Blockchain security is threatened by selfish mining, where a miner (operator) deviates from the protocol to increase their revenue. Selfish mining is exacerbated by adverse conditions: rushing (network propagation advantage for the selfish miner), varying block rewards due to block contents, called miner extractable value (MEV), and petty-compliant miners who accept bribes from the selfish miner. The state-of-the-art selfish-mining-resistant blockchain protocol, Colordag, does not treat these adverse conditions and was proven secure only when its latency is impractically high. We present MAD-DAG, Mutually-Assured-Destruction Directed-Acyclic-Graph, the first practical protocol to counter selfish mining under adverse conditions. MAD-DAG achieves this thanks to its novel ledger function, which discards the contents of equal-length chains competing to be the longest. We analyze selfish mining in both Colordag and MAD-DAG by modeling a rational miner using a Markov Decision Process (MDP). We obtain a tractable model for both by developing conservative reward rules that favor the selfish miner to yield an upper bound on selfish mining revenue. To the best of our knowledge, this is the first tractable model of selfish mining in a practical DAG-based blockchain. This enables us to obtain a lower bound on the security threshold, the minimum fraction of computational power a miner needs in order to profit from selfish mining. MAD-DAG withstands adverse conditions under which Colordag and Bitcoin fail, while otherwise maintaining comparable security. For example, with petty-compliant miners and high levels of block reward variability, MAD-DAG's security threshold ranges from 11% to 31%, whereas both Colordag and Bitcoin achieve 0% for all levels.

Blockchains implement decentralized monetary systems and applications. Recent advancements enable what we call tethering a blockchain to a primary blockchain, securing the tethered chain by nodes that post primary-chain tokens as collateral. The collateral ensures nodes behave as intended, until they withdraw it. Unlike a Proof of Stake blockchain which uses its own token as collateral, using primary-chain tokens shields the tethered chain from the volatility of its own token. State-of-the-art tethered blockchains either rely on centralization, or make extreme assumptions: that all communication is synchronous, that operators remain correct even post-withdrawal, or that withdrawals can be indefinitely delayed by tethered-chain failures. We prove that with partial synchrony, there is no solution to the problem. However, under the standard assumptions that communication with the primary chain is synchronous and communication among the tethered chain nodes is partially synchronous, there is a solution. We present a tethered-chain protocol called Aegis. Aegis uses references from its blocks to primary blocks to define committees, checkpoints on the primary chain to perpetuate decisions, and resets to establish new committees when previous ones become obsolete. It ensures safety at all times and rapid progress when latency among Aegis nodes is low.

Many blockchain-based decentralized services require their validators (operators) to deposit stake (collateral), which is forfeited (slashed) if they misbehave. Restaking networks let validators secure multiple services by reusing stake. These networks have quickly gained traction, leveraging over 20 billion in stake. However, restaking introduces a new attack vector where validators can coordinate to misbehave across multiple services simultaneously, extracting digital assets while forfeiting their stake only once.

The security of proof-of-work blockchain protocols critically relies on incentives. Their operators, called miners, receive rewards for creating blocks containing user-generated transactions. Each block rewards its creator with newly minted tokens and with transaction fees paid by the users. The protocol stability is violated if any of the miners surpasses a threshold ratio of the computational power; she is then motivated to deviate with selfish mining and increase her rewards.Previous analyses of selfish mining strategies assumed constant rewards. But with statistics from operational systems, we show that there are occasional whales – blocks with exceptional rewards. Modeling this behavior implies a state-space that grows exponentially with the parameters, becoming prohibitively large for existing analysis tools.We present the WeRLman1framework to analyze such models. WeRLman uses deep Reinforcement Learning (RL), inspired by the state-of-the-art AlphaGo Zero algorithm. Directly extending AlphaGo Zero to a stochastic model leads to high sampling noise, which is detrimental to the learning process. Therefore, WeRLman employs novel variance reduction techniques by exploiting the recurrent nature of the system and prior knowledge of transition probabilities. Evaluating WeRLman against models we can accurately solve demonstrates it achieves unprecedented accuracy in deep RL for blockchain.We use WeRLman to analyze the incentives of a rational miner in various settings and upper-bound the security threshold of Bitcoin-like blockchains. We show, for the first time, a negative relationship between fee variability and the security threshold. The previously known bound, with constant rewards, stands at 0.25 [2]. We show that considering whale transactions reduces this threshold considerably. In particular, with Bitcoin historical fees and its future minting policy, its threshold for deviation will drop to 0.2 in 10 years, 0.17 in 20 years, and to 0.12 in 30 years. With recent fees from the Ethereum smart-contract platform, the threshold drops to 0.17. These are below the common sizes of large miners [3].

Blockchain technology is responsible for the emergence of cryptocurrencies, such as Bitcoin and Ethereum. The security of a blockchain protocol relies on the incentives of its participants. Selfish mining is a form of deviation from the protocol where a participant can gain more than her fair share. Previous analyses of selfish mining make easing, non-realistic assumptions. We introduce a more realistic model with varying block rewards in the form of transaction fees. However, this comes at the cost of an intractable state space. To solve the complex model, we introduce WeRLman, a novel method based on deep Reinforcement Learning (deep RL). Using WeRLman, we show reward variability can significantly hurt blockchain security.

A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large miners can perform selfish mining -- deviate from the protocol to gain more than their fair share. Such systems are thus secure if all miners are smaller than a threshold size so their best response is following the protocol.