Bitcoin resilience test: How undersea cable failures reveal network's real weak point

​Damage to undersea internet cables is considered one of the primary risks to global digital infrastructure. They carry the vast majority of world traffic, and any accidents immediately impact entire nations. However, a new study by the Cambridge Centre for Alternative Finance proves that for Bitcoin, these threats are far less critical than they appear.

Researchers estimate that the network of the first cryptocurrency could withstand even large-scale infrastructure destruction. To lose a significant number of nodes, between 72% and 92% of interstate undersea cables would need to be knocked out simultaneously. In the real world, such a scenario is nearly impossible.

Accidental cable breaks and risks for Bitcoin

The study spans 11 years of Bitcoin network operation, covering the period from 2014 to 2025. During this time, scientists analyzed the performance of over 8 million nodes, 658 undersea cables, and 385 damage incidents, with 68 of those confirmed as actual outages.

The results were quite definitive, showing that in 87% of cases, such incidents affected less than 5% of nodes. The average effect was around -1.5%, while the median effect was just -0.4%. Even major accidents barely change the global picture. A telling example is the incident off the coast of Côte d’Ivoire in March 2024, when seafloor damage knocked out seven undersea cables at once and caused a massive regional internet blackout.

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However, the situation for Bitcoin looked different, as only a few nodes were operating in the region (approximately 0.03% of the network). Globally, the changes were almost imperceptible at about -2.5%, which falls within normal fluctuations. No consensus failures occurred. The price also did not react, as the correlation between such events and the Bitcoin exchange rate turned out to be nearly zero (-0.02).

Risks of a targeted attack on the Bitcoin network

The reason for such resilience lies in Bitcoin's architecture itself. It is a distributed network operating simultaneously across several levels: from physical infrastructure to peer-to-peer connections between nodes.

When researchers modeled random undersea cable outages, the Bitcoin network remained stable even with significant infrastructure losses. The critical threshold at which noticeable fragmentation begins (disconnection of more than 10% of nodes) lies between 72% and 92% of interstate cables. In other words, most global connections would have to fail at once for the network to experience serious disruptions.

However, the situation changes drastically if the attack is not accidental. In the case of a targeted strike on key cables connecting continents, the critical threshold drops to approximately 20% of the infrastructure. An even more vulnerable point is network providers.

The study proves that an attack on numerous of the largest hosting networks could have a much stronger effect. This refers to companies like Hetzner, OVHcloud, Comcast, Amazon Web Services, and Google Cloud.

To achieve a noticeable disruption in connectivity, it is enough to disable about 5% of routing capacity, an order of magnitude less than in the case of accidental cable damage.Data from Bitnodes for March 2026 confirms this pattern: among 23,150 reachable nodes, 869 are hosted on Hetzner servers, 348 each on Comcast and OVH servers, 336 on Amazon, and 313 on Google servers.

How Bitcoin became more resilient and the role of Tor

Interestingly, the network's resilience has not been constant. Over the years, it shifted along with the infrastructure itself. In 2014–2017, when the network was more geographically distributed, the resilience level was high — up to 0.90–0.92.

Later, due to the concentration of mining in East Asia, this figure dropped, reaching a minimum of 0.72 in 2021. At that time, about 74% of the hashrate was located in a single region. After the mining ban in China, the situation changed. The infrastructure quickly dispersed, and by 2022, the indicator recovered to 0.88, stabilizing at around 0.78 in subsequent years.

The most unexpected resilience factor turned out to be Tor. Ten years ago, almost no Bitcoin nodes used this network. But the situation has changed: by 2026, approximately 63% of nodes operate via Tor.

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This means that the majority of the network is effectively hidden and less dependent on classic internet infrastructure, as the geography of such nodes is difficult to track. Tor relays are concentrated mainly in countries with strong infrastructure — Germany, France, and the Netherlands. This increases the overall resilience of the network. In simulations, adding Tor increased the critical failure threshold by another 0.02–0.10.

Researchers call this "adaptive self-organization." Following instances of censorship — in Iran, Myanmar, and China — users shifted en masse to more resilient tools without any central coordinator.

The cloud danger

Against the backdrop of geopolitical tensions — from the Baltic region to the Middle East — concerns about the security of undersea cables are only growing. But for Bitcoin, as historical data shows, such incidents remain mostly "noise."

Instead, a more practical question arises: what happens if problems occur not at the cable level, but at the level of hosting and routing infrastructure? This is where the study finds a real point of vulnerability. A scenario of a targeted attack on key Autonomous Systems (ASNs) shows that disabling about 5% of routing capacity is enough for a noticeable disruption in connectivity. This does not lead to a break in consensus, but it can create serious delays in the propagation of blocks and transactions.

That said, even in extreme scenarios, Bitcoin has "insurance." As mentioned, most nodes operate through Tor, creating a baseline level of resilience even during significant disruptions in the regular internet. Additionally, technologies such as block relay networks, compact blocks, or satellite solutions like Blockstream Satellite further strengthen the network, even though they were not included in the study's model.