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BM-PAW: A Profitable Mining Attack in the PoW-based Blockchain System

Junjie Hu, Na Ruan

TL;DR

This paper addresses the security of PoW-based blockchains by introducing BM-PAW, a bribery-enhanced extension of the PAW attack that coordinates target-miner behavior via bribe money to increase attacker and target rewards. It develops a formal model of one- and two-pool scenarios, derives reward expressions, and optimizes infiltration powers $(r_1,r_2)$ to achieve incentive compatibility, demonstrating superior performance over PAW in both single- and multi-pool settings. The work further analyzes a two-pool Nash equilibrium showing BM-PAW can overcome the miner's dilemma, and provides practical countermeasures to mitigate bribery-based pool attacks. The findings highlight a significant vulnerability in pooled mining and offer guidance for defensive strategies, with potential applicability to other PoW-based cryptocurrencies. Overall, BM-PAW advances understanding of pool-level adversarial dynamics and emphasizes the need for robust mitigation in real-world mining ecosystems.

Abstract

Mining attacks enable an adversary to procure a disproportionately large portion of mining rewards by deviating from honest mining practices within the PoW-based blockchain system. In this paper, we demonstrate that the security vulnerabilities of PoW-based blockchain extend beyond what these mining attacks initially reveal. We introduce a novel mining strategy, named BM-PAW, which yields superior rewards for both the attacker and the targeted pool compared to the state-of-the-art mining attack, PAW. BM-PAW attackers are incentivized to offer appropriate bribe money to other targets, as they comply with the attacker's directives upon receiving payment. We further find the BM-PAW attacker can circumvent the miner's dilemma through equilibrium analysis in a two-pool BM-PAW game scenario, wherein the outcome is determined by the attacker's mining power. We finally propose practical countermeasures to mitigate these novel pool attacks.

BM-PAW: A Profitable Mining Attack in the PoW-based Blockchain System

TL;DR

This paper addresses the security of PoW-based blockchains by introducing BM-PAW, a bribery-enhanced extension of the PAW attack that coordinates target-miner behavior via bribe money to increase attacker and target rewards. It develops a formal model of one- and two-pool scenarios, derives reward expressions, and optimizes infiltration powers to achieve incentive compatibility, demonstrating superior performance over PAW in both single- and multi-pool settings. The work further analyzes a two-pool Nash equilibrium showing BM-PAW can overcome the miner's dilemma, and provides practical countermeasures to mitigate bribery-based pool attacks. The findings highlight a significant vulnerability in pooled mining and offer guidance for defensive strategies, with potential applicability to other PoW-based cryptocurrencies. Overall, BM-PAW advances understanding of pool-level adversarial dynamics and emphasizes the need for robust mitigation in real-world mining ecosystems.

Abstract

Mining attacks enable an adversary to procure a disproportionately large portion of mining rewards by deviating from honest mining practices within the PoW-based blockchain system. In this paper, we demonstrate that the security vulnerabilities of PoW-based blockchain extend beyond what these mining attacks initially reveal. We introduce a novel mining strategy, named BM-PAW, which yields superior rewards for both the attacker and the targeted pool compared to the state-of-the-art mining attack, PAW. BM-PAW attackers are incentivized to offer appropriate bribe money to other targets, as they comply with the attacker's directives upon receiving payment. We further find the BM-PAW attacker can circumvent the miner's dilemma through equilibrium analysis in a two-pool BM-PAW game scenario, wherein the outcome is determined by the attacker's mining power. We finally propose practical countermeasures to mitigate these novel pool attacks.

Paper Structure

This paper contains 24 sections, 4 theorems, 8 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Our Contributions
  3. Related Work
  4. Preliminaries
  5. Mining Process
  6. Forks
  7. Mining Pool
  8. Threat Model and Assumption
  9. Threat Model
  10. Assumptions
  11. One-pool: BM-PAW
  12. Overview
  13. Method
  14. Reward Analysis
  15. Implementation and Evaluation
  16. ...and 9 more sections

Key Result

theorem thmcountertheorem

A BM-PAW attacker can always earn more reward than honest mining, and the reward of a BM-PAW attacker has a lower bound defined by the reward from an FAW attack.

Figures (6)

  • Figure 1: The Markov state transition diagram of BM-PAW attack.
  • Figure 2: The workflow of the overall BM-PAW attack.
  • Figure 3: The attacker’s relative extra reward $RER_a^{BM-PAW,PAW}$ in different scenarios.
  • Figure 4: The attacker’s relative extra reward $RER_a^{BM-PAW,PAW}$ in different parameters.
  • Figure 5: The target pool’s relative extra reward $RER_t^{BM-PAW,PAW}$ in different scenarios.
  • ...and 1 more figures

Theorems & Definitions (4)

  • theorem thmcountertheorem
  • theorem thmcountertheorem
  • theorem thmcountertheorem
  • theorem thmcountertheorem