Timestamp Manipulation: Timestamp-based Nakamoto-style Blockchains are Vulnerable

TL;DR

The paper investigates timestamp-based Nakamoto-style blockchains and identifies a persistent, systemic threat from adversaries who manipulate block timestamps and strategically release or withhold blocks. It introduces two attack extensions, UUM and SUUM, that relax risk constraints and combine withholding with precise timestamp control to produce near-costless, persistent attacks that distort blockchain incentives. Through discrete-event simulations and real-network data from ETH 1.x-style networks, the authors quantify higher adversarial rewards (e.g., up to 33.30% at $\\alpha=0.25$ for SUUM) and elevated forking rates, alongside a death-spiral dynamic that discourages honest participation. The work also proposes mitigations spanning consensus adjustments, timestamp verification, and economic penalties to counter timestamp-based attacks and restore incentive fairness and network stability.

Abstract

Nakamoto consensus are the most widely adopted decentralized consensus mechanism in cryptocurrency systems. Since it was proposed in 2008, many studies have focused on analyzing its security. Most of them focus on maximizing the profit of the adversary. Examples include the selfish mining attack [FC '14] and the recent riskless uncle maker (RUM) attack [CCS '23]. In this work, we introduce the Staircase-Unrestricted Uncle Maker (SUUM), the first block withholding attack targeting the timestamp-based Nakamoto-style blockchain. Through block withholding, timestamp manipulation, and difficulty risk control, SUUM adversaries are capable of launching persistent attacks with zero cost and minimal difficulty risk characteristics, indefinitely exploiting rewards from honest participants. This creates a self-reinforcing cycle that threatens the security of blockchains. We conduct a comprehensive and systematic evaluation of SUUM, including the attack conditions, its impact on blockchains, and the difficulty risks. Finally, we further discuss four feasible mitigation measures against SUUM.

Figures (6)

• Figure 1: Attack Flowchart. This figure illustrates the attack flowchart for the proposed RUM, UUM and SUUM attacks in timestamp-based Nakamoto-style blockchains. The flowchart delineates the systematic process through which adversaries manipulate block timestamps and strategically withhold or release blocks to gain disproportionate rewards. It highlights the adversarial strategies’ escalation from RUM (risk-free) to UUM (risk-tolerant) and SUUM (withholding-enabled), emphasizing their impact on blockchain protocol and incentive fairness.
• Figure 2: State Transition Process. This figure illustrates the state transition process under different mining strategies. It delineates the dynamic transformation relationships among different states in the blockchain system. Black + Cyan transitions denote RUM Unclemakertimestampingoutthecompetitioninethereum. Black + Violet transitions denote UUM. Red transitions denote SUUM. It clearly presents the path from the initial state to the attack state through nodes and arrows, including the critical transition conditions between the deployment state and the attack state. It annotates the probabilities of state transitions and the behaviors of participants, revealing how attack strategies influence the blockchain’s difficulty and reward distribution by manipulating timestamps and block release timing. (a) In the RUM attack, the adversary employs a risk-free strategy and is not allowed to withhold blocks. The risk-free condition is achieved through the transition from the deployment state to the attack state when $t_1^{p_h} - t_0^{p_h} \in [9, 18)$. (b) Building upon the RUM framework, the UUM attack amplifies the steady-state probability of transitioning to the attack state, trading minimal risk for higher rewards. Specifically, the condition for transitioning from the deployment state to the attack state is extended to $t_1^{(p_h)}-t_0^{(p_h)}\in[9,+\infty)$. During the attack state, once the UUM adversary successfully discovers a new block, the attack is deemed successful by strategically setting the timestamp $t_1^{(p_{\alpha})}-t_0^{(p_{\alpha})}\in[1,900)$. (c) Building upon the UUM framework, the SUUM attack further enhances the adversary’s strategy space by allowing the withholding of blocks. The Markov process of SUUM can be decomposed into two parts. On one hand, from the local perspective of the deployment and downgrade states, SUUM reduces to UUM, where the strategies of the two adversaries are identical. On the other hand, from the local perspective of the deployment and attack states, SUUM resembles traditional selfish mining, with the sole distinction being that the SUUM adversary meticulously manipulates the timestamp $t_i^{(p_h)} - t_i^{(p_{\alpha})} \in [1, 9)$ to ensure the success of the SUUM attack.
• Figure 3: Steady-state Probability. This figure illustrates the steady-state probabilities of three different attack strategies as a function of the adversary's relative power $\alpha$. In the context of RUM and UUM, the steady-state probability of the Attack State serves as a critical metric for quantifying the detrimental impact of attacks. Specifically, a higher steady-state probability of the Attack State directly correlates with a more severe level of harm inflicted on the system. Conversely, for SUUM, the overall harm magnitude is jointly determined by the steady-state probabilities of three distinct states. Note that we only show the total probability of all Attack states within the SUUM model.
• Figure 4: Comparison of Relative Rewards under Different Mining Strategies. (a) Comparison of Adversary Relative Rewards under Different Mining Strategies. This figure compares the relative reward gains of adversaries under different mining strategies, illustrating the reward disparities among the SUUM, UUM, and RUM attack strategies compared to honest mining. The results demonstrate that SUUM yields the highest rewards, significantly outperforming UUM and RUM, while all three attack strategies surpass honest mining in profitability. This outcome confirms that adversaries can obtain excess rewards by manipulating timestamps and strategically withholding blocks. (b) Comparison of Honest Participant Relative Rewards under Different Mining Strategies. This figure presents a comparative analysis of honest participants' relative rewards under different mining strategies. Notably, SUUM exhibits the most severe reward suppression effect, followed by UUM and RUM, with all three attack strategies significantly undercutting the baseline rewards achievable through honest mining.
• Figure 5: Comparison of Minimal Difficulty Risk and Forking Rates under Different Attack Strategies. (a) Comparison of Minimal Difficulty Risk under Different Attack Strategies. This figure presents a comparative assessment of the minimal difficulty risk levels associated with different attack strategies. Honest mining maintains a baseline risk level of zero, while RUM introduces only minimal difficulty escalation risk. In contrast, UUM exhibits marginally higher risk due to its less restrictive attack conditions, and SUUM demonstrates the most significant risk elevation attributable to its block withholding mechanism. (b) Comparison of Forking Rates under Different Mining Strategies. This figure presents a comparative analysis of forking rates under different attack strategies, illustrating the relationship between the adversary's relative power and the resultant forking rate. The results demonstrate that honest mining maintains a zero forking rate due to strict protocol compliance, while the RUM, UUM, and SUUM attacks exhibit progressively increasing fork probabilities.

Theorems & Definitions (28)

• Example 1: Fork Selection
• Definition 1: Fairness
• Definition 2: Block Difficulty
• Definition 3: Block Reward
• Theorem 1: UUM Initiation Condition
• proof : Proof of Theorem 1
• Theorem 2: UUM Successful Condition
• proof : Proof of Theorem 2
• Theorem 3: UUM Successful Condition with Minimal Risk
• proof : Proof of Theorem 3