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Beyond Hashrate Majority Attacks

Transcript By: Bryan Bishop

Tags: Attacks

Category: Conference

Beyond 51% attacks

Vitalik Buterin

https://twitter.com/kanzure/status/1230624835996250112

Introduction

Alright let’s get started with the next session. Vitalik is from Canada. He is a former writer for Bitcoin Magazine and can often be seen wearing unicorn shirts. He will be telling us about “Beyond 51% attacks”. Take it away.

Okay, so hello everyone. Hi. Okay. I will start by reminding everyone that 51% attacks are in fact bad. Bear with me for a few minutes. Is this better? Okay.

51% attacks

What can 51% attacks do? There are different kinds of 51% attacks and they can do different kinds of things to different kinds of applications, with different consequences and kinds of consequences. The kind of 51% attacks that you’re probably familiar with are reverting blocks. You have a transaction, you publish money to an exchange, you go and trade on the exchange and trade those coins for another coin, you withdraw the other coin, then do a 51% attack that reverts the deposit transaction. This is the major kind of 51% attack that we have been talking about over the last 10 years and it’s the one that has been the most thought about.

Transaction censorship

Transaction censorship is also another kind of 51% attack. Transaction censorship is particularly dangerous in light of layer 2 protocols and DeFi which are both recently big trends. In the context of many layer 2 protocols, including plasma, channels, general state channels, lightning network, optimistic roll-up, interactive computation, TruBit, censorship = theft. So if you can censor challenge transactions then you can steal money from people. This doesn’t apply to zkRoll-up but it does apply to the great majority of protocols that people are thinking about now. In the context of DeFi, censorship is particularly dangerous because it’s a tool where you can do market manipulation and extract value. If you can censor every transaction going to the ethereum blockchain touching Uniswap, and wait a day, then the chances are that the ETH price will move a bit and I’ll be able to extract a huge amount of arbitrage value out of that attack.

Censorship is dangerous, and in DeFi protocols and layer 2 protocols then transaction censorship can also be considered theft.

Lite clients

Many people are running lite clients. If you’re running a lite client, then a 51% attack could lead to those lite clients accepting a chain that contains blocks that contain totally invalid transactions. Bad signatures, malformatted transactions, signatures that without authorization– just steal money from one account and move it to other accounts. They can get available blocks accepted by the network.

Data unavailability attack

Who here is familiar with the data availability problem? Okay, not all but many people. The idea here is that you can make a chain where you publish the block headers. Lite clients see the chain. But you don’t publish some or all the data in the block bodies. The reason why this is bad is because if the data is not published, then it might be correct or might not but there’s no way to generate a proof to prove to anyone else that it may be correct, and you’re denying people information that they might need to create future transactions. Unavailable blocks are also dangerous.

Discouragement attack

Discouragement attack is a term for griefing other participants and cause other participants to lose revenue and drive them to become part of your pool or drop out. Selfish mining above the 1/3rd mark is an example of this. 51% attacks are extremely powerful discouragement attacks, too.

Immutability

So, 51% attacks are powerful. 51% attacks are a threat to blockchain immutability. Who here remembers this? A pile of money got stolen from an exchange and they considered pushing for a day-long reversion of the bitcoin blockchain in order to get the money back. If things like this are possible, then things inside of blockchains can get reverted and blockchains lose the key property that makes them blockchains and this is terrible. Also, 51% attacks are not democratic they are plutocratic so there’s nothing good about them as a democratic method.

In practice

51% attacks can be done. This is a photo of a panel from Scaling Bitcoin Hong Kong 2015. 90% of bitcoin’s mining power was all conveniently in this photo sitting together in this pose saying “I got the powah”. Ha ha.

51% attacks have been done. Ethereum Classic, Bitcoin Gold, and others have been 51% attacked. I’m sure I’m missing one or two. It’s been happening.

Spawn camp attack

The kinds of 51% attacks we have seen aren’t even the worse. Spawn camp attack is like the worst case nightmare scenario for a 51% attack. Basically, get enough hardware to attack a chain, attack the chain, wait for it to recover and then attack again because you still have the hardware. Eventually the community gets fed up and they change the proof-of-work algorithm, and they don’t have time to build ASICs so you just rent lots of CPU and GPU hashrate power and just keep attacking, and then it’s dead until they switch to proof-of-stake or centralization.

Can proof-of-stake break the cycle?

This is a blog post I made in 2016 where I tried to describe a philosophy behind what proof-of-stake is and why this is something that is natural that we should expect to exist and why it is something that makes sense to get behind. There’s an asymmetry in proof-of-stake that says unlike proof-of-work where you only have rewards and so your penalties for participating vs not participating in an attack are only as large as the block rewards. In a proof-of-stake system, your attacks can be detected and you lose your deposits which is way larger than your stake can be slashed.

These got put into CasperCBC and a bunch of other proof-of-stake algorithms that are based on security deposits and slashing. Here in theory the goal is that a 51% attack becomes extremely expensive. But basically in order to attack a chain, you need to buy up a bunch of coins, you need to get more than 50% of the deposited coins in the system, and then when you get caught you get slashed, and if you want to attack again you have to buy more coins and because you keep buying then the price of this chain you really hate just keeps going up and eventually you get bankrupted, instead of the attacker going PoW we have the chain going PoS.

What about other kinds of attacks

Here we get to the first problem, which is what about other kinds of attacks? We have been focusing on finality reversion so far. This is fairly common from byzantine fault tolerance consensus theory. The basic idea is that if 2/3rds finalize one side and 1/3rds finalize another side… 1/3rd of all the validators have to make two contradictory messages and you can detect this and penalize them. This is about reversions, though.

What about other kinds of attacks? Data invalidity, data unavailability, censorship, and griefing.

Let’s collapse the problem a little bit. We’re not going to care about data validity. We’re going to notice that if you have guarantees of data availability and guarantees that if a block is part of the chain then all data in that block can be downloaded by a node in the network. If you have censorship resistance and you publish a lbock then it will eventually get included, and from these two things you get validity. The reason is that interactive computations, roll-ups and these existing protocols- basically you can just have a roll-up and for the roll-up you publish all that data on-chain and then the chain guarantees its availability and you have some fraud proofs and guarantee that if some computation is invalid then you can publish an uncensorable fraud proof that will get on-chain and get processed. So data validity isn’t as much of a concern because layer 1 or layer 2 fraud proofs can solve this.

If you can’t censor, you generally can’t grief because if you can’t censor other people’s blocks, then you can’t prevent them from getting included on chain. So as long as the incentives don’t penalize more limited forms of censorship that are still possible too much, then the griefing is also not going to be too much.

We’re down to two things: data availability attacks and then censorship attacks.

Data invalidity and unavailability

This is a paper that I wrote with musalbas and some others in 2017 where I describe this scheme for basically allowing clients of a blockchain to verify data availability of that blockchain without actually downloading all the data.

The simple strawman version of this scheme is like this: the dumb way to check that a block is available is to download the full thing. But here we are assuming a scalable, possibly sharded blockchain where you have more than 2 MB/second of data flying around on-chain and clients aren’t going to be able to download the whole thing. What we’re going to do for a client that wants to check data availability, it’s going to do a random sampling test. It will randomly select some pieces of data, like 30 pieces, 40 pieces, 80 pieces, just choose your security margin. It randomly selects the positions, asks for merkle proofs of those positions, and you will accept the block as valid as long as you receive valid replies for all the positions you accepted.

If you accept a block using this scheme, then you probabilistically know that with some probability that the block is valid. If less than 50% of the data is available, like if everything to the left of here is available but everything to the right of here is available, then at least one of your checks is going to fail with high probability.

With this kind of scheme, an attacker has the ability to trick a small number of specific clients. But if the attacker tricks more than a small number of clients, like enough clients such that the leaves they download makes up half the data, then those clients can go ahead and reconstruct the data from there.

This doesn’t prove that the block is completely available, it might be missing one piece, but it does prove that at least half the block is available. It would be nice if we had some technology for recovering a whole block from only 50% of the data. Hmm.

Erasure coding

This is erasure coding. So we’re going to take the data, pretend it’s evaluations of a polynomial, we will evaluate the same polynomial at even more points, and now any 50% of the data is going to be enough to recover everything. Now what we have is this scheme where you can verify that blocks are available and blocks are potentially very large sizes are available while personally downloading somewhere between 20 and 200 kilobytes of data.

This is the first part. This covers data availability attacks and it’s part of eth2’s sharding solution. This at least allows us to give sharding blockchains the same kinds of availability guarantees and through fraud proofs validity guarantees that existing non-scalable blockchains that demand everyone downloads everything already have.

Proving correctness

How do you prove that the root is a root of an erasure coding of this? How do you prove you haven’t stuck junk data in there? You can use fraud proofs, like this two-dimensional one where if you encode anything incorrectly then someone can make a short fraud proof of this and you could broadcast this to the network and the network can reject the block. This 2D scheme was from 2017. There was recently something about coded merkle trees where you encode every level in the merkle tree, which has nice properties. There’s also approaches that do not depend on fraud proofs, like using a STARK or a SNARK to prove that the merkle root has been correctly computed, and the other possibility involves polynomial commitments– you would have your data, and you would interpret it as evaluations of a polynomial, you figure out the polynomial, then you make a whole bunch of openings of those polynomial commitments at a whole bunch of points, and your data availability check would be to ask for instead of 80 positions then 80 openings or you could get more efficiency if you use clever algebra to get some kind of multi-opening.

The nice thing about these schemes is that they don’t depend on fraud proofs so the scheme for verifying block data has been published doesn’t have any extra built-in latency assumption anymore.

Sharding: beyond committees

Sharded blockchains that exist today generally depend on committees and the curent idea for how these work is that you have a whole bunch of nodes, you randomly sample some of the nodes and you need some majority or supermajority of them to sign off on some block for the network to accept that block as valid.

The problem is that any type of committee-based scheme is going to be censored by bad actors that go above some threshold. If we talk about resisting 51% attacks, then we want to talk about a system where even if a majority starts attacking then a minority should be able to continue operating the system itself.

Sharding with a fixed threshold isn’t really going to help you. So the solution here is that instead of relying on committees, then protocols need to rely much more fully on the set of data availability checking schemes.

Censorship

Who here wants to prevent censorship? Okay. That’s a good number of people. So, the status quo is not good. This is a post by nrryuya which has done some work on formally verifying ethereum things. He wrote a post that says there exists strategies by which a majority can censor blocks in the current eth2 design where that censorship is indistinguishable from single block latency and it’s really hard to attribute who is responsible.

The attack here is pretty mean: what the attacker does is he sometimes censors other people’s blocks and just puts their weight behind blocks that do not have whatever thing they are trying to censor, but the attacker itself also sometimes publishes things that contain the thing they are trying to censor but they publish it one second late. The attacker kind of takes some of their validators and uses them to vote for another block and uses some other validators to vote for their own block that actually contains the thing they want to censor… but their own votes don’t have enough to go over 51% so the thing they are trying to censor never actually gets included. This is multiple levels of indirection. In eth1 and eth2, a 51% attacker can censor and it’s kind of difficult to pin down when enough censorship is going on that it’s worth trying to do something about it.

Uncle inclusion

Here is the bare minimum thing we can do: uncle inclusion. The idea here is that basically the thing we do in eth1 where blocks that are not part of the chain can be included later, except unlike in eth1 we add in protocol rule that says transactions in an uncle also get processed.

So you have a chain, you have a block going off as a stale, and then this chain, and then this block gets included as an uncle over here. When you process this block, you process all these transactions and then you process these, then you go off and process the rest of the chain.

This has already been done to some extent in DAG blockchain protocols and those things are good. But the general idea here is that this makes total censorship much more distinguishable because in order to totally censor blocks that contain transactions you don’t like you would have to prevent them from getting included in the chain all the way up to the uncle inclusion period which you could in theory make as long as you want, so censorship becomes indistinguishable from latency up to some period.

Idea: timeliness detectors

Imagine if we could have at least clients that are online, so clients that are on the network downloading things and regularly communicating to other clients. Have them detect whether they saw a block arrive on time.

If they see that a block arrived on time, and they see that that block has not been accepted into some chain for a really long number of blocks, then that block is automatically disqualified. If a block is not on time, then potentially you could use this to do reversion 51% attacks as well.

The idea is that clients locally detect whether they see a block as having arrived when it should have arrived and they use this as information in determining what chain to follow.

Not every node is going to be able to follow the protocol because offline nodes exist too. Unless a 51% attack is actively happening, then if there’s no attack happening then the chain that is winning is going to be a chain that is fine anyway and if an attack is happening and you’re offline then you pretty much would have to check the social layer to see what’s going on, but only a small number of actors would have to do that and everyone else would have a pretty clear consensus.

Problem: edge attacks

What happens if you make a block and whatever definition of that block arriving on time, some nodes are going to see it arriving at different times. So nodes might disagree about whether a block was censored for too long, and whether a block was published on time, and they might disagree on these timing parameters.

An attacker can deliberately publish a block to maximize this problem. They can do lots of things. They could even create long-running disagreements about whether an attack is happening, and wreak a lot of governance problems.

So let’s have better timeliness detectors.

Improved timeliness detectors

We’re going to go back to the Byzantine Generals' Problem by going back to this Leslie Lamport paper from 1982. As it turns out, this paper contains an algorithm that people don’t really talk about but people maybe should talk about a lot more. It has this sentence hidden in the abstract with unforgeable written messages (meaning digital signatures) the problem is solvable for any number of generators and traitors. Lamport here is claiming that he has a consensus algorithm that is fault tolerant up to 99% faulty attackers.

This algorithm works, but it has a catch. The catch of this algorithm is that it only works as long as you have synchronicity assumption not just between the miners and validators doing the consensus, but also between the miners and the clients and between clients and other clients. It has a much stronger assumption about who is supposed to be online, and it uses this assumption to get a higher level of fault tolerance, but it’s a really serious assumption and we’re not comfortable making it by itself.

I’ll describe a version with a single attester. Here, assume the single attester is honest and then we will generalize it and we can have n attesters and we only need 1/n attesters to be honest. The idea here is imagine a block gets published, then clients and attesters have a kind of deadline by which they need to receive the block in order to consider that block as being on time. For clients, that deadline is going to be t, and then for the attester that deadline is going to be t + delta. So the proposer here is going to publish b and we’re going to assume the proposer is a bit evil and they attempt an edge attack and node 1 sees it before the deadline and node 2 sees it before the deadline. The attester sends the block, but because of the network synchronicity assumption, if even one node accepts the block then that means the attester is guaranteed to accept the block before their own deadline because the block can be sent over to the attester. A block plus a signature has a deadline that gets offset by two delta. If even one client sees a block as being on-time, then the attester is guaranteed to see it before their deadline and so the attesters add their own signature and because of network synchronicity, the other client is going to see the blockplus a signature before the deadline for a block with one signature.

The way to extend this to multiple attesters is trivial. You have lots of attesters. And clients are going to be t plus delta times k. I have a post on eth.research about this so if you want more details feel free to check this out. That’s the approximate idea. You have a set of attackers each of which who can delay the deadline by a bit, so if a block gets received by one then it propagates the timeliness of a block to the rest of the network as long as the network delay is below whatever your delta parameter is.

Fun fact

If you have this kind of timeliness detector, then they give you a blockchain on their own. The protocol is simple: you process all timely blocks in order of self-declared time, and that’s it. The only problem with this is that it requires a really long block time. This might be an interesting protocol to use if you want to process validator deposits, withdrawals, or slashing, but it’s not the best protocol for running a blockchain.

More realistically

You could detect attack chains and censorship attacks with timeliness detectors.

Summary

The anti-censorship technique is less perfect than other techniques is because it does depend on a synchronicy assumption between network and clients, and you can set this duration at whatever you want, but it is related to what level of censorship you’re willing to tolerate. You can get a guarantee of agreement between the set of nodes online, and in the event of an attack you can get consensus on whether a chain is an attack chain and all these other things. You can use this as the beginning of a partially social consensus about which way to recover, and this helps you assign stronger and higher penalize to attackers since you can more reliably identify who the attackers are.

In general, the mapping to attacks, if the attacker publishes an unavailable chain then the data availability checks will catch it; if they publish invalid blocks, then fraud proofs can catch that; if you censor blocks for a long time, then the chain automatically gets ignored by the network. If you censor blocks for a medium time, then you can use timeliness detectors to cleanly force things into the short case or the long case. If the attacker tries to not participate or break committees, then the response is to not use committees.

There we go. We have a collection of tools that essentially lets us fear 51% attacks much less, and either ignore or recover from many kinds of them. So thank you.


Sponsorship: These transcripts are sponsored by Blockchain Commons.

Disclaimer: These are unpaid transcriptions, performed in real-time and in-person during the actual source presentation. Due to personal time constraints they are usually not reviewed against the source material once published. Errors are possible. If the original author/speaker or anyone else finds errors of substance, please email me at kanzure@gmail.com for corrections or contribute online via github/git. I sometimes add annotations to the transcription text. These will always be denoted by a standard editor’s note in parenthesis brackets ((like this)), or in a numbered footnote. I welcome feedback and discussion of these as well.

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