Smart Contract Attacks: The Most Memorable Blockchain Hacks of All Time

Paulina Lewandowska

30 Dec 2022
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Due to their ability to automate financial procedures and transactions, smart contracts have the potential to completely change the way we conduct business. They are not impervious to security flaws, though, as is the case with other technologies. There have been a number of smart contract hacks in the past that have caused large losses and damaged the community's confidence. The most famous smart contract hacks ever will be covered in this article, along with the lessons that may be drawn from them. These incidents—from the DAO hack to the Bancor hack—have had a long-lasting effect on the blockchain sector and serve as reminders of the value of properly safeguarding smart contracts.

The DAO hack

A decentralized venture capital fund for the cryptocurrency and decentralized technology industries was one of the goals of the Decentralized Autonomous Organization, or DAO. Its decentralized architecture was designed to cut expenses while giving investors more power and access. The DAO was designed to run decentralized, relying on the collective judgment of its investors.

A flaw in the coding of The DAO, a smart contract on the Ethereum blockchain, was found by a hacker on June 17, 2016. This gave the attacker the ability to ask the contract to send money to them repeatedly, leading to the theft of 3.6 million ETH, which was then valued at about $70 million. Due to two flaws in the contract's architecture, the exploit was made possible: a mechanism that first transmitted the ETH and then modified the internal token balance was not designed to account for the possibility of repeated calls.

A flaw in the coding of The DAO, a smart contract on the Ethereum blockchain, was found by a hacker on June 17, 2016. This gave the attacker the ability to ask the contract to send money to them repeatedly, leading to the theft of 3.6 million ETH, which was then valued at about $70 million. Due to two flaws in the contract's architecture, the exploit was made possible: a mechanism that first transmitted the ETH and then modified the internal token balance was not designed to account for the possibility of repeated calls.

The Veritaseum hack

A cryptocurrency called Veritaseum was introduced in 2017. A cyberattack at Veritaseum in April 2018 cost the company the equivalent of $8.4 million in cryptocurrencies.

The Veritaseum cryptocurrency's smart contract had a flaw that allowed for the hack to take place. By using a reentrancy attack, the flaw allowed an attacker to siphon money from the Veritaseum smart contract. In a reentrancy attack, an attacker can run a smart contract's function repeatedly before the state of the contract is changed, allowing the attacker to remove money from the contract before the state is updated to reflect the withdrawal.

The Veritaseum attack served as a reminder of the value of properly protecting smart contracts as well as the possible dangers of employing them. It also emphasized the necessity of rigorous testing and auditing of smart contracts to make sure they are safe and without flaws.

The Bancor hack

On the Ethereum blockchain, the Bancor network is a decentralized exchange that enables users to purchase and sell a range of different cryptocurrencies. The Bancor network was hacked in July 2018, and as a result, about $12 million worth of cryptocurrency was lost.

The hack was conducted by taking advantage of a weakness in the smart contract that controlled the Bancor network. Due to a vulnerability, an attacker was able to take over the Bancor contract and steal money from it. In order to stop more losses, the Bancor team was able to react to the attack promptly and halt trading on the site.

The Bancor attack served as a reminder of the value of properly protecting smart contracts as well as the possible dangers of employing them. It also emphasized the necessity of rigorous testing and auditing of smart contracts to make sure they are safe and without flaws.

Hacks in DEFI

Decentralized finance (DeFi) projects benefit greatly from smart contracts since they enable automated, self-executing financial processes and transactions. They are used to speed up, confirm, and enforce contract negotiations and performance.

Because smart contracts can be used to enable a variety of financial transactions and handle large quantities of money, smart contract security is crucial in DeFi projects. If a smart contract is not adequately protected, attackers may leverage its flaws to steal money from it or engage in other forms of contract manipulation. Users of the DeFi project may suffer large losses as a result, and the initiative's credibility and dependability may be harmed.

The bZx hack

A decentralized finance (DeFi) platform called bZx enables users to utilize smart contracts to borrow and lend cryptocurrency. bZx experienced two different attacks in February 2020 that took use of holes in its smart contracts.

On February 14, 2020, a hacker used a flaw in the bZx smart contract to steal about $6 million worth of cryptocurrency. This was the first theft. On February 18, 2020, a fresh vulnerability in the bZx smart contract was used by a different hacker to steal an additional $350,000 worth of cryptocurrency.

The bZx hacks were caused by flaws in the bZx smart contracts, which let attackers take advantage of them and steal money from them. The intrusions served as a reminder of the value of properly protecting smart contracts as well as the possible dangers of employing them. To ensure the security and lack of vulnerabilities in their smart contracts, DeFi projects must thoroughly test and audit them.

The Harvest Finance hack

The Harvest Finance hack was a security issue that happened in October 2020. An attacker used a smart contract weakness to steal cryptocurrencies valued at about $24 million. A decentralized finance (DeFi) technology called Harvest Finance enables users to generate yield by supplying liquidity to various financial marketplaces.

The hack happened when a perpetrator drained funds from the Harvest Finance smart contract by taking advantage of a flaw in it. Due to a vulnerability, the attacker was able to alter the contract and withdraw money from it without setting off the security features. The Harvest Finance team was able to stop trading on the platform to stop more losses after the hack was identified many hours after it happened.

The Akropolis hack

The Akropolis decentralized finance (DeFi) platform was attacked on November 12, 2020, when a protocol flaw resulted in the loss of about 2,030,841.0177 DAI from the impacted YCurve and sUSD pools. The problem was caused by a bug in the platform's SavingsModule smart contract's handling of the deposit logic, which gave the attacker the ability to create a significant number of pool tokens without the support of valued assets. This happened because the protocol did not correctly impose reentrancy protection on the deposit logic and validate supported tokens. Users of the Akropolis platform experienced severe disruption and losses as a result of the Smart Contract Hacks.

Conclusion - Smart Contract Hacks

One cannot stress the significance of properly safeguarding smart contracts. Smart contracts are capable of handling large quantities of value and a variety of financial activities. If a smart contract is not properly secured, it may cause consumers to suffer large losses and jeopardize the project's legitimacy and dependability.

Because of this, it is crucial that smart contracts undergo extensive testing and auditing. Smart contracts can be made secure and fault-free with the aid of testing and auditing. It is an essential stage in the creation process and can aid in safeguarding the security of blockchain projects and ensuring their smooth operation.

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Master UI Component Creation with AI: The Ultimate Guide for Developers

Gracjan Prusik

24 Mar 2025
Master UI Component Creation with AI: The Ultimate Guide for Developers

Introduction

Modern frontend development is evolving rapidly, and creating UI components with AI tools is helping developers save time while enhancing interface quality. With AI, we can not only speed up the creation of UI components but also improve their quality, optimize styles, and ensure better accessibility.

This article explores how creating UI components with AI is transforming frontend development by saving time and improving workflows. Specifically, we will discuss:

  • Generating components from images,
  • AI for style analysis and optimization,
  • Automatic style conversion and code migration,
  • AI in generating UI animations.

Creating UI Components with AI from Images

One of the interesting applications of AI in frontend development is the ability to generate components from an image. AI can recognize the structure of the interface and generate HTML/CSS or JSX code that matches the provided image. One of the most popular tools for UI creation is Lovable.

For testing, let's see how the tool performs in creating a simple contact form from the page you are currently on, which is Nextrope.

Query:

"Recreate the image I've sent you in Next.js using CSS."

Sample image used in the query:

The result received:

Tworzenie komponentów UI - Wygenerowany widok

Lovable did an excellent job transforming the image into code. The view is fully responsive. It’s important to remember that the more precise the request, the better the AI will replicate the expected view. However, even with a simple command, the tool performed surprisingly well.

Of course, AI has its limitations. It still makes quite a few mistakes. The generated code still requires review and fixes from the developer, but in some cases, the entire process is significantly sped up—correcting the code often takes less time than creating the component from scratch.

Optimizing UI Components with AI Tools for Style Improvements

This problem of inaccurate code reproduction by AI can be partially addressed by using it to analyze and improve styles. Tools like ChatGPT, DeepSeek and Claude are capable of not only generating code but also diagnosing CSS errors and suggesting why a particular style might not be working as expected.

Simple example: Why is the div not centered?

Query: "Why is this div not centered?"

AI analyzes the code and provides the following response:

Problem: The parent container does not have a defined width or display: flex.
Solution: Add the appropriate styles to the parent container.

.parent {
  display: flex;
  justify-content: center;
  align-items: center;
  width: 100vw;
  height: 100vh;
}

AI for Automatic Style Conversion and Code Migration in UI Components

AI can assist with style conversion between different technologies, such as transferring code from traditional CSS to Styled Components or Tailwind CSS.

Let's assume we have a style written in traditional CSS:

.button {
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;
}

.button:hover {
  background-color: darkblue;
}

We can use AI for automatic conversion to Styled Components:

import styled from "styled-components";

const Button = styled.button`
  background-color: blue;
  color: white;
  padding: 10px 20px;
  border-radius: 5px;
  transition: background-color 0.3s ease;

  &:hover {
    background-color: darkblue;
  }
`;

export default Button;

AI can also assist in migrating code between frameworks, such as from React to Vue or from CSS to Tailwind.

This makes style migration easier and faster.

How AI Enhances UI Animation Creation

Animations are crucial for enhancing user experience in interfaces, but they are not always provided in the project specification. In such cases, developers have to come up with how the animations should look, which can be time-consuming and require significant creativity. AI, in this context, becomes helpful because it can automatically generate CSS animations or animations using libraries like Framer Motion, saving both time and effort.

Example: Automatically Generated Button Animation

Suppose we need to add a subtle scaling animation to a button but don't have a ready-made animation design. Instead of creating it from scratch, AI can generate the code that meets our needs.

Code generated by AI:

import { motion } from "framer-motion";

const AnimatedButton = () => (
  <motion.button
    whileHover={{ scale: 1.1 }}
    whileTap={{ scale: 0.9 }}
    className="bg-blue-500 text-white px-4 py-2 rounded-lg"
  >
    Press me
  </motion.button>
);

In this way, AI accelerates the animation creation process, providing developers with a simple and quick option to achieve the desired effect without the need to manually design animations from scratch.

Summary

AI significantly accelerates the creation of UI components. We can generate ready-made components from images, optimize styles, transform code between technologies, and create animations in just a few seconds. Tools like ChatGPT, DeepSeek, Claude and Lovable are a huge help for frontend developers, enabling faster and more efficient work.

In the next part of the series, we will take a look at:

If you want to learn more about how AI is impacting the entire automation of frontend processes and changing the role of developers, check out our blog article: AI in Frontend Automation – How It's Changing the Developer's Job?

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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