What Is a 51% Attack?

A 51% attack is an attack on a cryptocurrency blockchain by a group of miners who control more than 50% of the network’s mining hash rate. Owning 51% of the nodes on the network gives the controlling parties the power to alter the blockchain.

The attackers would be able to prevent new transactions from gaining confirmations, allowing them to halt payments between some or all users. They would also be able to reverse transactions that were completed while they were in control. Reversing transactions could allow them to double-spend coins, one of the issues consensus mechanisms like proof-of-work were created to prevent.

Understanding a 51% Attack

A blockchain is a distributed ledger—essentially a database—that records transactions and information about them and then encrypts the data. The blockchain’s network reaches a majority consensus about transactions through a validation process, and the blocks where the information is stored are sealed. The blocks are linked together via cryptographic techniques where previous block information is recorded in each block. This makes the blocks nearly impossible to alter once they are confirmed enough times.

The 51% attack is an attack on the blockchain, where a group controls more than 50% of the hashing power—the computing that solves the cryptographic puzzle— of the network. This group then introduces an altered blockchain to the network at a very specific point in the blockchain, which is theoretically accepted by the network because the attackers would own most of it.

Changing historical blocks—transactions locked in before the start of the attack—would be extremely difficult even in the event of a 51% attack. The further back the transactions are, the more difficult it is to change them. It would be impossible to change transactions before a checkpoint, where transactions become permanent in Bitcoin’s blockchain.

Attacks Are Prohibitively Expensive

A 51% attack is a very difficult and challenging task on a cryptocurrency with a large participation rate. In most cases, the group of attackers would need to be able to control the necessary 51% and have created an alternate blockchain that can be inserted at the right time. Then, they would need to out-hash the main network. The cost of doing this is one of the most significant factors that prevent a 51% attack.

For example, the most advanced application-specific integrated circuit (ASIC) miner is the Bitmain S19 XP Hydro. It costs more than $19,800 and has a hash rate of 255 terahashes per second (TH/s).

The top three mining pools by hashrate are:

• FoundryUSA, at 54.42 exahashes per second (EH/s); 23.75% of the total Bitcoin network hashrate
• AntPool, at 41.49 EH/s; 18.12% of the total Bitcoin network hashrate
• Binance Pool, at 34.48 EH/s; 15.06% of the total network hashrate

Combined, these three pools make up 56.93% of the network hashrate, a whopping 130.4 EH/s (1.304 million TH/s). To equal that hashrate, the attackers would need more than 511,373 S19 XP Hydros—which would put fixed costs close to $10.13 billion, plus a building to host the equipment, maintenance staff, electricity, and cooling.

Major cryptocurrencies, such as Bitcoin, are unlikely to suffer from 51% attacks due to the prohibitive cost of acquiring that much hashing power. For that reason, 51% attacks are generally limited to cryptocurrencies with less participation and hashing power.

After Ethereum’s transition to proof-of-stake, a 51% attack on the Ethereum blockchain became even more expensive. To conduct this attack, a user or group would need to own 51% of the staked ETH on the network. It is possible for someone to own that much ETH, but it’s unlikely; according to Beaconchain, more than 13.8 million ETH were staked at the end of September 2022. An entity would need to own more than 6.9 million ETH (more than $9 billion worth) to attempt an attack.

Once the attack started, the consensus mechanism would likely recognize it and immediately slash the staked ETH, costing the attacker an extraordinary amount of money. Additionally, the community can vote to restore the “honest” chain, so an attacker would lose all of their ETH just to see the damage repaired.

Attack Timing

In addition to the costs, a group that attempts to attack the network using a 51% attack must not only control 51% of the network but must also introduce the altered blockchain at a very precise time. Even if they own 51% of the network hashing rate, they still might not be able to keep up with the block creation rate or get their chain inserted before valid new blocks are created by the ‘honest’ blockchain network.

Again, this is possible on smaller cryptocurrency networks because there is less participation and lower hash rates. Large networks make it nearly impossible to introduce an altered blockchain.

Outcome of a Successful Attack

In the event of a successful attack, the attackers could block other users’ transactions or reverse them and spend the same cryptocurrency again. This vulnerability, known as double-spending, is the digital equivalent of a perfect counterfeit. It is also the basic cryptographic hurdle blockchain consensus mechanisms were designed to overcome.

Successful 51% attackers may also implement a Denial-of-Service (DoS) attack, where they block the addresses of other miners for the period they control the network. This keeps the “honest” miners from reacquiring control of the network before the dishonest chain becomes permanent.

Who Is at Risk of 51% Attack?

The type of mining equipment is also a factor, as ASIC-secured mining networks are less vulnerable than those that can be mined with GPUs; they are much faster. Cloud services such as NiceHash—which considers itself a “hash-power broker”—theoretically make it possible to launch a 51% attack using only rented hash power, especially against smaller, GPU-only networks.

Bitcoin Gold has been a common target for attackers because it is a smaller cryptocurrency by hashrate. Since June 2019, the Michigan Institute for Technology’s Digital Currency Initiative has detected, observed, or been notified of more than 40 51% attacks—also called chain reorganizations, or reorgs—on Bitcoin Gold, Litecoin, and other smaller cryptocurrencies.

What Is Artificial Intelligence (AI)?

Artificial intelligence (AI) refers to the simulation of human intelligence by software-coded heuristics. Nowadays this code is prevalent in everything from cloud-based, enterprise applications to consumer apps and even embedded firmware.

The year 2022 brought AI into the mainstream through widespread familiarity with applications of Generative Pre-Training Transformer. The most popular application is OpenAI’s ChatGPT. The widespread fascination with ChatGPT made it synonymous with AI in the minds of most consumers. However, it represents only a small portion of the ways that AI technology is being used today.

The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning (ML), which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.

Understanding Artificial Intelligence (AI)

When most people hear the term artificial intelligence, the first thing they usually think of is robots. That’s because big-budget films and novels weave stories about human-like machines that wreak havoc on Earth. But nothing could be further from the truth.

Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. The goals of artificial intelligence include mimicking human cognitive activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning, and perception, to the extent that these can be concretely defined. Some believe that innovators may soon be able to develop systems that exceed the capacity of humans to learn or reason out any subject. But others remain skeptical because all cognitive activity is laced with value judgments that are subject to human experience.

As technology advances, previous benchmarks that defined artificial intelligence become outdated. For example, machines that calculate basic functions or recognize text through optical character recognition are no longer considered to embody artificial intelligence, since this function is now taken for granted as an inherent computer function.

AI is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, and more.

Applications of Artificial Intelligence

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for suggesting drug dosages, identifying treatments, and for aiding in surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits—all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.

Types of Artificial Intelligence

Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon’s Alexa and Apple’s Siri. You ask the assistant a question, and it answers it for you.

Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.

Special Considerations

Since its beginning, artificial intelligence has come under scrutiny from scientists and the public alike. One common theme is the idea that machines will become so highly developed that humans will not be able to keep up and they will take off on their own, redesigning themselves at an exponential rate.

Another is that machines can hack into people’s privacy and even be weaponized. Other arguments debate the ethics of artificial intelligence and whether intelligent systems such as robots should be treated with the same rights as humans.

Self-driving cars have been fairly controversial as their machines tend to be designed for the lowest possible risk and the least casualties. If presented with a scenario of colliding with one person or another at the same time, these cars would calculate the option that would cause the least amount of damage.

Another contentious issue many people have with artificial intelligence is how it may affect human employment. With many industries looking to automate certain jobs through the use of intelligent machinery, there is a concern that people would be pushed out of the workforce. Self-driving cars may remove the need for taxis and car-share programs, while manufacturers may easily replace human labor with machines, making people’s skills obsolete.