Data has become the vigor of the digital age, powering industries, economies, and societies worldwide. Whether personal information, financial records, intellectual property, or trade secrets, data is the driving force behind decision-making, innovation, and business operations. However, data security has emerged as a paramount concern with the increasing digitization of our lives and businesses.

Data & Threats

Data, by default, is not secure. It can face many different types of threats!

Phishing, malware, and everything in between

Malicious actors continually evolve their tactics to exploit vulnerabilities in data systems. The threat landscape is dynamic and relentless, from phishing scams to sophisticated malware. It can be something as simple as an email that looks legitimate but tricks you into revealing personal information, like your login details. Or they could use sophisticated software that sneaks into your computer when you download seemingly harmless files.

Data Breaches: A Trust-Tumbling Avalanche   

Imagine your favorite store suddenly losing your credit card information to cybercriminals. These breaches aren't just a blip on the radar; they can lead to severe financial losses and even tarnish a company's reputation, making customers hesitant to trust them again.

Data Integrity

Imagine a digital contract - once you've signed it, you want to be absolutely certain that no one can secretly alter the terms. Any unauthorized changes can create confusion, lead to wrong choices, and erode trust in the digital realm. It's like having an unbroken seal on your important documents, ensuring their reliability. Ensuring the integrity of data is critical.

Centralized Systems 

Think of centralized data storage systems like a fortress with a single gate. When a hacker manages to break through that gate, they can potentially wreak havoc on everything stored inside. It's like having all your valuables in one easily accessible room. A breach in such a system can be catastrophic, leaving everything exposed to theft or damage.

Data & Security

As we've explored, data is under a constant barrage of threats in the section above. From phishing schemes that trick the unsuspecting to data breaches that leave us vulnerable, our personal information often hangs in the balance. However, blockchain technology's a shining beacon of hope amidst this sea of vulnerabilities.

Blockchain offers immutability, transparency, and hence, security with which we can

  1. Shield data against threats because of immutability and transparency
  2. It’s transparent and distributed nature can defend against phishing and malware
  3. Immutability also guards the data Integrity
  4. Dismantle Centralized Vulnerabilities because of Blockchain's decentralized nature.

IT Governance produced data breach reports, and from the stats, the most valuable sector is healthcare. Not only does it hold the largest percentage, but the percentage of breaches increased by 5% in a year!

Cyber attacks and data breaches

Cyber attacks and data breachers by sector, Q2 2023

Healthcare Data: Precious records of one’s life

In the world of medicine, data assumes a role of unparalleled importance, serving as the foundation upon which patient care is built. Healthcare data encompasses a wide range of information crucial for delivering effective and safe medical care. It is the backbone of modern medicine, underpinning the following vital aspects.

Blockchain in healthcare data

Eliminating Insurance Discrimination

Imagine securely locking your medical information in a digital vault that only you control. This means that you hold the key when it comes to your health history. No one else—especially not insurance companies—can peek inside without your permission.

Data Privacy and Fairness
By encrypting and securing healthcare data on a blockchain, individuals can have greater control over their medical information. Insurance companies will no longer be able to discriminate against individuals based on pre-existing conditions or sensitive health history.

Equitable Premiums

With a safeguarded medical history, patients will be treated fairly, as premiums will reflect a more balanced view of health risks. It's like ensuring that everyone pays a reasonable price for their insurance, no matter their health journey.

Fast-Tracking Medical Breakthroughs

Imagine if researchers and pharmaceutical companies had a secure, transparent, and lightning-fast way to access patient data. It's like a high-speed highway connecting them directly to the information they need. They can work more efficiently, like Formula 1 racers zooming to the finish line.

Secure Data Sharing

Blockchain's secure and transparent data-sharing capabilities can revolutionize clinical research by allowing researchers and pharmaceutical companies to access patient data securely. This can expedite drug development and improve patient outcomes.

Incentivized Participation

Through blockchain-based smart contracts, patients can be compensated for sharing their health data for research purposes while maintaining control over who accesses their information.

Supply Chain Management

Imagine a world where you can trace the journey of every medicine, like tracking a package you ordered online. From the moment a pill is made to the second it lands in your hands, its entire history is recorded. It's like an unbreakable chain connecting every step, ensuring no impostor medicine sneaks in.

Drug Traceability

Supply chain management ensures that the medication you take is the real deal. It's like a digital guardian for your health, making sure you're safe from counterfeit drugs and putting your well-being front and center.

Telemedicine and Remote Patient Monitoring

Imagine every detail from your virtual doctor's visits being sealed securely in a digital vault. It's like having a time capsule that holds your health story, ensuring it stays exactly as it was when you left your virtual appointment. You can have a doctor's appointment from the comfort of your home, and all your health data is sent securely through a virtual tunnel. It's like having a high-tech, invisible shield around your personal information.

Secure Data Exchange

With this security in place, healthcare providers can confidently offer you top-notch remote services, knowing your privacy is locked up like a fortress. It's like having a private consultation room at your fingertips, all thanks to blockchain.

Immutable Medical Records

Patient records generated during telehealth consultations can be stored on a blockchain, ensuring their integrity and availability for future reference.

Healthcare Fraud Prevention

Think of your medical bills as a ledger, like a record of all your expenses. Now, imagine that the ledger is super secure, like a locked vault. Every time there's a transaction, it gets recorded in this vault, and no one can tamper with it.

Billing Transparency

Blockchain can improve transparency in healthcare billing by recording every transaction securely. This doesn't just protect you but also insurance companies. When everyone can trust the billing process, it's a win-win for patients and insurers, making healthcare costs more transparent and fair. With blockchain, we can ensure no hidden fees or surprises in your medical bills.

No More Paper Prescriptions

Think about when you travel, and you need proof of your prescriptions to carry your medicines. It's like needing a permission slip for your pills. Now, imagine if you could have this proof securely stored online, like a digital passport for your medications.

Cloud prescriptions

With cloud prescriptions, you wouldn't have to deal with paper hassles or worry about losing them. It's like having a magic wallet that keeps your prescription records safe and easily accessible.


In an era driven by data, security and integrity are paramount. Blockchain technology offers a revolutionary solution to the challenges faced by data in our increasingly digital world. From protecting against cyber threats to ensuring fairness and privacy in healthcare, blockchain's potential is vast.

Patients can regain control of their medical information, insurance discrimination can be eliminated, medical breakthroughs can be accelerated, and data sharing can become seamless and secure. Blockchain ensures transparent supply chains, secures telemedicine, and preserves the integrity of medical records. It also brings transparency to healthcare billing and eliminates the need for paper prescriptions when traveling.

Embracing blockchain is not just adopting a new technology; it's a commitment to safeguarding the core of our digital lives. With BlockApex, get all that is necessary to leverage this technology for the better!

We already went through the primer of fuzzing. It's time for us to venture further into the depths of this fascinating realm.

The aim of this article is pretty straightforward. We will discuss various methodologies, including stateless and stateful fuzzing, bounded model checking, and end-to-end (E2E) testing. We will also explore renowned tools such as Echidna, Etheno, and Foundry, illuminating their unique differences and operation mechanisms.

So buckle up, and prepare for an enlightening journey into the intricate world of fuzz testing methodologies and frameworks!

Let’s get started, shall we?

Delving into the heart of fuzzing methodologies, we have a couple of points of interest! The two central pillars: Stateless Fuzzing and Stateful Fuzzing.

Given our solid foundation in the basics of fuzzing, exploring these two methodologies should be a manageable task. Let's initially grasp them through easy-to-understand hypothetical scenarios.

This time we take up an example of a glass! 

Stateless Fuzzing

Stateless fuzzing is like hosting a series of independent one-act plays, each involving a different glass. Picture this: you're the director of a zany experiment where the main objective is to find out if the glass will break under certain circumstances.

In the First act, we see our lead (a glass) being tapped by a teaspoon. Lights out, curtains close. 
For Act Two, instead of continuing with the same glass, you bring a brand new glass onto the stage. This time, you drop a small pebble into it. 
Third Act requires you to throw it on the ground, again you bring a new one of the set.

And so it goes, each act starting with a new glass and a new scenario.

Just like a forgotten Hollywood star, the glass from the previous act is disregarded. Each new act is oblivious to the fate of its predecessor. But you might wonder, "Hey, what if the teaspoon tap weakened the glass, and it was the pebble that pushed it to the edge? Shouldn't we consider this?"

Enter our next star, stateful fuzzing!!!!

Stateful Fuzzing

With stateful fuzzing, we switch from one-act plays to a gripping multi-episode drama. Now, instead of a new glass for each experiment, we have one protagonist glass that goes through every scenario in sequence.

Imagine act one starts with the same teaspoon tap. But in act two, instead of grabbing a new glass, we take the same one and drop the pebble into it. By act three, this brave little glass is starting to tell a story, its history reflected in every smudge and scratch from the previous episodes.

It's like a thrilling TV series. You can't just jump into season three without knowing what happened in the first two seasons. Everything is connected; past actions have repercussions. If that glass cracks or breaks, we'll know the full journey that led to its downfall.

To sum up, stateless fuzzing is a series of isolated, forgetful tests on different 'glasses', while stateful fuzzing follows a single 'glass' through all the trials and tribulations, carrying the history of each test into the next. It's the difference between speed dating and a long-term relationship, fr!

And remember, whether you're dealing with stateless or stateful fuzzing, always wear safety goggles. You know, for the 'shattering' results!

Now let's understand one more important thing before we jump into the necessary tooling.

Bounded Model Checking (BMC)

Bounded Model Checking is basically a technique used in testing, including fuzz testing, to verify systems against a set of specifications that are pre-defined. It does so by checking a system's behavior for a fixed number of steps, i.e., the "bound". If a bug or discrepancy can be found within this fixed number of steps, the model checker will report it. It's just one side. Let's talk about it a little more!

Common example

let's say depositing zero amount into AMM on a fresh contract will revert with the error “MIN_INITIAL_SHARES” remember when I say don't waste your call with inputs that revert, the obvious calls; for that, unit tests are enough. In that case, we know depositing such amounts will throw the same error, so I will guide my fuzzer not to generate inputs that lie outside those “bounds.”

Here's a simple analogy: Imagine you're in a maze (your software), and you're trying to find if there's a path that leads you to a cheese (the bug). The Bounded Model Checking process is like saying, "I'll only look for paths that take 10 steps or less". If the cheese can be found within those 10 steps, you'll find it. If not, BMC will report there's no cheese, at least within those 10 steps. Simple!

Some essence of E2E Testing

E2E testing is a testing methodology that validates the complete flow of an application from start to finish. It simulates a real-world user to ensure that the system behaves as expected. For instance, in a sign-up form scenario, E2E testing would validate all possible user actions, such as entering a blank email and password, a valid email and password, an invalid email and/ or password, followed by clicking the sign-up button. This ensures that all these actions work as a user might expect.

When we talk about mapping E2E testing onto fuzz testing, we're essentially combining the structured, scenario-based approach of E2E testing with the randomness and unpredictability of fuzz testing. E2E testing ensures the system works as expected under normal conditions, while fuzz testing checks the system's stability against unexpected conditions.

Example time: Imagine your smart contract is like a vending machine. End-to-End (E2E) testing is like checking the entire process of buying a snack: inserting the coin, choosing the snack, and receiving it. Everything should work as expected.

Now, fuzz testing is like a curious kid who starts pushing random buttons or inserting random objects instead of coins into the vending machine. The goal is to see if the machine can handle this unexpected behavior without breaking down.

So, when setting up your testing environment, you're preparing for both the regular snack buyer (E2E testing) and the curious kid (fuzz testing). This way, you ensure your vending machine (smart contract) can handle both expected and unexpected situations.

Open up the toolbox!

Lastly, we need to know which tools are best in the case of Solidity smart contracts. When I started working in the web3 space, I instantly became a huge Hardhat fan, only to discover Foundry and get distracted by it. 

As I came across fuzz testing, Echidna, a product by Trail of Bits, embraced me with open arms. It's good, does the job, is intelligent, and is based on HEVM, allowing more in-depth low-level controls. 

But tbh, when Foundry showed its fuzzing prowess, I fell in love hard, but it didn't mean that I and Echidna broke up, let's compare both tools, and you know what, I’ll let you guys judge!!

Framework benchmarking

Input GenerationEchidna generates inputs tailored to your actual code.Foundry fuzzer also generates random test inputs.
Corpus CollectionEchidna offers optional corpus collection and mutation to find complex multifunctional bugs.Foundry fuzzer's approach to corpus collection is not explicitly mentioned anywhere in their docs.
Coverage GuidanceEchidna provides coverage guidance to help identify which lines are covered after the fuzzing campaign. However, it does not provide proper coverage for invariants.Foundry fuzzer's approach to coverage guidance, it does provide a proper coverage file with code visibility.
Integration with Code Analysis ToolsEchidna is powered by Slither to extract useful information before the fuzzing campaign.Foundry fuzzer does not offer static code analysis out-of-the-box, tbh it's not necessary.
Targeted Use caseEchidna is primarily used for fuzzing/ property-based testing of EVM (Ethereum Virtual Machine) code. It allows explicitly marking the target contract.Foundry fuzzer is used for fuzzing Ethereum smart contracts in Solidity. Any contract can be targeted by pointing in the terminal.
Bounded Model CheckingIn Echidna, there is no bounded model checking integrated due to this fuzzer leading to wasted calls due to call reverts.Foundry fuzzer has a builtin bounded model checking which can be used according to the foundry docs.
State Sync from ForkEchidna requires the use of Etheno, an independent tool, for state sync from fork.Foundry fuzzer can sync the state from forking RPC which is very handy.
Cheat CodesEchidna does not provide any  cheat codes which is a very big hustle to work your way around configs.Foundry fuzzer provides the builtin libraries of cheat codes which helps a lot while writing fuzz tests .
Specific contract to TargetEchidna targets every contract but this can be controlled via command flags.In Foundry fuzzer you can explicitly specify the targeted contract in the setUp function.


This blog explores the fascinating world of fuzz testing methodologies and frameworks. We delve into stateless and stateful fuzzing. Bounded Model Checking (BMC) is introduced as a technique to verify systems against predefined specifications. Additionally, we discuss the essence of End-to-End (E2E) testing, combining structured scenarios with fuzz testing's unpredictability. Lastly, we compare renowned fuzzing tools, Echidna and Foundry, highlighting their unique features and differences.

Decentralized exchanges (DEXs) have disrupted the cryptocurrency trading landscape by introducing trustless and transparent platforms for exchanging digital assets. A critical element of DEXs is the order-matching mechanism, which enables the execution of trades. This blog post delves into the intricacies of order-matching mechanisms, highlighting the advancements that have enhanced user efficiency, liquidity, and overall trading experience.

Understanding Order Matching

Order matching is the fundamental process of pairing buy and sell orders to enable asset exchange. In traditional centralized exchanges, order matching is typically facilitated by a centralized order book. However, DEXs operate in a decentralized manner, necessitating alternative mechanisms to address the challenges associated with the absence of a central authority.

Approaches to Order Matching in DEXs

In the early days of DEXs, simplistic order matching mechanisms, often referred to as "first-come, first-served" or "priority-based" matching, were prevalent. During this nascent stage, trades were executed based on the order of receipt, without considering factors such as price or quantity.

Order Books

Order books serve as records of trade orders submitted by users who want to exchange assets. Here's how the process typically works:

  1. Users looking to exchange assets submit buy or sell requests, which are stored in the order book. By submitting an order, the user becomes a "maker."
  2. The order book contains information about the tokens the user wishes to exchange and the desired terms of the trade.
  3. To validate the order, makers sign it with their private key, providing authentication.
  4. The order is then broadcasted through the exchange network, where takers, or other participants looking to trade, come forward.
  5. If a taker is satisfied with a maker's order, they confirm the trade, and the smart contract handles the remaining steps of the process, such as asset transfers and settlement.

Liquidity Pools

To address the centralization challenge associated with order books, decentralized finance (DeFi) projects utilize liquidity pools. In this model, market makers are referred to as liquidity providers, and the pools facilitate trading. Here's an overview of how liquidity pools work:

  1. A liquidity pool consists of two or more tokens. When a liquidity pool is created, a liquidity provider supplies equal-value amounts of each token to the pool and sets an initial price.
  2. Any person adding tokens to the liquidity pool contributes an equal value of both tokens. As a result, liquidity providers earn LP (Liquidity Provider) tokens based on the amount of liquidity they provide to the pool.
  3. When trades occur, a portion of the transaction fee is distributed among all LP token holders in the pool. This rewards liquidity providers for their participation.
  4. With each trade, an automated market-making algorithm adjusts the price of the tokens in the pool, maintaining balance and reflecting market dynamics.

Liquidity pools and automated market making provide an alternative approach to trading, promoting decentralization and liquidity provision within the DeFi ecosystem.

This blog focuses on order books and the revolving aspect.

An order-matching system is imperative whether centralized or decentralized order books are employed. Where as in the case of AMMs we need liquidity for order to be fulfilled.

Order Matching Engine

An order-matching engine is a mechanism used in financial exchanges to match buy and sell orders submitted by market participants. It operates based on predefined rules and algorithms, considering price, time, and order priority factors. The primary objective of the order matching system is to facilitate the optimal execution of trades, ensuring fairness, efficiency, and price discovery within the marketplace.

The order-matching engine in a decentralized exchange constantly listens to the order book for new orders. When a new order is received, the engine attempts to find a matching order in the book. If no match is found, the order is added to the order book and remains there until a suitable match is found. The transaction is executed once a match is identified and both parties are notified.

Various methods can be employed within an order-matching engine. The most commonly used algorithm is the first in, first out (FIFO), which prioritizes fulfilling the older order first. Additionally, there are algorithms like Price-Time Priority and Pro-Rata Algorithms.

Price-Time Priority Algorithm

A price-time priority algorithm is a fundamental approach used in order-matching systems. It prioritizes the highest bid with the lowest ask to ensure the best available price for trade execution. The algorithm compares the prices of buy and sell orders, giving preference to orders with the most favorable prices. In case of orders with the same price, the algorithm prioritizes the order placed earliest.


Using the price-time priority algorithm, the highest bid ($35,500) will be matched with the lowest ask ($35,000). This ensures that the best available price is achieved and the trade is executed based on the order priority.

Pro-Rata Algorithm

The pro-rata algorithm distributes the available quantity among compatible orders proportionally. When there are multiple orders at the same price, this algorithm divides the trade quantity based on the relative sizes of the orders. Each order receives a fraction of the trade based on its proportion to the total quantity. The pro-rata algorithm promotes fairness by providing an equal opportunity for traders to participate in trades at the same price level.


Consider the following scenario.

The available quantity will be proportionally distributed among the compatible orders using the pro-rata algorithm. In this case, each order will receive a fraction of the trade based on its relative size.

To calculate the fractions, we need to determine the total quantity of all compatible ask orders, which is 2 + 3 + 1 = 6 Bitcoins. Using the pro-rata algorithm, User X will receive 2 Bitcoins, User Y will receive 3 Bitcoins, and User Z will receive 1 Bitcoin.

Each user's allocation is determined by the proportion of their order size relative to the total quantity available to be traded.

Enhancing Order Matching Engine With BlockApex

Team BlockApex is working on a DEX, that aims to combine the user-friendly experience of centralized exchanges with the security and transparency of decentralized platforms.

The order matching algorithm implemented by our experienced team enables partial and fractional order matching.

The order matching engine follows a price-time matching preference, where the price is the primary key and time as the secondary factor. The highest bid is always matched with the lowest ask. To facilitate this, the exchange maintains two priority queues, one for bids, also known as buy orders, and the other for asks or sell orders, for each trading pair.

Let's walk through the order-matching flow using an example

Suppose User X submits an ask order of 1 Bitcoin for $35,500. Since this price is lower than the highest bid (User A's $36,000), the system attempts to match User X's ask order with the available bids.

The system takes the top bid from the bid queue, which in this case is User A's bid of $36,000, and checks if User X's ask can be fulfilled. The order can be fully filled since User A's bid price is higher than User X's ask price. The system updates the order status, and User X's ask order is matched and filled by User A's bid. If the bid price were lower than the asking price, the system would continue checking the next bid in the queue until a match is found or no more bids are available.

After the successful match, the queue is updated accordingly. User A's bid is removed from the bid queue, and the next highest bid (User B's $34,500) becomes the new top bid in the queue.

If a new order enters the queue, it does so through smart insertion. Furthermore, it should be noted that order matching always takes O(1) time. The order-matching algorithm handles various scenarios, including both bid and ask orders being partial or complete and cases where either the bid or ask order is partial or complete.

Users have the flexibility to choose whether they want to allow partial orders or only complete orders. However, the system supports both direct order matching, i.e., where orders are matched at the exact price point, and fractional order matching, i.e., where orders are partially matched based on their proportional quantities.

The order-matching algorithm handles various cases such as

Priority Queue: Fueling Efficiency and Precision

In a Priority queue each element is assigned a priority value. Elements with higher priority are dequeued before elements with lower priority. In the context of order matching, priority queues are used to manage buy and sell orders based on their prices.

There are multiple reasons for using priority queue such as

Partial Order Matching

When the users choose the option of partial order matching, the order gets fulfilled with multiple orders. Users have the freedom to accept the bids and leave, even if their ask orders are not fully filled, or they can wait until they obtain the requested amount.

An example of how it works is shown in the figure below

The order matching engine ensures efficient and equitable trade execution by matching the highest bids with the lowest asks while considering the time priority of orders. Partial order matching is supported, further augmenting efficiency and liquidity.

Order matching engines and algorithms play a vital role in decentralized exchanges, facilitating the seamless execution of trades and ensuring fair and efficient market operations. From the early priority-based matching to the more sophisticated algorithms like price-time priority, FIFO, and pro-rata, order matching has evolved to meet the diverse needs of traders. 

As decentralized exchanges continue to innovate and refine their order-matching mechanisms, users can expect improved liquidity, faster order execution, and a more seamless trading experience. With the combination of user-friendly interfaces and the inherent benefits of decentralization, DEXs are poised to revolutionize the financial landscape, empowering individuals to have full control over their digital assets and participate in a global, trustless marketplace.


Decentralized exchanges (DEXs) have transformed cryptocurrency trading by providing transparent and trustless platforms for exchanging digital assets. Order matching, a crucial component of DEXs, has evolved from simplistic priority-based matching to advanced algorithms like price-time priority, FIFO, and pro-rata. DEXs have introduced centralized and decentralized order book models to improve efficiency while maintaining decentralization.

Also read our blog about Liquidity Challenges In Illiquid Marketplaces.


Let’s start with how hacks happen!

In most cases, security breaches in softwares are a result of unexpected scenarios that haven't been anticipated while unit testing and therefore do not have written tests for.

Imagine if I were to suggest that it's possible to handle such a unique edge case and compose a single test that could scrutinize nearly all potential scenarios.

In this article series, we will try answering one question at a time, starting with what is fuzz testing. Let's dive right in. 

What is fuzz testing and how can it be applied to smart contracts in Solidity?

I will start with the first half of the question, and we will then connect the dots with the second part by the end of this article.

Well, for starters, there is a formal definition of fuzzing and fuzz testing available, which goes by the book, but that’s not what you guys are here for, right? 🙂

Let me keep it easy and simple for you. While writing tests, you make sure that code coverage should always be 100%, but even when the coverage is 100%, you can never ensure that no bugs have been left in your smart contract code. That’s where fuzzers come in, fuzzers generate a set of inputs for your contract’s test cases by reaching the boundaries that are missed while writing unit tests.

Formally! Fuzz Testing or Fuzzing is when you supply random data to your system in an attempt to break it.

But it still depends on how good fuzz tests you have written. 

Fuzzers as software are dumb, basically they lack intelligence and the computational boundaries within which they operate. 

In such a context where multiple actions are defined, a fuzzer is liable to choose any of them at random for execution. This raises a  challenge where the fuzzer chooses an action for a particular situation that is inappropriate, e.g., the fuzzer is misled by an inaccurate address for an onlyOwner type function, resulting in the expected reversion.

This issue is considered a low-hanging fruit since we anticipate that the contract should revert in such scenarios, which should be covered within our unit tests. Hence, it would be ideal if our invariant tests could bypass these actions. 

This ultimately would prevent wasting valuable fuzzing calls that can be more effectively utilized on valid edge cases. Therefore, one of the challenges of writing Fuzz Tests is getting the most value out of them.

Fuzzing is just a technique used to improve security. This can uncover vulnerabilities that manual testing might miss since it covers a wider range of potential input scenarios.

Nevertheless, while fuzzing enhances the security of smart contracts, it doesn't always guarantee absolute security. It's just another method of reducing security risks but it does not completely eliminate them. This is because while fuzzing can test many different inputs and scenarios, it may not cover every possible scenario, especially those that involve complex multistep interactions with other contracts.

Introduction of Invariant/Property

Now, let's get an introduction to Invariant, a.k.a. Property. This is where things get little bit complicated (or rather holistic, but not as much as you think).

To put it simply, Invariant is the property that you bet that the system should always hold. During exhaustive testing on this part, you can anticipate that fuzz testing is much more dynamic as compared to unit testing. In unit testing, you supply a single input and get the expected/unexpected results, but in the case of invariant testing, you bet that specific property should be held!

During an invariant test run, the fuzzer will call the test with many randomly generated values, verifying that our assertion holds for each one. This lets us test a specific property of a specific function in a specific contract.

The term "invariant" in the context of DeFi protocols refers to a particular property or rule that must never be violated, no matter what actions are taken within the system. Essentially, these invariants are the core principles or 'laws' that the protocol operates by to ensure the system's stability and fairness.

Lending Markets Invariant

In lending markets like Compound or Aave, there is an important rule that helps ensure the system's overall safety. The rule states that

A user cannot take any action that puts their account, or any other account, into a situation where the value of their borrowed assets exceeds the value of their collateral.

To explain further, when you borrow assets in these markets, you must provide collateral of greater value. This collateral acts as a safety net for the protocol and its lenders. The 'safety threshold' defines the maximum ratio of borrowing to collateral allowed. If the value of the borrowed assets starts approaching this threshold, the account is deemed unsafe. Users are restricted from taking actions that would push accounts into this unsafe state or further worsen an already unsafe state. It's like preventing someone from borrowing more than their house is worth in a mortgage agreement.

Automated Market Makers (AMMs) Invariant

AMMs like Uniswap or Sushiswap, have a core invariant based on a mathematical formula that maintains the relationship between the amounts of two tokens in a liquidity pool.

A widely used invariant is x*y=k, where x and y represent the quantities of two tokens in a pair, and k is a constant value. This equation ensures that the product of the amounts of tokens is always constant. It helps determine the price for each token and maintains a balance of liquidity between them. If more of one token is bought, the price of that token rises to maintain the constant k.

Liquidity Mining/Staking Invariant

Similarly, in liquidity mining or staking protocols like Yearn Finance or Synthetix, a key rule is

A user can only withdraw the same number of staking tokens they initially deposited.

This means if you deposit 10 tokens into the protocol for staking or liquidity mining, you can only withdraw those 10 tokens back. You might earn rewards for participating, but the amount of staking tokens you initially put in remains constant. It's similar to only being able to withdraw the exact amount of money you put into a savings account in a bank, regardless of the interest you've earned.

Invariants are the protocols' backbone, ensuring the systems remain stable and operate as intended.

Basic of Fuzzing

Now let’s move towards an interim question “How fuzzer generates inputs and discovers edge cases?”

To answer this question, we will use a code example for better understanding, but before that, we need to understand one more important thing.

While Invariant testing applies the same idea to the system as a whole, rather than defining properties of specific functions, we define "invariant properties" about a specific contract or system of contracts that should always hold. Invariant tests can be a great tool for shaking out invalid assumptions, providing a holistic approach to testing smart contracts. By examining the entire system, these tests uncover vulnerabilities, complex edge cases, and unexpected interactions. 

Let’s look at this ​​crowdfunding contract.

pragma solidity ^0.8.0;

contract Crowdfunding {
    uint256 public fundingGoal;
    uint256 public deadline;
    mapping(address => uint256) public contributions;

    constructor(uint256 _fundingGoal, uint256 _deadline) {
        fundingGoal = _fundingGoal;
        deadline = _deadline;

    function contribute() public payable {
        require(block.timestamp < deadline, "Deadline has passed");
        contributions[msg.sender] += msg.value;

    function withdraw(uint256 amount) public {
        require(block.timestamp > deadline, "Deadline has not passed");
        require(contributions[msg.sender] > 0, "No contributions");
        require(amount <= address(this).balance);
        contributions[msg.sender] = 0;

Introducing a Bug:

We will intentionally introduce a bug in the contract to demonstrate the effectiveness of fuzz testing. In the withdraw() function, we remove a vital constraint, that is, to check if the amount from the function argument is equal to or less than the user deposited balance to cover the withdrawal amount before transferring funds to the contributor. This oversight may allow an attacker to drain the contract's balance entirely.

Let’s say we have extracted a property that says, “The withdrawal amount should be less or equal to the deposited amount” (that's really basic, I know, but let's take it for the sake of learning…) Additionally, we could have extracted more properties, but right now I wanna jump to the point I know where this smart contract won't behave as expected.

So, our Property, though in pseudocode, sounds like the statement above, but in code, it will look like the following snippet.

assert( amount <= contributions[msg.sender]);

Think positive while extraction properties

This invariant is very simple to understand at this point. Unlike writing unit tests, where you often think about what could go wrong and you think offensive, this way, the invariants help you focus on how the system functions when everything is going right. In essence, you're studying the system and identifying what changes occur when specific actions are taken. After these actions are performed, you observe the transformations that happen within the system and to its state. 

It is this set of consistent and predictable changes where you think as a system developer and build your invariant around. So, instead of looking for potential threats to the system, 

You're concentrating on the inherent properties that ensure the system's stability and successful operation, which ultimately ensures the security of a system.


Fuzz testing, or fuzzing, is a technique used to improve the security of software, including smart contracts in Solidity. It involves supplying random or unexpected data as inputs to a system in an attempt to break it and uncover vulnerabilities that manual testing might miss. Fuzzers generate a set of inputs for testing scenarios that may have been missed during unit testing, helping to identify bugs and potential security issues.

Invariants, also known as properties, are specific rules or principles that should always hold true within a system. They are essential for ensuring the stability and integrity of protocols like lending markets, automated market makers (AMMs), and liquidity mining/staking systems. Invariant testing involves checking if these properties hold true by using randomly generated values and verifying the assertions for each input.

By using fuzzing and invariant testing together, developers can identify vulnerabilities, complex edge cases, and unexpected interactions within smart contracts. However, while fuzzing improves security, it does not guarantee absolute security. It is just one method to reduce security risks and should be used in conjunction with other security practices and techniques.

Also, read our audit course series "Infiltrating the EVM".

Artificial Intelligence (AI) and blockchain are two emerging technologies that have gained significant attention in recent years. While AI is rapidly becoming the top priority for Silicon Valley experts, blockchain and cryptocurrencies were the buzzwords until last year. Rather than focusing on trends, it's important to understand the potential of these technologies and how they can be integrated to unlock new possibilities. The AI and blockchain integration can help overcome some of the limitations of each technology and create a more secure, transparent, and efficient Web3 ecosystem. This article explores the differences between AI and blockchain, ways to integrate them, use cases, and challenges that need to be addressed.

Understanding the Differences: AI and Blockchain at a Glance

AI and blockchain both rely on data, but their core principles differ vastly. AI involves the ability of machines to learn and make decisions based on data, whereas blockchain is a decentralized digital ledger that records transactions in a secure and transparent manner. However, by integrating these two technologies, we can unlock a whole new level of innovation that can overcome the limitations of each technology.

For instance, the decentralized nature of blockchain can help improve the transparency and security of AI algorithms, while AI can enhance the efficiency and accuracy of web3 applications. This integration has the potential to revolutionize industries such as decentralized finance, healthcare, and supply chain management, creating new possibilities and opportunities for businesses and individuals alike.

Use Cases

Let’s discuss some of the use cases of AI and blockchain integration.

Decentralized Finance (DeFi)

AI can be used to analyze data on the blockchain to identify profitable trading strategies and optimize portfolio management. For instance, an AI-powered DeFi platform could use machine learning algorithms to analyze market data and identify patterns that indicate potential investment opportunities. This could help investors make better-informed decisions and maximize returns on their investments.

Supply Chain Management

AI can be used to track goods and services throughout the supply chain and ensure compliance with regulations. For example, an AI-powered supply chain management system could use machine learning algorithms to track products from their origin to their destination and ensure that they meet all regulatory requirements along the way. This could help increase transparency and trust in supply chain operations.

Personalized Content

AI can be used to create personalized content for users based on their preferences and behavior on the blockchain. For instance, an AI-powered social media platform could use machine learning algorithms to analyze a user's activity on the platform and recommend content that aligns with their interests. This could help improve user engagement and retention. The entire concept of metaverses and virtual counterparts depicting people’s real world life can be revolutionized through the use of AI.

Identity Verification

AI can be used to verify the identity of users on the blockchain, which can help prevent fraud and enhance security. For example, an AI-powered identity verification system could use facial recognition technology to verify a user's identity before granting them access to a blockchain-based application. This could help reduce the risk of fraud and protect user data.

Optimizing Mining Costs

AI can help optimize costs for blockchain mining by analyzing data and providing insights on the most efficient mining strategies. By analyzing variables such as electricity costs, hardware efficiency, and network difficulty, AI algorithms can identify the most cost-effective mining approach. Additionally, AI can help predict market trends and fluctuations, which can inform decisions on when to buy or sell mining equipment and how to allocate resources. It has the potential to enhance the efficiency and profitability of blockchain mining operations.

However, the AI and blockchain integration is not without its challenges. One of the biggest challenges is the lack of standardization in data formats and protocols. This makes it difficult to share data between different blockchains and AI systems. Another challenge is the potential for bias in AI algorithms, which can result in unfair or inaccurate decisions.


The AI and blockchain integration can unlock new possibilities and help overcome some of the limitations of each technology. However, it is important to address the challenges and ensure that the integration is done in a responsible and ethical manner. By doing so, we can create a more secure, transparent, and efficient Web3 ecosystem.

Also read GameFi: Future of Gaming or Short-lived Gimmick?

This target is focused on exploring the dark side of Play-To-Earn dApps.

The recent crypto bear market has provided a valuable lesson to different kinds of investors. The seasoned players have learned the importance of liquidity management, and new entrants have witnessed the consequences of a sagging market. In the last quarter, Bitcoin, the largest and most well-known cryptocurrency, has lost over 25% of its value. This bear market has been characterized by a loss in investor confidence with liquidity being drained from the market, increased regulatory scrutiny with the entire FTX fiasco, negative sentiments regarding the future of crypto and blockchain technology, etc.

Most of the economic and finance experts attribute this fall to the unnecessary hype and speculation that allowed artificial price increments of many cryptocurrencies from late 2017 and early 2018. Others believe that the downfall is due to the maturing of the market and the growing realization among people that valuations of the majority of crypto projects are not backed by sound fundamentals. Regardless of the reason, the current market is extremely volatile and poses a huge risk to new investors who plan to enter.

However, in the midst of all this crypto market spectacle, there is one category that still holds significant momentum, and it is the ‘play to earn’ class. Despite the failure of popular P2E projects such as Axie Infinity, Crypto Kitties, and even STEPN (in the move-to-earn category), the play-to-earn buzzword is still making rounds, and a whole new bunch of projects with P2E models are continuing to launch in the crypto space. 

A current example of such a project can be ‘Gala Games’ powered by the cryptocurrency $GALA. Despite having no whitepaper and no concrete tokenomics framework, the token has surged 130% on the year to date (YTD) basis. 

This article aims to uncover the loopholes of current P2E projects and how their token economic models are designed to benefit only the short-term users. We will also discuss the issue with a neutral perspective and highlight the key events due to which a P2E token price is bound to plummet with the currently applied frameworks.

The Shortcomings in P2E

Play-to-earn or P2E for short, typically refers to a business model where players can earn real-world or in-game currency by playing games, completing tasks, and performing different activities. This in-game currency is usually the project’s native cryptocurrency and is used to reward users. These tokens can then be swapped and withdrawn as real-world currency. But of course, the mechanics are designed such that exits are minimal, compelling players to rotate the earned currency in game and increase their standings. 

Let’s explore some of the reasons why this model is unsustainable and lacks a proper economic viewpoint.

  1. Prioritizing “Earn” Over “Play”

To start off, the biggest reason why the majority of the P2E projects fail is because they develop and market the ‘earn’ aspect rather than focusing on the ‘play’ aspect. They do not have a real product to show and they eventually tie the vision and growth of the project with the token instead of an interesting gameplay or sound in-game mechanics.

In addition, when the project is marketed as play-to-earn the behavior of users is such that they play the game specifically to earn and not for entertainment or leisure. This behavioral aspect of users plays a crucial role in determining the long-term growth and value of the project or, in other words, the token price. As users focus on the earn feature, sooner or later, their exits are inevitable, and this consequently affects the long-term value of the token.

  1. Flawed Economic Framework

Secondly, the economic frameworks of P2E projects violate the basic economic concepts of supply and demand. The reward tokens of majority of the P2E projects have uncapped supply, which means an unlimited amount of tokens can be minted and distributed to users. Economics 101 suggests that unlimited minting of tokens increases the circulating supply and leads to inflation while causing the value of the currency to decline. 

Now one might argue that these projects have burn mechanisms installed so that the circulating supply is reduced as rewards are accumulated. However, at a low level, it is pretty obvious that if the net impact is not increasing, then how are all the users generating returns? To be able to generate returns in the form of tokens, the net emissions must be positive, which means more tokens minted than burned.

Although the token price can stay afloat if the buying pressure is consistent, but again it can only happen if the token has sound utility, which is not the case with these projects. So regardless of the amount of rewards a user accumulates by playing the game, if the token value decreases over time, there is no incentive for the user to keep playing.

  1. Illusion of Sustainability

This brings us to the next point that how the number of new users play an important role in keeping a P2E project afloat in the short term. Let’s take Axie Infinity as an example to illustrate the point. The majority of the new capital inflows or purchases of the $SLP token (Axie’s reward token) is when new Axies are purchased/minted. This not only creates a buy pressure on the token but also reduces circulating supply as the $SLP proceeds are burned.

However, the framework is economically flawed because as the growth of new users tend to decrease there is no influx of new money or in other words, no buying pressure on the token, therefore it plummets in value. On the flip side, as the number of new users increases, the accumulated rewards also grow, which means that now more people will be dumping the reward tokens at the market, thus creating a classic Ponzi scenario.

With a declining token price and an increasing number of users, it becomes very difficult to maintain the same level of return because you now have to mint more tokens which translates to more token emissions and a further price decline. This death spiral causes a lot of players to lose interest and leave the ecosystem, and as they take their exits, the negative compounding effect on the token price becomes huge.

The $SLP token price of $0.002 from an all-time high of $0.36 and STEPN’s $GST token price of $0.015 from an all-time high of $7.8 are some of the examples acting as supporting evidence for their flawed economic framework.


In conclusion, play-to-earn projects have become an increasingly popular business model in the crypto space, but they are not immune to the volatility and challenges of the broader market. Many projects have struggled to establish a sustainable token economic model and have focused on the "earn" aspect of play-to-earn rather than the "play" aspect. This has led to a lack of real products and has contributed to the decline of token value in a bear market.

Furthermore, the unlimited minting of tokens in these projects can lead to inflation and ultimately decrease the value of the token. Additionally, play-to-earn projects often rely on a large and active user base to drive engagement and token usage. But in a bear market where users may be less likely to engage with these projects and instead focus on preserving their existing assets. This can lead to decreased demand for the token, further driving down its value.

At a micro level, there are a lot of other reasons why current P2E models are flawed such as protocols not re-investing their revenue in the ecosystem, underdeveloped unit economics to generate returns for each and every user in the long run, not tying the reward token to a sustainable in-game utility, etc.

also read Liquidity Challenges In Illiquid Marketplaces


With time, technology grows in almost every field. The inception of the internet has proved to be a significant technological revolution that influenced the world extensively. Talking about today, we have witnessed various phases of the web until now. 

The original web 1.0 was comprised of static pages whereas web 2.0 is more of an interactive version of the web where user-generated content is at its peak. Now we have web 3.0, which uses blockchain technology to create a more decentralized web.  Instead of independent sites hosted on a particular server, Web 3.0 use of the blockchain provides greater resiliency, protection against censorship, and other benefits.

Let’s dive into the concepts of web 2.0 and web 3.0, along with major security differences. 

Web 2.0: 

After the dot-com bubble burst, the constant need for technological advancements expedited the modifications on the internet. Those modifications brought large-scale changes to its usability, functionality, and also its interface.

In 2004, a conference was held, which is now famously known as the Web 2.0 Summit, Tim O’Reilly and John Battelle held the summit. They explained that web 2.0 is not just a mere concept but has a high gravitational core. They visualized web 2.0 as a set of principles and practices that tie together as a solar system. Those solar system components are visible at some distance from its core, making every element essential. 

Web 2.0 emerged as a reading, writing, and creating space where people could interact and collaborate. The web2 brought tremendous changes in the history of the internet. Firstly, the read-only version of the website was molded into a more interactive, updated version. Here, the users could interrelate with the content and even share their points of view in the form of feedback, comments, and suggestions which brought social media platforms to the limelight (e.g., Facebook, founded in 2004). These social media platforms let their users create content (such as blogs, press-release, articles, videos, etc.) which in essence, instill a sense of freedom of expression. Web 2.0 gained high popularity through user-generated content. 

From a Technical POV, web 2.0 expanded tremendously. The basic Html language was improvised by introducing Java, JavaScript, and other languages. That brought enhancement in creativity, improved functionality along with increased collaboration.

Although this new and improved version of the world wide web has paved the way for a plethora of emerging technologies and realized the previously conceptualized freedom, it has also imposed numerous restrictions. Most importantly centralization or censorship which means that there exists a central entity that has concentrated all the power and control within itself. Such kind of centralization creates a facade of “Perceived Freedom”. The platforms have all the control to manipulate things we see online!

Web 3.0: 

To cater to the issue of centralization web 3.0 emanates into the picture. Web 3.0 proposed the concept of decentralization. Decentralization simply means that the power and decision-making processes are passed down from the management to the users. There is no central entity that would control the internet.

The word Web 3.0 was coined by Gavin Wood - Co-Founder of Ethereum and Founder of Polkadot. 

What makes web 3.0 the future of the internet? Well, Web 3.0 is a semantic web where it promises to establish information in a better-existing way than any current search engine can ever attain. Web 3.0 promotes four concepts which mainly are authenticity, i.e., every piece of information existing on the internet is a fact or derived from a fact. Integrity, willingness to abide by moral principles, and ethical values. Transparency, the data present on the internet is accessible for every user to witness. Lastly, Confidentiality which is achieved by Blockchain technology, where every user’s identity is anonymous, making it secure. 

Web 3.0 also entails the use of Distributed Ledger Technology (Blockchain) and Smart Contracts, which protect the information of each of its users creating decentralization. 

Now let’s discuss some of the major differences between web 2.0 and web 3.0 

How Web 2.0 is Different From Web 3.0?

Web 3.0 faces many of the same security risks as Web 2.0.  However, the differences between the two technologies create new security risks and amplify others. 

Identity Governance: 

In the world of web 2.0, giving a real identity is the major focus. Many companies want to sell users’ data so that they could protect themselves from scams. Social media platforms usually ask for authentication so that they could have a list of known and identifiable users. This is also a security benefit because it gives a trail of information and evidence if any scam happens. 

Whereas Web 3.0, which is a blockchain-based system, works on pseudonymous where users could be identified with their public keys or blockchain address. Key management in web 3.0 is a major concern as weak authentication causes easy loopholes to penetrate, whereas difficult to identify the culprit. 

Patching vs Prevention: 

In the traditional IT world, a large amount of security work is responsive. A patch is deployed to call it a safe day when a loophole is discovered. If data on the server is corrupted with ransomware, it could be easily rolled back to the original state. 

Web 3.0 works differently, data is stored on an immutable ledger i.e once the data is deployed no changes can be made. It is highly cardinal to be proactive, and prevention focuses so that all the loopholes are identified before the deployment. 

Payment Integration: 

In general, stealing money on Web 2.0  involves stealing valuable data such as credit card information or data that can be used for fraud. This way culprit could make out monetizing incentives from it. Also, web 2.0 attacks are mostly ransomware through which the bad actors make millions. 

In web 3.0, money is built onto the web itself in the form of cryptocurrencies. This makes it easier for hackers/cybercriminals to monetize the attacks. That's why it is crucial to consider security to be top-notch. 

Centralized System: 

As discussed above web 2.0 is extremely centralized. This has significant privacy implications but also means that these organizations own their security and can bring significant resources to bear on securing their infrastructure.

Decentralization has many advantages, but it also has security implications.  With decentralization, decisions are made by an open group and no one “owns” the security of the system.  Governance by consensus is slower than centralized voting because consensus must be reached by all participants in an open forum.  It can be more difficult to force nodes to install updates if they cannot block a proposed change simply because they don't care about it.

In a Nutshell: 

Web 3.0 is still in its infancy, and significant development will be needed before it supplants web 2.0. As the technology evolves and matures, some security risks may be conclusively resolved and others may be created. Web 3.0 security is vital to the success and widespread adoption of Web 3.0 technology. 


Blockchain technology is fundamentally used for cryptography, but nowadays, companies are using it to handle distributed databases and even healthcare. So why are they opting for it? Simple! Blockchain assures security through transactions that are made through consensus and decentralization.

Yet blockchain is prone to cyberattacks like London Hard Fork, where the hacker was successful to steal $50 million worth of funds. So here arises another question, how do we keep the blockchain application safe? Let me walk you through some security frameworks for blockchain applications. But first, let's see some cyberattacks.

Blockchain World & Cyberattacks: 

Blockchain is secure but that doesn’t mean it can not be hacked! History has given us a lot of incidents to witness due to cyberattacks. There were trillions of losses in the blockchain world. 

According to the statistics, the 6 most costly blockchains were hacked in 2021. Another survey showed that manipulating decentralized finance (DeFi) protocols was the fastest-growing method to swipe crypto in 2021. More than $1.6 billion has been exploited from DeFi in 2022 thus far!

So what kinds of blockchain security frameworks are there? Keep reading to find out! 

Blockchain Security Framework: 

When creating a blockchain application it is really essential that all the security measures are taken into account. A complete security-controlled framework for applications contains 

Data Privacy: 

Data privacy is the protection of personal information from unauthorised access and use. It ensures the collection, storage, processing, and use of personal data in an appropriate manner.

Smart Contract Security: 

Rigorous analysis of the smart contract allows the security specialists to go through every line of code and identify any loophole present. Smart contract auditors at BlockApex do automated reviews along with extensive execution of the test cases in search of any vulnerabilities to secure the application. 

Identity & Access Management: 

Identity and access management is very essential as it allows people to access the resources, the application should be smart enough to identify appropriate people to access it. 

Advanced Penetration Testing: 

Advanced pen testing involves a deep security assessment and the latest offensive security approach to discover crucial vulnerabilities in applications before they are exploited. It includes pen testing everything from web apps to wallets and Layer1 blockchains and other assets like bridges, cryptocurrency wallets, web apps, mobile apps, digital custody solutions, cloud security, and APIs. 

Key Management: 

Public Key Infrastructure (KPI) is utilized in the blockchain to verify and confirm the transactions made on the blockchain. Securing the key management function could be an obstacle, as an attacker if found the keys by any means like brute force, side-channel attack, physical access to the system, ineffective encryption, replay attack, etc. They can enter and make destructive choices by swiping millions from it. So it's really crucial to protect the keys. 

Complete Security: 

This includes completing and constantly evaluating the company’s most vital assets, pushing maximum automation, and delivering top cybersecurity consulting and implementation every step of the way. This includes security architecture assessment, code audits, security best practices, custom red team engagements, web application pen-testing, cloud provider pen-testing, API pen-testing, technical security compliance, continuous smart contract auditing, blockchain protocol security assessment, and DevOps.

In a Nutshell: 

Taking all these security measures into account it is highly recommended that security should be top-notched so that the Dapp remains intact and funds safe. 

Did you know that your computer must have been spied on with malicious software at some point or another? 

Well! To begin with the story, around the world there are 90% of people whose computers and other technological devices get infected with spying viruses. But the story doesn’t end here! The sad truth about it is that more than half of the time people are not even aware of such things and along the road, they lost valuable assets. 

Now you see how this can be a great issue! So what you can do about it? What anti-viruses can you use to protect your technological devices?

Although various options are available to protect one’s computer, today, we will specifically focus on VPN. Let’s explore! 

What is VPN & How Does It Work? 

Starting from the basics, in layman’s terms a virtual private network or VPN protects your online footprints by encrypting them such as your browsing history, the files/documents you download, login information, and much more. This usually happens when a user is consuming an open WiFi. 

So how the VPN protects us? Well, the VPN uses a private network that helps users mask their surfing history on the internet, hackers even advertisers can’t steal the data and use it for their means. 

Sounds like VPN could be a potential candidate for prevention against spying. Let’s deep dive more and before coming to any conclusion explore its benefits and drawbacks. 

Benefits & Drawbacks of VPN:

Some benefits which a VPN provides are: 

  1. Reliable encryption i.e no one can trespass and see your activity 
  2. No activity log i.e no hacker can access the activities you are doing 
  3. Usually, they are also available on all operating systems. 
  4. Some of the VPN-providing companies also offer 24/7 customer service 
  5. They also offer high-quality speed
  6. A considerable number of servers covering several dozens of countries. 

But, an interesting twist comes when companies offer all these services with a price i.e Paid VPN. Since free cheese is not even granted to a mouse with a trap, a free VPN doesn’t give all these royalties to its customer. 

From the benefits of the VPN list, we can conclude that if it has reliable encryption like AES-256 and does not run an activity log, that VPN can save you from spyware.

You must be having a question! Is it really true? 

Yes and No! 

Yes, it would protect you from being traced out and being spied on, on the internet. But your service provider will only have access to your IP address. Generally, the service provider does not breach the privacy of the users and invade it or share it with anyone. 

Now focus on the word generally because in some cases the service providers are obliged to provide this kind of information to the government. So if your country's laws include such aspects a VPN service provider is bound to steal your information. 

Apart from that, you must also be aware that a VPN protects you from being spied but if you accidentally download any malware to your technological device VPN won’t protect you there. In order to be fully protected you need to have antivirus software on your computer. 

So, now we have answered your concern, a VPN protects you from being spied but you should also be aware of the limitations that follow along. 

How Can You Protect Yourself From Spying?

Remember, VPN is a smart option in the market that provides you protection against your surfing activities but it can not prevent any kind of malware, or spyware downloaded on the computer.

Nevertheless, let’s discuss some ways in which you make the service more effective.

  1. Do not connect to over-loaded servers, firstly they won’t provide you high-quality speed, as well as the server, could be so overloaded that it may leak your sensitive information.
  2. Change your passwords regularly and keep every password of applications you use different. Although this is not directly related to VPN but surely would help you protect your identity.
  3. Use VPNs that allow split tunneling, that way you can encrypt traffic coming from a specific app through the VPN, and others would be using regular internet. 

Over To You Now!

Now, we have seen that a normal VPN could save you loads from spying. You just need to be careful with human errors and avoid any kind of malware downloads. 


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