Below is a copy of a security report submitted to Ripple Labs on 18th May 2015. Unfortunately, after more than three months, no fix has been released and no acknowledgment of whether the exploit is regarded as neeeding a fix has been given. Fair disclosure suggests it is better to make the exploit public after this period of time. Github links have been updated to the current code. Unfortunately JIRA links are now broken due to the bug tracker being made private.
Full disclosure: I have received numerous bounties for security and bug reports in the past and was employed as a contractor for Ripple Labs for a period of time.
tl;dr It is possible to "mine" transactions ids which give you a better chance of exploiting the state of the Ripple ledger for financial gain.
Exploiting Ripple Transaction Ordering For Fun And Profit
Ripple transactions are serializable to a custom binary format that is cryptographically hashed and then signed by one of two schemes (ECDSA and EdDSA). This signature is then inserted back into the binary serialized form which is hashed again to produce a transaction id through which it can be uniquely identified. Transactions are submitted to a rippled node, which verifies that the signature matches the public key and the hash of the public key (account id) and if the signature is good, it disseminates the transaction to the other rippled nodes operating on the same network that it knows about. These instances pass that transaction on to nodes that it did not receive it from and this flooding should result in it being considered for adoption in a ledger by the validators running in the network by the process of consensus. All non-validating nodes do actually process the transactions, but only the agreed ledger of the trusted validators counts for all intents and purposes.
Consensus requires that these transactions be ordered so that all validators can apply them in the same order and reach the same result. There is some confusion amongst the community and no conclusive documentation on this ordering. So, as is often the case, we need to read the code. First, let’s look at an example ledger that show some interesting characteristics:
SELECT at.TransId, ...> at.TxnSeq, ...> at.Account, ...> t.FromSeq ...> FROM AccountTransactions at ...> INNER JOIN Transactions t ...> ON at.Transid=t.Transid ...> AND t.FromAcct=at.Account ...> WHERE at.LedgerSeq=13304583 ...> ORDER BY at.TxnSeq; TransID TxnSeq Account FromSeq ---------------------------------------------------------------- ---------- --------------------------------- ---------- 013BDF50F3D6FE5684935D1BB63DDE5DF3994C533F87D4C5C8DCF37047C42B71 0 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18514 03415480457F5BDDC6FDE019A9BB6E6B8DF45A1111375B08CAB64A099C8C01D1 1 rfRQfYGrs8BoyS4duDjWDH11yq5JVX46A 32642 03C181BF1A23A85521970EBF5A144E09B74FC0D6B0A9C7CC4B11EEA14C8C5794 2 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18515 044F9A749F75545937FF37560A8EF325D2EDED25E44AD59FD3B5E01CD8D43A01 3 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18516 079E0378F417F1380FF4BB25A5865DC749FF145B2C1FCCCD02A740EC8AC06028 4 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18517 0829279565D00E649CF673E36FE599F6F0D421175C9FCF04CF97CACC8334F054 5 rEaWE52vbfqoHCbaidH4oE32DYfbQtw9c 144527 0999C5ACA9588F003A5676DEECA3FE7D8B02605F6706C33E6BA72CB8820F5103 6 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18518 0BC5C5BC792A2AE9B5B5733BE017041143C5FAC902BCE80095C00DA9FDF64729 7 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18519 0C2DD73CB47045E52F4D9F1DAAD42A9E375EC3502F7F5FF24FAA5D2EF6B34942 8 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18520 0C67BE5505C301A4B78853BDA1A8167C0BF61EF040AC2C67278329054581DD48 9 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 18521 44FCFD01470516ABBD293C2414A4F5B61408F53B5FD8CD69EDFD2DC8024DFFF3 10 rBN3aEzHtDMPtkW2ci1PJNFxXLi4DUT1E 76770 5CCCC475B697777C251578B7360202C33052753C9114C088229FB8E110D487AF 11 rKkhd9rTV2fJR59DgsFQG67GFcqoNVgSN 10352 8C644A85749C0C5618B7D7DA7BAAFC1F9779118DBCCB3D328FC9ADA44664DD83 12 rDaeHQrL8bEbPuQGx7rkb1vCwUbPqGdDt 22113 97B42105AE8154B80B7065559DF2EE82C4F470AE3A582855FEAE635C8E5DC9EA 13 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270095 A968BBA4412E7D6358520DC3E7702079BD35DCABB67AAEA96CC9675D9EF61FD3 14 rDN91UzG9HyDyGuoNpuyCxKdw1FcFA9bA 41608 AB506718DE4CA503DAF42D562ABC407B882428F5C658D65B4605A9D21ADC6F50 15 rEaWE52vbfqoHCbaidH4oE32DYfbQtw9c 144528 AE03E8C3097CBA89F1F30A788553321BD1472E7EB7885A9545A0C185CB7E586A 16 rDN91UzG9HyDyGuoNpuyCxKdw1FcFA9bA 41609 C5837574AC71F9F12C5EA1D32D8F74CF53FC1B6FC7FEC8F2CB459AE19176DC6F 17 rEqm4WyqHqHoLYKM9fwDkpnK3Adi2yJdD 23391 C9C63127696EBBA8D4115B4BD672C64B85E7B9C6CBCB5536A2F081A2A9F0F1C0 18 rUY4bFSz4y6gsGvedGmKoLngMnvMoawej 42738 DA2A6FB112C476A264EE11A0C616B71F5F578A84AF986A9A13F79855109C5AE0 19 rGcSxmn1ibh5ZfCMAEu2iy7mnrb5nE6fb 14931 F8D9BF6ECA2083482C27A4CF45A25690427671E5928A7E740A4691BEF91A6AA5 20 rBN3aEzHtDMPtkW2ci1PJNFxXLi4DUT1E 76771 064F9CD745883CD69F2284ECE789827232EF5A339FDAF78CD260D60BA1F2EDF2 21 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270096 FA51521273C46C5984693BBB48E3421AF07D51C2D303C48365F7AB5630D689D6 22 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270097 A29C7AB2CEE16436B367DCB0C810127135DD0C5524A84854853CD67EC0CAFF58 23 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270098 9B3E3F55F0D5BA7A6FD962154E28FFE886D36E902CBD50D64DD01DA2A5DD513F 24 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270099 E47F0AC7185B9EBD920D85D15296E80E66EC559D6812D893DBA4E315BA2B3B11 25 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270100 24C80472F6716CC2B1211A665B3CF14D9775A6C74337D59C70156F4E3447AF48 26 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270101 0A66B53BBB40A5057CADAFA0FFB0AC2BAB41D2889D1DAD6178D41DD023CD2DD6 27 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270102 D44DD02D5A454F16ABB90EAE71BBB2F90549BB2764F8D12B117F646E34BAE4A9 28 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270103 0AC2D4A67D6349E2ECF1722BA6A9AA45F02195DA0EB2B4417AEBDE767A7E7EB7 29 rfCFLzNJYvvnoGHWQYACmJpTgkLUaugLE 5270104 4465966BBE18C0038FE5CEDA93E1E3D437258881D76678353D209F4841185EED 30 rDaeHQrL8bEbPuQGx7rkb1vCwUbPqGdDt 22114 578214C13AD9FFA75EBD721BA67EE33AFF0570284FEF292B207F80FAF8D31BD0 31 rEaWE52vbfqoHCbaidH4oE32DYfbQtw9c 144529 94A52C051E01A91282955DF70284288DEAE4B4555315681C52AB9CD31827D725 32 rGcSxmn1ibh5ZfCMAEu2iy7mnrb5nE6fb 14932
- Transactions 0 to 20 are ordered by transaction id, with multiple accounts being intermingled, but for each account, the account sequence and transaction id have a congruent order, this being the default case for an account with a single transaction in a particular ledger.
- Transactions 21 to 29 are transactions for a single account, this time with transactions being ordered by account sequence (note 5270095 though).
- Transactions 30 to 32 are for accounts that already have transactions earlier on, but for which the transaction id is lexicographically before the id of one of the earlier transactions for the same account.
So back to the code. It is tempting to say that the linked code is almost impossible to understand without the aid of a lot of logging. There are three places where transactions received from the network are applied to a ledger:
But as far as I can tell it is only the application on line 1009 that actually matters for the transaction ordering of a closed and final ledger. So if we move into applyTransactions() we can see what decides that order:
Iterates set in transaction id order, putting any failures of the transaction engine into retriableTransactions which is seeded by the root hash of the transaction ShaMap:
and ordered by a XOR-ing of the transaction root hash with the account id, by account sequence and then (irrelevantly?) transaction id.
These retriabletransactions are then iterated up to three times to apply them to the end of the ledger. In some ways it doesn’t really matter what anyone thinks the code is doing or is intended to do (the code itself makes that task very hard), only the observed behaviour matters. Which brings us on to a pair of exploits that take advantage of this observed transaction ordering behaviour.
Advantageous Arbitrage Transaction Placement
Arbitrage is the taking advantage of different prices in different markets to collect the difference minus any applicable transaction fees. It’s free money that doesn’t last for long and its effect is to remove liquidity and at the same time bring equality to markets and inform speculators of correct prices given the state of all markets. The Ripple network is ripe for arbitrage given the abundance of currencies and multiple markets for each of those currencies. The problem of finding opportunities can be speedily solved by recursively applying a FIFO queue-based Bellman Ford algorithm to a graph of the negated logarithms of the ratios of the funded tips of all order books to find negative cycles and removing the smallest offer in each cycle on each recursion. For each path apply transaction fees and see if gain, given available liquidity of the account and the path, is greater than cost. This way many, and indeed complex, paths can be found (trade secret revealed!).
However, arbitrage is a race and there are two ways to win. Find paths that no-one else has found or exploit paths before anyone else does. The author was initially motivated by the first challenge but soon also found a solution to the second. The exploit follows:
- Build a list of profitable paths.
- Build OfferCreate transactions for each path with
tfImmediateOrCancel|tfSellflags set and append them to an ordered list.
LastLedgerSequenceto current ledger+1 (+2 can still work when the market is busy and submission times are universally slow).
- For each transaction set
SourceTagto an incrementing counter (nonce) between 1 and n, sign the transaction and determine the transaction id.
- For first transaction select the nonce which yields the lexicographically lowest transaction id. For subsequent transactions select the nonce which yields the lowest transaction id greater than the id of the previous transaction.
- Sign once more and submit transactions to the network.
The effect of the above process is that with high likelihood your transactions will be processed before those of most other accounts in a ledger, dependent on how high your value of n is. If another arbitrageur has found the same path and submitted similar transactions, they are more likely to fail in their aim of exploiting the path as maximally as you. Of course this depends on the liquidity of the path and the available funds of each arbitrageur.
Obviously there are tight time constraints to find opportunities in a ledger, sign number of trades x n and make sure all the transactions reach the validators before the next ledger closes. For this reason using a programming language with a fast native version of the signature generation scheme helps. In benchmarks, using an optimised native Ed25519 implementation, it has been found that a path of 7 transactions can be signed 256 times each with a different nonce in 360ms to “mine” a preferable transaction id. The author is certain that this benchmark could be improved. Following is an example ledger where many arbitrage bots are fighting to exploit the same opportunity. One aptly named account seems to be winning the race, but see later analysis of all accounts for some interesting observations.
SELECT at.TransId, ...> at.TxnSeq, ...> at.Account, ...> t.FromSeq ...> FROM AccountTransactions at ...> INNER JOIN Transactions t ...> ON at.Transid=t.Transid ...> AND t.FromAcct=at.Account ...> WHERE at.LedgerSeq=13432605 ...> ORDER BY at.TxnSeq; TransID TxnSeq Account FromSeq ---------------------------------------------------------------- ---------- ---------------------------------- ---------- 0014BE643C33325E2484C7AB4E86BD86C0015847199566A7CC7658DBD663E57C 0 rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 16434 0100B6409A32F45C73FA26365EC234F34B16011A8F22B2A52DA1BAD8F0D33244 1 rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 16435 03603F21BCC62C4E2E47A023B286E31A4FA78ACF5A0BB4930CACA7DEC0265734 2 rfTiZ53FW6aXcY9TFXQbZMsHQeP4h6Dgkh 395872 041436C67D9FF4504391D88620BCE1755EA7D67804D609B839CA2F88D3CF891C 3 rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 16436 042CB042F781ABFE30BC7DD3AB5EA6444DC7EF0AE389FE7CD3E52F6D9E151A83 4 rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 16437 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6741CED9D585CEADCB0B1953F3A3AA40916579E3874F4CDA5F4C11364C2CA7A9 77 rn3x9UZcrb6tEWzu7nXSMEtJsHX2tT6xuF 244295 2A1CAFBE7ACBA2876243D1090CFFC1A7EC543C6E55C0D3C911E09B4C05E7DB6D 78 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129520 818899490B360D3C83693CAE057DBE0F90589EC942B0D203FBB12D769A0EDD59 79 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129521 1E2325594547696BF988358C9CD2A31B9D997895307DCADDFCE396D4B70BA1EC 80 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129522 AB886755511D069CC086E61194EF563B69881B69320DBFCB464B17EACAEF6204 81 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129523 5DABC2F63D0B2E98310C499377CEE8FFFAC9DB2A61DFDDA7C3AD545CC1E5AB87 82 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129524 73015BF2D2991633636A380487B36DF448F281995ECEB0EAA31ABFAFDF9F0523 83 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129525 72CDB07181629CAAD7C68374AAA3E1CC0418326A9BD7C5502EE20987950CEAB2 84 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129526 1ACCAFF7797275B81BB209554005E6AA357042D488DB41ACD8FC6C32F7579F5E 85 raT8RBKLgP3WmKtCy5TWjcUrPUBEBfgJ3k 129527
To summarise and generalise the exploit, to get your trade(s) considered first in a ledger, make sure that your transaction ids are as low as possible, using a nonce (SourceTag, Memo or tiny fractional amounts on TakerPays or TakerGets), and make sure the order of the transaction ids matches the order of the account sequence field to avoid relegation to the retriabletransactions doghouse at the end of the ledger.
Large Trade Front Running
As discussed at length in the book Flash Boys, given enough relative time advantage, it is possible for one actor to discover a large trade and execute other trades in advance to take advantage of that initial trade executing, often to the disadvantage of the original trader. This is a controversial topic that has created much anger directed towards High Frequency Trading. Ripple is hardly suitable for HFT, but it does share one thing in common with HFT markets, and that is that there is latency between a trade being known to the network and it being executed and applied to a closed ledger. Given the previous exploit of being able to manipulate transaction order a slightly more terrifying exploit presents itself:
- Monitor all incoming proposals for OfferCreate transactions.
- Check funding of transaction against in memory, up to date record of all account balances (using ledger_data and AffectedNodes).
- If OfferCreate crosses completely at least one other Offer, create an OfferCreate transaction A to fully consume the first existing Offer. Create OfferCreate transaction B to sell acquired asset at the price of the next highest Offer in the same order book minus a very small fractional amount.
LastLedgerSequencefor A and B to be current ledger+1.
- “Mine” a transaction id for B which is less than the transaction id of the incoming proposed transaction, but as close as possible.
- “Mine” a transaction id for A which is less than the transaction id of B, but as close as possible.
- Submit A and then B.
The above exploit effectively lets you buy an asset and then sell it at a higher price with the assurance that it will be bought in the same ledger. The effect of this will be that the original buyer will receive less of the asset than he or she might have expected. The wider the gap between the funded tip of an order book and the next funded offer, the greater the profit. The exploit could be extended to consume multiple offers dependent on the size of the incoming proposed transaction. There are risks to the strategy, in that it possible that the incoming proposed transaction might not make it into the expected ledger (disputed process) but experimentation would probably provide more data on that likelihood. This exploit is unexploited by the author.
Risks, Mitigation And Observations
The two above exploits would very likely undermine public confidence in a market as operating “fairly”. Defining fair is a hard task, especially in a distributed context where latencies are wildly variable. There is no such thing as strict time ordering on a global scale of independent machines each with their own clock and network routes to each other. However, the current system clearly favours those with an understanding of the protocol’s signature generation scheme.
What’s a better solution? Well, strictly ordering by Account and then Sequence with the Account order seeded by a root hash of all other transactions to be considered makes a lot of sense, as any new transactions changes the order for all other transactions and is very hard to know for certain unless your transaction is the last one to be submitted to the network in time for that particular ledger. Why this isn’t the case already is a source of confusion. Perhaps the end of ledger “reshuffling” is an attempt to solve the order of dependent transactions, for situations such as a funding payment for an account and that new account creating an Offer in the same ledger. Disallowing edge case scenarios like that would certainly simplify things.
The key observation and suggestion I’d like to make is that it is perfectly reasonable for a trader to want to make a series of transactions which are dependent on the outcome of the previous one, to take advantage of opportunities such as arbitrage. However, the current unpredictability of which transactions will succeed and which will fail means that there is a high chance of getting stuck with an undesired asset, which is why this exploit is so useful. It vastly increases the chances of success of all transactions based on a fixed model of the previous ledger. However, it would be preferable and more “fair” for the chain of transactions to always all succeed or all fail, and the chance of success is based on a lottery of account ordering. To achieve this requires a unit of atomicity greater than a single transaction.
Payments, on paper, are meant to offer this. But they don’t have much flexibility at all. As far as I have been able to determine, creating custom paths which hop between more than two order books is impossible. The condition of using an account with rippling enabled as an “in-between” stage is nonsensical when the trader does not at any point want to hold the “in-between” asset. It seems like an awful lot of complexity was added to facilitate the idea of temporary credit in payments, but is poorly understood, hardly documented and used by very few people and results in not very much gain. Study of the ledger history shows that Dan Miller/pigeons is about the only person who really tries to make use of it.
What would be much more ideal is being able to submit a batch of OfferCreate transactions which either all succeed or all fail. Either that, or Payments are modified to support circular paths along up to say 16 order books without the need for rippling enabled intermediaries. Payments are better in the sense that they have support for the flag combination
tfLimitQuality|tfPartialPayment|tfNoDirectRipple, which is exactly what an arbitrageur would want if it actually worked for a circular path!
There is some discussion here: RIPD-829 but the explanation doesn’t really add up. If the author can calculate a sequence of offer amounts that take the maximum available liquidity from a chain of existing offers, why can’t rippled? The only challenge for the arbitrageur should be then to find the best possible paths, initial input amounts and minimum expected gain (sendmax/amount ratio) and see if anyone else’s payment with the same path gets picked first by virtue of the account ordering. An extra benefit of this would be much reduced transaction size, many OfferCreates reduced to a single Payment.
In The Wild
The author has demonstrated that the transaction ordering can be fixed with two accounts. Initially
roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ used ECDSA signatures to “spin/mine” transaction ids that ensured best placement in a ledger to exploit arbitrage opportunities. It soon became apparent that EdDSA would offer even faster creation of desired transaction ids and so
rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 made use of these features:
Within the space of a few weeks around 900 XRP has been turned into 57,737 XRP, “unfairly” displacing other arbitrageurs’ chances of success in the process. The next obvious question is whether this exploit is known to others. The answer is yes:
SELECT MIN(LedgerSeq),MAX(LedgerSeq) FROM Transactions; MIN(LedgerSeq) MAX(LedgerSeq) -------------- -------------- 13243962 13483930 Unfortunately not much history... SELECT Account, SUM(Zero), SUM(NotZero), COUNT(*), CAST(SUM(Zero) AS REAL)/CAST (COUNT(*) AS REAL)*100 As Percent FROM( SELECT FromAcct AS Account, CASE WHEN SUBSTR(Transid,1,1)='0' THEN 1 ELSE 0 END AS Zero, CASE WHEN SUBSTR(Transid,1,1)='0' THEN 0 ELSE 1 END AS NotZero FROM Transactions ) GROUP BY Account HAVING COUNT(*)>32 ORDER BY Percent DESC LIMIT 30; Account SUM(Zero) SUM(NotZero) COUNT(*) Percent ---------------------------------- ---------- ------------ ---------- ---------------- rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 17924 2 17926 99.9888430213098 roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ 6512 67 6579 98.9816081471348 r3pFpfYfCoj9Sm5cpRwc4iD4mFPoF9HDVf 36 8 44 81.8181818181818 r2d2iZiCcJmNL6vhUGFjs8U8BuUq6BnmT 1195 3604 4799 24.9010210460513 rBApdygJewirS7hmF3rsSDfN9VvLiJ4uM8 6 28 34 17.6470588235294 rwzNnh868djfPPsN77GYwH8xYbiAXoKW3E 7 34 41 17.0731707317073 r93NwTGcTMXVAJmujHHYihezSxPLuRym5M 5 29 34 14.7058823529412 r4PowrZ7KZw83oWDYxzY82ht2kgDmFUpB7 8 48 56 14.2857142857143 raL1xU5rggQZ8cw1MUhiVRF7NzNsy7UyLB 9 55 64 14.0625 r42Ccnr9HS77UciZ8kkUe3dRsY7tXqm5M3 6 37 43 13.953488372093 rhotcWYdfn6qxhVMbPKGDF3XCKqwXar5J4 5 31 36 13.8888888888889 rHDQcQNWTV1jrBNyLJoeYDiL7Z9CbPyjLF 6 38 44 13.6363636363636 rBRXcf7BYs2CN7GfAAXjLQPEh7d46BP9RE 5 32 37 13.5135135135135 rUgsWsFYsLJwK1YCJHqofX9Sd9oVA6mgMe 5 34 39 12.8205128205128 rJRi8WW24gt9X85PHAxfWNPCizMMhqUQwg 15 106 121 12.396694214876 ra94ZuJRF8yt6Yv7GvwDoNcdaQKV5zLhnZ 7 50 57 12.280701754386 rrsGUZnvUE5jiG1jw1YpSLz8fPz7QNYBNL 5 36 41 12.1951219512195 r9JvBskaY32NQxHjEz63eJXfx9tQrSdrdr 12 88 100 12.0 rhWFasnjCuk2unVvAmqtArhYux6MNb1FUY 9 66 75 12.0 rHsZHqa5oMQNL5hFm4kfLd47aEMYjPstpg 29710 218477 248187 11.9708123310246 rHJ7KLBwbGL1Nxv15zWucqMsKDs2793cRe 5 37 42 11.9047619047619 r3cw8TWbYnsJuMKs5jnLUzsq9NBNo41E2Y 6 45 51 11.7647058823529 rhBUWH1rM8EzY3E1CN186CkTZ2KPv7pvke 8 61 69 11.5942028985507 rH5vgPUmMU5yyroKMEYUqrjjZBjrBU91hV 6 46 52 11.5384615384615 r47GLMFhJPjshD65J8TJSWZJzM3jPHcJdZ 7 54 61 11.4754098360656 rBAfbNiNHNrdbSPrxy28S5C3oZsXkgWbVy 4 31 35 11.4285714285714 raRt2xRmnnLGWYXD5VqtqKfuy4KM324Q3g 5 39 44 11.3636363636364 r429uRzCoM48x9JFaqyefwKJ9GQNrnceRb 24 192 216 11.1111111111111 rEwy79WnbBb2TxERquM1sotKUy5TLkeVZQ 6 48 54 11.1111111111111 rK3hATVGb61XjjUFTG1q9UUAzAoDufhmoo 8 64 72 11.1111111111111
Accounts with small transaction counts would be expected to turn up in a normal distribution of the transaction id front nibble, the expected median percentage being 6.25%. This query would be much more conclusive with more history (and even better in a real SQL database that has UNHEX()) but the accounts that are definitely “spinning” to some degree on transaction ids include:
rapido5rxPmP4YkMZZEeXSHqWefxHEkqv6 Author’s EdDSA arbitrage bot roverTeW1kfwHb9WZyoJW9M6sP4nC6BuJ Author’s ECDSA arbitrage bot r3pFpfYfCoj9Sm5cpRwc4iD4mFPoF9HDVf Possible trade front-runner r2d2iZiCcJmNL6vhUGFjs8U8BuUq6BnmT Another arbitrage bot rHsZHqa5oMQNL5hFm4kfLd47aEMYjPstpg One of the most frequently appearing market-making bots
To ensure confidence in the Ripple network operating fairly the above described two exploits should be made impossible by fixing the transaction ordering to prevent unfair transaction placement for canny operators. Perversely, the desire to save energy by not using proof of work to agree on ledger outcomes has been countermanded by an exploit that uses the same concepts to “mine” transaction ids.