What is smart contracts security audit and why is it so important?

Paulina Lewandowska

10 Jan 2023
What is smart contracts security audit and why is it so important?

Introduction

You've probably heard of "security audits" if you've ever used a smart contract. Because a blockchain-based system is only as safe as its weakest link, it's crucial for smart contract developers to conduct routine security audits to make sure that their systems are not exposed to threats or flaws that could reveal confidential data. It's critical for developers to comprehend potential security concerns with smart contracts and how to address them to give users a satisfying user experience. Security audits are a key step in the process whether you're creating your own blockchain platform or adding smart contracts to an existing one.

Smart contract security audits

With the terms of the agreement written directly into lines of code, a smart contract is a self-executing contract. Blockchain technology, a decentralized system that securely and openly records transactions, frequently uses them. A comprehensive assessment of the security and dependability of a smart contract is called a smart contract security audit.

A smart contract security audit involves experts examining the code for flaws and making sure the contract works as intended. This procedure is crucial because it aids in identifying any potential contract problems, ensuring that the contract will function properly and securely when applied in practical circumstances. Developers and users can trust that the contract is trustworthy and reliable by conducting a smart contract security audit.

Reasons for smart contract audit

Before creating a smart contract, there are a number of factors to take into account. The primary goal is to guarantee your smart contract's functioning, security, and interoperability with other contracts. A smart contract audit has a number of special advantages, including:

  • Identifying and fixing any bugs or vulnerabilities in the code
  • Ensuring the security of the smart contract and protecting against potential attacks
  • Verifying that the smart contract will function as intended and not cause any unintended consequences or malfunctions.

By conducting a thorough security audit, you can have confidence in the integrity and reliability of your smart contract.

There are three main types of smart contract audits: manual, automated, and hybrid.

  • A manual audit involves a human expert manually reviewing the code and identifying any potential vulnerabilities or weaknesses. This type of audit can be time-consuming but is often considered the most thorough option.
  • An automated audit uses software tools to scan the code for potential issues. While automated audits can be faster than manual audits, they may not catch all potential vulnerabilities.
  • A hybrid audit combines both manual and automated approaches, providing a balance between thoroughness and efficiency. By using both human expertise and automated tools, a hybrid audit can provide a comprehensive assessment of the smart contract's security and functionality.

Ultimately, the type of audit you choose will depend on your specific needs and resources.

Steps involved in a smart contract audit

  • Code review: The auditor thoroughly examines the code during this stage to find any potential problems or weaknesses.
  • Static analysis: The auditor conducts a static analysis of the code using a variety of tools to search for errors and security flaws.
  • Dynamic analysis: To assess the smart contract's performance and check that it performs as intended, the auditor runs tests on it.
  • Testing: To find any mistakes that might occur during execution, the auditor tests the smart contract using a variety of tools under actual business settings.

These stages will enable the auditor to offer a thorough evaluation of the security, usability, and overall effectiveness of the smart contract

Smart contract audit checklist

To ensure the security and dependability of the contract, it's crucial to take a number of variables into account when conducting a smart contract audit. Here are some crucial factors to bear in mind during the audit process:

  • Common errors: The auditor will look for any harmful code or defects that could allow attackers to modify the terms of the smart contract code and obtain money or information.
  • Known vulnerabilities: The auditor will look for any flaws in the Ethereum platform that have been publicly disclosed and which could result in security breaches or other problems with the smart contract code.
  • Exploits that might be used: The auditor will assess whether there are any feasible methods that a hacker might use the smart contract system to his advantage and possibly steal money.

Importance of auditing a smart contract

Although smart contracts are a ground-breaking technology, they must be properly tested and confirmed before being utilized in the real world because, like any software, they are not flawless. The value of security testing is becoming increasingly clear as the use of smart contracts increases. Working with an expert auditor who is familiar with the nuances of smart contracts and who can see any potential weaknesses or vulnerabilities is crucial for this reason. You can be sure that your smart contract is secure and reliable and eliminate any future risks or problems by completing a thorough audit.

You may find a more in-depth article on this subject that Nextrope has already published here.

Conclusion

In conclusion, security audits of smart contracts are an essential component of blockchain development. They aid programmers in making sure their code is safe, capable of withstanding attacks during periods of high traffic, and scalable enough to meet demand over time. You and your team can find any possible problems and fix them before they have an impact on usability and perhaps turn away clients by routinely inspecting any new applications before release. Security audits of smart contracts are crucial to preserving your platform's dependability and integrity.

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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!