the agreements it contains are copied and saved.

We have compiled a list of typical smart contract flaws that users may encounter and methods that may be taken to safeguard them as experts in building smart contracts.

Reentrancy attacks

These exploits give an adversary the ability to repeatedly run a smart contract function and siphon off its cash.

In a reentrancy attack, a malicious contract calling a vulnerable contract is created by the attacker, who then waits for the vulnerable contract to execute a function that transfers money to their contract. Before the susceptible contract has an opportunity to change its internal state, the attacker's contract calls the vulnerable contract once more right away. The attacker can drain the cash from the susceptible contract by doing this procedure repeatedly.

Reentrancy attacks are particularly harmful since they can be carried out covertly over a long period of time and are frequently challenging to identify. They can also be challenging to stop since they frequently rely on flaws in the vulnerable contract's architecture.

Smart contract developers should put protective measures in place to guard against reentrancy attacks, such as employing mutexes (locking mechanisms) to prevent repeated calls to a contract's functions and thoroughly examining the contract's code for any potential vulnerabilities.

Unchecked send

This flaw enables an attacker to transmit a lot of tokens to a smart contract, thereby exhausting its resources and leading to failure.

An attacker uses a malicious contract to transmit a large number of tokens to a weak contract in a single transaction in an unchecked send attack. The susceptible contract might not have adequate security measures in place to handle the significant influx of tokens, which might lead to it running out of gas and failing. Due to this, the contract might no longer be usable, which could cause users who depend on it to lose money or other assets.

Send attacks that are left unchecked pose a special threat because they might be challenging to identify and have negative user effects. Smart contract developers should put safety measures in place to stop massive influxes of tokens, like limiting the number of tokens that can be sent in a single transaction, to protect against uncontrolled send attacks.

Integer overflow/underflow

This flaw occurs when a smart contract improperly handles integer arithmetic operations, potentially allowing an attacker to change the state of the contract.

When an integer value exceeds the amount that can be stored in the specified number of bits, it is said to have overflowed. The value may "wrap around" as a result and turn very little negative. When an integer value is less than the smallest amount that may be stored in the allocated number of bits, an integer underflow occurs. The value may "wrap around" as a result and turn into an extremely large positive number.

An attacker may take advantage of these weaknesses to influence the state of the contract and maybe get unauthorized access to money or assets. An attacker may, for instance, employ an integer overflow to make a contract move more money than it should or an integer underflow to make a contract transfer less money than it should.

Smart contract developers should thoroughly evaluate the code and implement safety measures to prevent integer overflow and underflow flaws. Using tools or libraries that can handle arithmetic operations involving huge integers is one method to achieve this. Utilizing data types that can store huge integer values without incurring overflow or underflow is another choice. It is crucial for developers to put these safeguards in place in order to guard against vulnerabilities that might be used by attackers.

Lack of access control

Without adequate access control safeguards, a smart contract may be open to unwanted alterations or attacks.

A smart contract may be open to attacks or illegal changes if the right access control procedures are not in place. For instance, if the contract does not have adequate security measures to prevent unauthorized access, an attacker might be able to alter the status of the contract or access sensitive data.

Smart contract developers should use measures like using access modifiers (e.g., "public," "private," or "internal") to control access to contract functions and data, as well as role-based access control to grant access to certain functions or data to specific groups or individuals, to prevent lack of access control vulnerabilities.

Lack of input validation

A smart contract may be vulnerable to malicious data being injected into it if input is not adequately validated, which might allow an attacker to modify the contract's status.

A smart contract may be vulnerable to malicious data being injected into it if input is not adequately validated, which might allow an attacker to modify the contract's status. For instance, a hacker could be able to take advantage of a lack of input validation to force a contract to send money to an unapproved address or to gain access to confidential information.

Smart contract developers should put mechanisms in place to validate the data that is input into the contract in order to guard against vulnerabilities caused by a lack of input validation. This could entail putting in place checks to make sure that data satisfies particular requirements prior to being accepted by the contract and using libraries or tools to validate data types, ranges, and formats.

Summary

Smart contract flaws can have detrimental effects on consumers, including the loss of money, the impossibility of accessing assets, and the disclosure of private or confidential data. It's critical that both consumers and developers are aware of potential vulnerabilities and take precautions to guard against them. Using mutexes to prevent concurrent calls to a contract's functions, limiting the number of tokens that can be sent in a single transaction, using tools or libraries that support arithmetic operations with large integers, putting in place access control measures, and validating data input into the contract are some of the methods covered in this article for securing smart contracts.

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!