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MAD-DAG: Protecting Blockchain Consensus from MEV

Roi Bar-Zur, Aviv Tamar, Ittay Eyal

TL;DR

This work tackles the vulnerability of Proof-of-Work blockchains to selfish mining under realistic adversaries, including rushing, MEV-driven reward variability, and petty-compliant miners. It proposes MAD-DAG, a DAG-based protocol with three core mechanisms: a difficulty-adjustment rule, a subsidy mechanism that favors canonical uncontested blocks, and the MAD ledger function that discards contents from equal-length competing chains. The authors develop a tractable MDP-based framework, including an upper-bound model, to bound selfish-mining revenue and compute security thresholds, showing MAD-DAG maintains positive security thresholds (e.g., 11%–31%) under adverse conditions where Colordag and Bitcoin fail. The findings demonstrate MAD-DAG’s practical latency and robustness to adverse conditions, offering a promising path for secure, incentive-compatible DAG-based PoW protocols in the presence of MEV and bribery dynamics.

Abstract

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.

MAD-DAG: Protecting Blockchain Consensus from MEV

TL;DR

This work tackles the vulnerability of Proof-of-Work blockchains to selfish mining under realistic adversaries, including rushing, MEV-driven reward variability, and petty-compliant miners. It proposes MAD-DAG, a DAG-based protocol with three core mechanisms: a difficulty-adjustment rule, a subsidy mechanism that favors canonical uncontested blocks, and the MAD ledger function that discards contents from equal-length competing chains. The authors develop a tractable MDP-based framework, including an upper-bound model, to bound selfish-mining revenue and compute security thresholds, showing MAD-DAG maintains positive security thresholds (e.g., 11%–31%) under adverse conditions where Colordag and Bitcoin fail. The findings demonstrate MAD-DAG’s practical latency and robustness to adverse conditions, offering a promising path for secure, incentive-compatible DAG-based PoW protocols in the presence of MEV and bribery dynamics.

Abstract

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.

Paper Structure

This paper contains 60 sections, 2 equations, 14 figures.

Figures (14)

  • Figure 1: Colordag's reward mechanism and MAD-DAG's ledger function. Red blocks are destructed. Blue blocks are contested. Gray blocks are unacceptable.
  • Figure 2: Example states in the NC and DAG models.
  • Figure 3: Selfish mining revenue in NC, Colordag, and MAD-DAG.
  • Figure 4: Security threshold of NC, Colordag, and MAD-DAG as a function of fork sensitivity.
  • Figure 5: Security threshold of NC, Colordag, and MAD-DAG as a function of guaranteed fee $f$.
  • ...and 9 more figures