Monero Network Experiences 51% Hash Rate Takeover: Qubic's Two-Hour Mining Experiment Resulting in Significant Block Disruptions

In a significant technical demonstration on August 15, Qubic successfully executed a controlled 51% attack scenario on the Monero network, resulting in substantial blockchain reorganization. The experiment saw 6 blocks undergo reorganization while 60 blocks were rendered orphaned, effectively disrupting the network’s consensus mechanism over a concentrated period.

Unprecedented Hash Rate Dominance

During the two-hour experimental window, Qubic’s mining operation captured an extraordinary 80% of all Monero block production. The network’s computational power spiked to 2.71 GH/s, with Qubic’s contribution accounting for 52% of the entire global Monero hash rate—a threshold exceeding the critical 51% benchmark necessary to compromise network security. Across this timeframe, participants mined 5,506 Monero blocks in total.

Economic Output and Destruction Mechanism

The experiment’s financial implications were substantial. Miners extracted approximately 750 XMR and 7 million XTM throughout the operation. This activity triggered the burning of 17.2 billion QUBIC tokens—combining unsold Tari from prior distributions with the newly generated XTM. Based on the destruction mechanism, this represented an average burn rate of approximately 3,200 QUBIC per billion, translating to roughly $55,000 in equivalent value.

Simultaneously, the network’s Computors and mining participants collectively accumulated 62.2 billion QUBIC in rewards, valued at approximately $200,000. This earning structure demonstrates compelling economics: Qubic’s current miner compensation stands nearly four times higher than what Monero miners typically receive—a critical factor illustrating the incentive differential between the two networks.

Ongoing Implications

The 51% attack experiment remains in active progress, offering real-time insights into network vulnerability patterns and blockchain reorganization mechanics. This controlled demonstration provides valuable technical data for understanding distributed consensus system resilience.