In this chapter, we look at some of the most important issues related to the security and privacy of the Lightning Network. First, we’ll consider privacy, what it means, how to evaluate it, and some things you can do to protect your own privacy while using the Lightning Network. Then we’ll explore some common attacks and mitigation techniques.
The key value proposition of cryptocurrency is censorship resistant money. Bitcoin offers participants the possibility of storing and transferring their wealth without interference by governments, banks, or corporations. The Lightning Network continues this mission.
Unlike trivial scaling solutions like custodial Bitcoin banks, the Lightning Network aims to scale Bitcoin without compromising on self custody, which should lead to greater censorship resistance in the Bitcoin ecosystem. However, the Lightning Network operates under a different security model, which introduces novel security and privacy challenges.
The question, "Is Lightning private?" has no direct answer. Privacy is a complex topic; it is often difficult to precisely define what we mean by privacy, particularly if you are not a privacy researcher. Fortunately, privacy researchers use processes to analyze and evaluate the privacy characteristics of systems, and we can use them too! Let’s look at how a security researcher might seek to answer the question, "Is Lightning private?" in two general steps.
First, a privacy researcher would define a security model that specifies what an adversary is capable of and aims to achieve. Then, they would describe the relevant properties of the system and check whether it conforms to the requirements.
A security model is based on a set of underlying security assumptions. In cryptographic systems, these assumptions are often centered around the mathematical properties of the cryptographic primitives, such as ciphers, signatures, and hash functions. The security assumptions of the Lightning Network are that the ECDSA signatures, SHA-256 hash function, and other cryptographic functions used in the protocol behave within their security definitions. For example, we assume that it is practically impossible to find a preimage (and second preimage) of a hash function. This allows the Lightning Network to rely on the HTLC mechanism (which uses the preimage of a hash function) for the atomicity of multihop payments: nobody except the final recipient can reveal the payment secret and resolve the HTLC. We also assume a degree of connectivity in the network, namely that Lightning channels form a connected graph. Therefore, it is possible to find a path from any sender to any receiver. Finally, we assume network messages are propagated within certain timeouts.
Now that we’ve identified some of our underlying assumptions, let’s consider some possible adversaries.
Here are some possible models of adversaries in the Lightning Network. An "honest-but-curious" forwarding node can observe payment amounts, the immediately preceding and following nodes, and the graph of announced channels with their capacities. A very well-connected node can do the same but to a larger extent. For example, consider the developers of a popular wallet who maintain a node that their users connect to by default. This node would be responsible for routing a large share of payments to and from the users of that wallet. What if multiple nodes are under adversarial control? If two colluding nodes happen to be on the same payment path, they would understand that they are forwarding HTLCs belonging to the same payment because HTLCs have the same payment hash.
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Note
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Multipart payments (see [mpp]) enable users to obfuscate their payment amounts given their nonuniform split sizes. |
What may be the goals of a Lightning attacker? Information security is often described in terms of three main properties: confidentiality, integrity, and availability.
- Confidentiality
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The information only gets to intended recipients.
- Integrity
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The information does not get altered in transit.
- Availability
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The system is functioning most of the time.
The important properties of the Lightning Network are mostly centered around confidentiality and availability. Some of the most important properties to protect include:
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Only the sender and the receiver know the payment amount.
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No one can link senders and receivers.
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An honest user cannot be blocked from sending and receiving payments.
For each privacy goal and security model, there is a certain probability that an attacker succeeds. This probability depends on various factors, such as the size and structure of the network. Other things being equal, it is generally easier to successfully attack a small network rather than a large one. Similarly, the more centralized the network is, the more capable an attacker can be if "central" nodes are under their control. Of course, the term centralization must be defined precisely to build security models around it, and there are many possible definitions of how centralized a network is. Finally, as a payment network, the Lightning Network depends on economic stimuli. The size and structure of fees affect the routing algorithm, and therefore can either aid the attacker by forwarding most payments through their nodes or prevent this from happening.
What does it mean to de-anonymize someone? In simple terms, de-anonymization implies linking some action with a person’s real-world identity, such as their name or physical address. In privacy research, the notion of de-anonymization is more nuanced. First, we are not necessarily talking about names and addresses. Discovering someone’s IP address or telephone number may also be considered de-anonymization. A piece of information that allows linking a user’s action to their previous actions is referred to as identity. Second, de-anonymization is not binary; a user is neither fully anonymous nor completely de-anonymized. Instead, privacy research looks at anonymity compared to the anonymity set.
The anonymity set is a central notion in privacy research. It refers to the set of identities such that, from an attacker’s viewpoint, a given action could correspond to anyone in the set. Consider a real-life example. Imagine you meet a person on a city street. What is their anonymity set from your point of view? If you don’t know them personally, and without any additional information, their anonymity set roughly equals the city’s population, including travelers. If you additionally consider their appearance, you may be able to roughly estimate their age and exclude the city residents who are obviously older or younger than the person in question from the anonymity set. Furthermore, if you notice that the person walks into the office of Company X using an electronic badge, the anonymity set shrinks to the number of Company X’s employees and visitors. Finally, you may notice the license number of the car they used to arrive at the place. If you are a casual observer, this doesn’t give you much. However, if you are a city official and have access to the database that matches license plate numbers with names, you can narrow down the anonymity set to just a few people: the car owner and any close friends and relatives that may have borrowed the car.
This example illustrates a few important points. First, every bit of information may bring the adversary closer to their goal. It may not be necessary to shrink the anonymity set to the size of one. For instance, if the adversary plans a targeted denial-of-service (DoS) attack and can take down 100 servers, the anonymity set of 100 suffices. Second, the adversary can cross-correlate information from different sources. Even if a privacy leak looks relatively benign, we never know what it can achieve in combination with other data sources. Finally, especially in cryptographic settings, the attacker always has the "last resort" of a brute-force search. Cryptographic primitives are designed so that it is practically impossible to guess a secret such as a private key. Nevertheless, each bit of information brings the adversary closer to this goal, and at some point, it becomes attainable.
In terms of Lightning, de-anonymizing generally means deriving a correspondence between payments and users identified by node IDs. Each payment may be assigned a sender anonymity set and a receiver anonymity set. Ideally, the anonymity set consists of all the users of the network. This assures that the attacker has no information whatsoever. However, the real network leaks information that allows an attacker to narrow down the search. The smaller the anonymity set, the higher the chance of successful de-anonymization.
While it’s true that transactions on the Bitcoin network do not associate real-world identities with Bitcoin addresses, all transactions are broadcast in cleartext and can be analyzed. Multiple companies have been established to de-anonymize users of Bitcoin and other cryptocurrencies.
At first glance, Lightning provides better privacy than Bitcoin because Lightning payments are not broadcast to the whole network. While this improves the privacy baseline, other properties of the Lightning protocol may make anonymous payments more challenging. For instance, larger payments may have fewer routing options. This may allow an adversary who controls well-capitalized nodes to route most large payments and discover payment amounts and probably other details. Over time, as the Lightning Network grows, this may become less of a problem.
Another relevant difference between Lightning and Bitcoin is that Lightning nodes maintain a permanent identity, whereas Bitcoin nodes do not. A sophisticated Bitcoin user can easily switch nodes used to receive blockchain data and broadcast transactions. A Lightning user, on the contrary, sends and receives payments through the nodes they have used to open their payment channels. Moreover, the Lightning protocol assumes that routing nodes announce their IP address in addition to their node ID. This creates a permanent link between node IDs and IP addresses, which may be dangerous, considering that an IP address is often an intermediary step in anonymity attacks linked to the user’s physical location and, in most cases, real-world identity. It is possible to use Lightning over Tor, but many nodes do not use this functionality, as can be seen from statistics collected from node announcements.
A Lightning user, when sending a payment, has its neighbors in its anonymity set. Specifically, a routing node only knows the immediately preceding and following nodes. The routing node does not know whether its immediate neighbors in the payment route are the ultimate sender or receiver. Therefore, the anonymity set of a node in Lightning roughly equals its neighbors (see The anonymity set of Alice and Bob constitutes their neighbors).
Similar logic applies to payment receivers. Many users open only a handful of payment channels, therefore limiting their anonymity sets. Moreover, in Lightning, the anonymity set is static or at least slowly changing. In contrast, one can achieve significantly larger anonymity sets in on-chain CoinJoin transactions. CoinJoin transactions with anonymity sets larger than 50 are quite frequent. Typically, the anonymity sets in a CoinJoin transaction correspond to a dynamically changing set of users.
Finally, Lightning users can also be denied service, having their channels blocked or depleted by an attacker. Forwarding payments requires capital—a scarce resource!—to be temporarily blocked in HTLCs along the route. An attacker may send many payments but fail to finalize them, occupying honest users' capital for long periods. This attack vector is not present (or at least not as obvious) in Bitcoin.
In summary, while some aspects of the Lightning Network’s architecture suggest that it is a step forward in terms of privacy compared to Bitcoin, other properties of the protocol may make attacks on privacy easier. Thorough research is needed to evaluate what privacy guarantees the Lightning Network provides and improve the state of affairs.
The issues discussed in this part of the chapter summarize research available in mid-2021. However, this area of research and development is growing quickly. We are happy to report that the authors are aware of multiple research teams currently working on Lightning privacy.
Now let’s review some of the attacks on LN privacy that have been described in academic literature.
Recent research describes various ways in which the security and privacy of the Lightning Network may be compromised.
One of the goals for a privacy-preserving payment system is to hide the payment amount from uninvolved parties. The Lightning Network is an improvement over Layer 1 in this regard. While Bitcoin transactions are broadcast in cleartext and can be observed by anyone, Lightning payments only travel through a few nodes along the payment path. However, intermediary nodes do see the payment amount, although this payment amount might not correspond to the actual total payment amount (see [mpp]). This is necessary to create a new HTLC at every hop. The availability of payment amounts to intermediary nodes do not present an immediate threat. However, an honest-but-curious intermediary node may use it as a part of a larger attack.
An attacker might be interested in learning the sender and/or the receiver of a payment to reveal certain economic relationships. This breach of privacy could harm censorship resistance, as an intermediary node could censor payments to or from certain receivers or senders. Ideally, linking senders to receivers should not be possible to anyone other than the sender and the receiver.
In the following sections, we will consider two types of adversaries: the off-path adversary and the on-path adversary. An off-path adversary tries to assess the sender and the receiver of a payment without participating in the payment routing process. An on-path adversary can leverage any information it might gain by routing the payment of interest.
First, consider the off-path adversary. In the first step of this attack scenario, a potent off-path adversary deduces the individual balances in each payment channel via probing (described in a subsequent section) and forms a network snapshot at time t1. For simplicity’s sake, let’s make t1 equal 12:05. It then probes the network again at sometime later at time t2, which we’ll make 12:10. The attacker would then compare the snapshots at 12:10 and 12:05 and use the differences between the two snapshots to infer information about payments that took place by looking at paths that have changed. In the simplest case, if only one payment occurred between 12:10 and 12:05, the adversary would observe a single path where the balances have changed by the same amounts. Thus, the adversary learns almost everything about this payment: the sender, the recipient, and the amount. If multiple payment paths overlap, the adversary needs to apply heuristics to identify such overlap and separate the payments.
Now, we turn our attention to an on-path adversary. Such an adversary might seem convoluted. However, in June 2020, researchers noted that the single most central node observed close to 50% of all LN payments, while the four most central nodes observed an average of 72% payments. These findings emphasize the relevance of the on-path attacker model. Even though intermediaries in a payment path only learn their successor and predecessor, there are several leakages that a malicious or honest-but-curious intermediary might use to infer the sender and the receiver.
The on-path adversary can observe the amount of any routed payment as well as timelock deltas (see [onion_routing]). Hence, the adversary can exclude any nodes from the sender’s or the receiver’s anonymity set with capacities lower than the routed amount. Therefore, we observe a trade-off between privacy and payment amounts. Typically, the larger the payment amount is, the smaller the anonymity sets are. We note that this leakage could be minimized with multipart payments or with large capacity payment channels. Similarly, payment channels with small timelock deltas could be excluded from a payment path. More precisely, a payment channel cannot pertain to a payment if the remaining time the payment might be locked for is larger than what the forwarding node would be willing to accept. This leakage could be evicted by adhering to the so-called shadow routes.
One of the most subtle and yet powerful leakages an on-path adversary can foster is the timing analysis. An on-path adversary can keep a log for every routed payment, along with the amount of time it takes for a node to respond to an HTLC request. Before starting the attack, the attacker learns every node’s latency characteristics in the Lightning Network by sending them requests. Naturally, this can aid in establishing the adversary’s precise position in the payment path. Even more, as it was recently shown, an attacker can successfully determine the sender and the receiver of a payment from a set of possible senders and receivers using time-based estimators.
Finally, it’s important to recognize that unknown or unstudied leakages probably exist that could aid de-anonymizing attempts. For instance, because different Lightning wallets apply different routing algorithms, even knowing the applied routing algorithm could help exclude certain nodes from being a sender and/or receiver of a payment.
The balances of Lightning channels are supposed to be hidden for privacy and efficiency reasons. A Lightning node only knows the balances of its adjacent channels. The protocol provides no standard way to query the balance of a remote channel.
However, an attacker can reveal the balance of a remote channel in a probing attack. In information security, probing refers to the technique of sending requests to a targeted system and making conclusions about its private state based on the received responses.
Lightning channels are prone to probing. Recall that a standard Lightning payment starts with the receiver creating a random payment secret and sending its hash to the sender. Note that for the intermediary nodes, all hashes look random. There is no way to tell whether a hash corresponds to a real secret or was generated randomly.
The probing attack proceeds as follows. Say the attacker Mallory wants to reveal Alice’s balance of a public channel between Alice and Bob. Suppose the total capacity of that channel is 1 million satoshis. Alice’s balance could be anything from zero to 1 million satoshis (to be precise, the estimate is a bit tighter due to channel reserve, but we don’t account for it here for simplicity). Mallory opens a channel with Alice with 1 million satoshis and sends 500,000 satoshis to Bob via Alice using a random number as the payment hash. Of course, this number does not correspond to any known payment secret. Therefore, the payment will fail. The question is: how exactly will it fail?
There are two scenarios. If Alice has more than 500,000 satoshis on her side of the channel to Bob, she forwards the payment. Bob decrypts the payment onion and realizes that the payment is intended for him. He looks up his local store of payment secrets and searches for the preimage that corresponds to the payment hash, but does not find one. Following the protocol, Bob returns the "unknown payment hash" error to Alice, who relays it back to Mallory. As a result, Mallory knows that the payment could have succeeded if the payment hash was real. Therefore, Mallory can update her estimation of Alice’s balance from "between zero and 1 million" to "between 500,000 and 1 million." Another scenario happens if Alice’s balance is lower than 500,000 satoshis. In that case, Alice is unable to forward the payment and returns the "insufficient balance" error to Mallory. Mallory updates her estimation from "between zero and 1 million" to "between zero and 500,000."
Note that in any case, Mallory’s estimation becomes twice as precise after just one probing! She can continue probing, choosing the next probing amount such that it divides the current estimation interval in half. This well-known search technique is called binary search. With binary search, the number of probes is logarithmic in the desired precision. For example, to obtain Alice’s balance in a channel of 1 million satoshis up to a single satoshi, Mallory would only have to perform log2 (1,000,000) ≈ 20 probings. If one probing takes 3 seconds, one channel can be precisely probed in only about a minute!
Channel probing can be made even more efficient. In its simplest variant, Mallory directly connects to the channel she wants to probe. Is it possible to probe a channel without opening a channel to one of its endpoints? Imagine Mallory now wants to probe a channel between Bob and Charlie but doesn’t want to open another channel, which requires paying on-chain fees and waiting for confirmations of the funding transactions. Instead, Mallory reuses her existing channel to Alice and sends a probe along the route Mallory → Alice → Bob → Charlie. Mallory can interpret the "unknown payment hash" error in the same way as before: the probe has reached the destination; therefore, all channels along the route have sufficient balances to forward it. But what if Mallory receives the "insufficient balance" error? Does it mean that the balance is insufficient between Alice and Bob or between Bob and Charlie?
In the current Lightning protocol, error messages report not only which error occurred but also where it happened. So, with more careful error handling, Mallory now knows which channel failed. If this is the target channel, she updates her estimates; if not, she chooses another route to the target channel. She even gets additional information about the balances of intermediary channels, on top of that of the target channel.
The probing attack can be further used to link senders and receivers, as described in the previous section.
At this point, you may ask: why does the Lightning Network do such a poor job at protecting its users' private data? Wouldn’t it be better to not reveal to the sender why and where the payment has failed? Indeed, this could be a potential countermeasure, but it has significant drawbacks. Lightning has to strike a careful balance between privacy and efficiency. Remember that regular nodes don’t know balance distributions in remote channels. Therefore, payments can (and often do) fail because of insufficient balance at an intermediary hop. Error messages allow the sender to exclude the failing channel from consideration when constructing another route. One popular Lightning wallet even performs probing internally to check whether a constructed route can really handle a payment.
There are other potential countermeasures against channel probing. First, it is hard for an attacker to target unannounced channels. Second, nodes that implement just-in-time (JIT) routing may be less prone to the attack. Finally, as multipart payments make the problem of insufficient capacity less severe, the protocol developers may consider hiding some of the error details without harming efficiency.
When resources are made publicly available, there is a risk that attackers may attempt to make that resource unavailable by executing a denial-of-service (DoS) attack. Generally, this is achieved by the attacker bombarding a resource with requests, which are indistinguishable from legitimate queries. The attacks seldom result in the target suffering financial loss, aside from the opportunity cost of their service being down, and are merely intended to aggrieve the target.
Typical mitigations for DoS attacks require authentication for requests to separate legitimate users from malicious ones. These mitigations incur a trivial cost to regular users but will act as a sufficient deterrent to an attacker launching requests at scale. Anti-denial-of-service measures can be seen everywhere on the internet—websites apply rate limits to ensure that no one user can consume all of their server’s attention, film review sites require login authentication to keep angry r/prequelmemes (Reddit group) members at bay, and data services sell API keys to limit the number of queries.
In Bitcoin, the bandwidth that nodes use to relay transactions and the space that they avail to the network in the form of their mempool are publicly available resources. Any node on the network can consume bandwidth and mempool space by sending a valid transaction. If this transaction is mined in a valid block, they will pay transaction fees, which adds a cost to using these shared network resources.
In the past, the Bitcoin network faced an attempted DoS attack where attackers spammed the network with low-fee transactions. Many of these transactions were not selected by miners due to their low transaction fees, so the attackers could consume network resources without paying the fees. To address this issue, a minimum transaction relay fee that set a threshold fee that nodes require to propagate transactions was set. This measure largely ensured that the transactions that consume network resources will eventually pay their chain fees. The minimum relay fee is acceptable to regular users but would hurt attackers financially if they tried to spam the network. While some transactions may not make it into valid blocks within high-fee environments, these measures have largely been effective at deterring this type of spam.
Similarly to Bitcoin, the Lightning Network charges fees for the use of its public resources, but in this case, the resources are public channels, and the fees come in the form of routing fees. The ability to route payments through nodes in exchange for fees provides the network with a large scalability benefit—nodes that are not directly connected can still transact—but it comes at the cost of exposing a public resource that must be protected against DoS attacks.
When a Lightning node forwards a payment on your behalf, it uses data and payment bandwidth to update its commitment transaction, and the amount of the payment is reserved in their channel balance until it is settled or failed. In successful payments, this is acceptable because the node is eventually paid out its fees. Failed payments do not incur fees in the current protocol. This allows nodes to costlessly route failed payments through any channels. This is great for legitimate users, who wouldn’t like to pay for failed attempts, but also allows attackers to costlessly consume nodes' resources—much like the low-fee transactions on Bitcoin that never end up paying miner fees.
At the time of writing, a discussion is ongoing on the lightning-dev mailing list as to how best address this issue.
There are two known DoS attacks on public LN channels which render a target channel, or a set of target channels, unusable. Both attacks involve routing payments through a public channel, then holding them until their timeout, thus maximizing the attack’s duration. The requirement to fail payments to not pay fees is fairly simple to meet because malicious nodes can simply reroute payments to themselves. In the absence of fees for failed payments, the only cost to the attacker is the on-chain cost of opening a channel to dispatch these payments through, which can be trivial in low-fee environments.
Lightning nodes update their shared state using asymmetric commitment transactions, on which HTLCs are added and removed to facilitate payments. Each party is limited to a total of 483 HTLCs in the commitment transaction at a time. A channel jamming attack allows an attacker to render a channel unusable by routing 483 payments through the target channel and holding them until they time out.
It should be noted that this limit was chosen in the specification to ensure that all the HTLCs can be swept in a single justice transaction. While this limit may be increased, transactions are still limited by the block size, so the number of slots available is likely to remain limited.
A channel liquidity lockup attack is comparable to a channel jamming attack in that it routes payments through a channel and holds them so that the channel is unusable. Rather than locking up slots on the channel commitment, this attack routes large HTLCs through a target channel, consuming all the channel’s available bandwidth. This attack’s capital commitment is higher than the commitment jamming attack because the attacking node needs more funds to route failed payments through the target.
Computer networks are often layered. Layering allows for separation of concerns and makes the whole system manageable. No one could design a website if it required understanding all the TCP/IP stack up to the physical encoding of bits in an optical cable. Every layer is supposed to provide the functionality to the layer above in a clean way. Ideally, the upper layer should perceive a lower layer as a black box. In reality, though, implementations are not ideal, and the details leak into the upper layer. This is the problem of leaky abstractions.
In the context of Lightning, the LN protocol relies on the Bitcoin protocol and the LN P2P network. Up to this point, we only considered the privacy guarantees offered by the Lightning Network in isolation. However, creating and closing payment channels are inherently performed on the Bitcoin blockchain. Consequently, for a complete analysis of the Lightning Network’s privacy provisions, one needs to consider every layer of the technological stack users might interact with. Specifically, a de-anonymizing adversary can and will use off-chain and on-chain data to cluster or link LN nodes to corresponding Bitcoin addresses.
Attackers attempting to de-anonymize LN users may have various goals, in a cross-layer context:
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Cluster Bitcoin addresses owned by the same user (Layer 1). We call these Bitcoin entities.
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Cluster LN nodes owned by the same user (Layer 2).
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Unambiguously link sets of LN nodes to the sets of Bitcoin entities that control them.
There are several heuristics and usage patterns that allow an adversary to cluster Bitcoin addresses and LN nodes owned by the same LN users. Moreover, these clusters can be linked across layers using other powerful cross-layer linking heuristics. The last type of heuristics, cross-layer linking techniques, emphasizes the need for a holistic view of privacy. Specifically, we must consider privacy in the context of both layers together.
Lightning Network blockchain interactions are permanently reflected in the Bitcoin entity graph. Even if a channel is closed, an attacker can observe which address funded the channel and where the coins are spent after closing it. For this analysis, let’s consider four separate entities. Opening a channel causes a monetary flow from a source entity to a funding entity; closing a channel causes a flow from a settlement entity to a destination entity.
In early 2021, Romiti et al. identified four heuristics that allow the clustering of these entities. Two of them capture certain leaky funding behavior and two describe leaky settlement behaviors.
- Star heuristic (funding)
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If a component contains one source entity that forwards funds to one or more funding entities, these funding entities are likely controlled by the same user.
- Snake heuristic (funding)
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If a component contains one source entity that forwards funds to one or more entities, which themselves are used as source and funding entities, then all these entities are likely controlled by the same user.
- Collector heuristic (settlement)
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If a component contains one destination entity that receives funds from one or more settlement entities, these settlement entities are likely controlled by the same user.
- Proxy heuristic (settlement)
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If a component contains one destination entity that receives funds from one or more entities, which themselves are used as settlement and destination entities, then these entities are likely controlled by the same user.
It is worthwhile pointing out that these heuristics might produce false positives. For instance, if transactions of several unrelated users are combined in a CoinJoin transaction, then the star or the proxy heuristic can produce false positives. This could happen if users are funding a payment channel from a CoinJoin transaction. Another potential source of false positives could be that an entity could represent several users if clustered addresses are controlled by a service (e.g., exchange) or on behalf of their users (custodial wallet). However, these false positives can effectively be filtered out.
If outputs of funding transactions are not reused for opening other channels, the snake heuristic does not work. If users refrain from funding channels from a single external source and avoid collecting funds in a single external destination entity, the other heuristics would not yield any significant results.
LN nodes advertise aliases, for instance, LNBig.com. Aliases can improve the usability of the system. However, users tend to use similar aliases for their own different nodes. For example, LNBig.com Billing is likely owned by the same user as the node with alias LNBig.com. Given this observation, one can cluster LN nodes by applying their node aliases. Specifically, one clusters LN nodes into a single address if their aliases are similar with respect to some string similarity metric.
Another method to cluster LN nodes is applying their IP or Tor addresses. If the same IP or Tor addresses correspond to different LN nodes, these nodes are likely controlled by the same user.
For more privacy, aliases should be sufficiently different from one another. While the public announcement of IP addresses may be unavoidable for those nodes that wish to have incoming channels in the Lightning Network, linkability across nodes of the same user can be mitigated if the clients for each node are hosted with different service providers and thus IP addresses.
Associating LN nodes to Bitcoin entities is a serious breach of privacy that is exacerbated by the fact that most LN nodes publicly expose their IP addresses. Typically, an IP address can be considered as a unique identifier of a user. Two widely observed behavior patterns reveal links between LN nodes and Bitcoin entities:
- Coin reuse
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Whenever users close payment channels, they get back their corresponding coins. However, many users reuse those coins in opening a new channel. Those coins can effectively be linked to a common LN node.
- Entity reuse
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Typically, users fund their payment channels from Bitcoin addresses corresponding to the same Bitcoin entity.
These cross-layer linking algorithms could be foiled if users possess multiple unclustered addresses or use multiple wallets to interact with the Lightning Network.
The possible de-anonymization of Bitcoin entities illustrates how important it is to consider the privacy of both layers simultaneously instead of one at a time.
The Lightning Network, as the name suggests, is a peer-to-peer network of payment channels. Therefore, many of its properties (privacy, robustness, connectivity, routing efficiency) are influenced and characterized by its network nature.
In this section, we discuss and analyze the Lightning Network from the point of view of network science. We are particularly interested in understanding the LN channel graph, its robustness, connectivity, and other important characteristics.
One could have expected that the Lightning Network is a random graph, where edges are randomly formed between nodes. If this was the case, then the Lightning Network’s degree distribution would follow a Gaussian normal distribution. In particular, most of the nodes would have approximately the same degree, and we would not expect nodes with extraordinarily large degrees. This is because the normal distribution exponentially decreases for values outside of the interval around the average value of the distribution. The depiction of a random graph (as we saw in [lngraph]) looks like a mesh network topology. It looks decentralized and nonhierarchical: every node seems to have equal importance. Additionally, random graphs have a large diameter. In particular, routing in such graphs is challenging because the shortest path between any two nodes is moderately long.
However, in stark contrast, the LN graph is completely different.
Lightning is a financial network. Thus, the growth and formation of the network are also influenced by economic incentives. Whenever a node joins the Lightning Network, it may want to maximize its connectivity to other nodes in order to increase its routing efficiency. This phenomenon is called preferential attachment. These economic incentives result in a fundamentally different network than a random graph.
Based on snapshots of publicly announced channels, the degree distribution of the Lightning Network follows a power-law function. In such a graph, the vast majority of nodes have very few connections to other nodes, while only a handful of nodes have numerous connections. At a high level, this graph topology resembles a star: the network has a well-connected core and a loosely connected periphery. Networks with power-law degree distribution are also called scale-free networks. This topology is advantageous for routing payments efficiently but prone to certain topology-based attacks.
An adversary might want to disrupt the Lightning Network and may decide its goal is to dismantle the whole network into many smaller components, making payment routing practically impossible in the whole network. A less ambitious, but still malicious and severe goal might be to only take down certain network nodes. Such a disruption might occur on the node level or on the edge level.
Let’s suppose an adversary can take down any node in the Lightning Network. For instance, it can attack them with a distributed denial of service (DDoS) attack or make them nonoperational by any means. It turns out that if the adversary chooses nodes randomly, then scale-free networks like the Lightning Network are robust against node-removal attacks. This is because a random node lies on the periphery with a small number of connections, therefore playing a negligible role in the network’s connectivity. However, if the adversary is more prudent, it can target the most well-connected nodes. Not surprisingly, the Lightning Network and other scale-free networks are not robust against targeted node-removal attacks.
On the other hand, the adversary could be more stealthy. Several topology-based attacks target a single node or a single payment channel. For example, an adversary might be interested in exhausting a certain payment channel’s capacity on purpose. More generally, an adversary can deplete all the outgoing capacity of a node to knock it down from the routing market. This could be easily obtained by routing payments through the victim node with amounts equalling the outgoing capacity of each payment channel. After completing this so-called node isolation attack, the victim cannot send or route payments anymore unless it receives a payment or rebalances its channels.
To conclude, even by design, it is possible to remove edges and nodes from the routable Lightning Network. However, depending on the utilized attack vector, the adversary may have to provide more or fewer resources to carry out the attack.
The Lightning Network is a dynamically changing, permissionless network. Nodes can freely join or leave the network, they can open and create payment channels anytime they want. Therefore, a single static snapshot of the LN graph is misleading. We need to consider the temporality and ever-changing nature of the network. For now, the LN graph is growing in terms of the number of nodes and payment channels. Its effective diameter is also shrinking; that is, nodes become closer to each other, as we can see in The steady growth of the Lightning Network in nodes, channels, and locked capacity (as of September 2021).
In social networks, triangle closing behavior is common. Specifically, in a graph where nodes represent people and friendships are represented as edges, it is somewhat expected that triangles will emerge in the graph. A triangle, in this case, represents pairwise friendships between three people. For instance, if Alice knows Bob and Bob knows Charlie, then it is likely that at some point Bob will introduce Alice to Charlie. However, this behavior would be strange in the Lightning Network. Nodes are simply not incentivized to close triangles because they could have just routed payments instead of opening a new payment channel. Surprisingly, triangle closing is a common practice in the Lightning Network. The number of triangles was steadily growing before the implementation of multipart payments. This is counterintuitive and surprising given that nodes could have just routed payments through the two sides of the triangle instead of opening the third channel. This may mean that routing inefficiencies incentivized users to close triangles and not fall back on routing. Hopefully, multipart payments will help increase the effectiveness of payment routing.
A common metric to assess the centrality of a node in a graph is its betweenness centrality. Central point dominance is a metric derived from betweenness centrality, used to assess the centrality of a network. For a precise definition of central point dominance, the reader is referred to Freeman’s work.
The larger the central point dominance of a network is, the more centralized the network is. We can observe that the Lightning Network has a greater central point dominance (i.e., it is more centralized) than a random graph (Erdős–Rényi graph) or a scale-free graph (Barabási–Albert graph) of equal size.
In general, our understanding of the dynamic nature of the LN channel graph is rather limited. It is fruitful to analyze how protocol changes like multipart payments can affect the dynamics of the Lightning Network. It would be beneficial to explore the temporal nature of the LN graph in more depth.
The LN graph forms spontaneously, and nodes connect to each other based on mutual interest. As a result, incentives drive graph development. Let’s look at some of the relevant incentives:
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Rational incentives:
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Nodes establish channels to send, receive, and route payments (earn fees).
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What makes a channel more likely to be established between two nodes that act rationally?
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Altruistic incentives:
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Nodes establish channels "for the good of the network."
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While we should not base our security assumptions on altruism, to a certain extent, altruistic behavior drives Bitcoin (accepting incoming connections, serving blocks).
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What role does it play in Lightning?
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In the early stages of the Lightning Network, many node operators have claimed that the earned routing fees do not compensate for the opportunity costs stemming from liquidity lock-up. This would indicate that operating a node may be driven mostly by altruistic incentives "for the good of the network." This might change in the future if the Lightning Network has significantly larger traffic or if a market for routing fees emerges. On the other hand, if a node wishes to optimize its routing fees, it would minimize the average shortest path lengths to every other node. Put differently, a profit-seeker node will try to locate itself in the center of the channel graph or close to it.
We’re still in the early stages of the Lightning Network. Many of the concerns listed in this chapter are likely to be addressed as it matures and grows. In the meantime, there are some measures that you can take to guard your node against malicious users; something as simple as updating the default parameters that your node runs with can go a long way in hardening your node.
If you intend to use the Lightning Network to send and receive funds between nodes and wallets you control, and have no interest in routing other users' payments, there is little need to announce your channels to the rest of the network. You could open a channel between, say, your desktop PC running a full node and your mobile phone running a Lightning wallet, and simply forgo the channel announcement discussed in [ch03_How_Lightning_Works]. These are sometimes called "private" channels; however, it is more correct to refer to them as "unannounced" channels because they are not strictly private.
Unannounced channels will not be known to the rest of the network and won’t normally be used to route other users' payments. They can still be used to route payments if other nodes are made aware of them; for example, an invoice could contain routing hints which suggests a path with an unannounced channel. However, assuming that you’ve only opened an unannounced channel with yourself, you do gain some measure of privacy. Since you are not exposing your channel to the network, you lower the risk of a denial-of-service attack on your node. You can also more easily manage the capacity of this channel, since it will only be used to receive or send directly to your node.
There are also advantages to opening an unannounced channel with a known party that you transact with frequently. For example, if Alice and Bob frequently play poker for bitcoin, they could open a channel to send their winnings back and forth. Under normal conditions, this channel will not be used to route payments from other users or collect fees. And since the channel will not be known to the rest of the network, any payments between Alice and Bob cannot be inferred by tracking changes in the channel’s routing capacity. This confers some privacy to Alice and Bob; however, if one of them decides to make other users aware of the channel, such as by including it in the routing hints of an invoice, then this privacy is lost.
It should also be noted that to open an unannounced channel, a public transaction must be made on the Bitcoin blockchain. Hence it is possible to infer the existence and size of the channel if a malicious party is monitoring the blockchain for channel opening transactions and attempting to match them to channels on the network. Furthermore, when the channel is closed, the final balance of the channel will be made public once it’s committed to the Bitcoin blockchain. However, since the opening and commitment transactions are pseudonymous, it will not be a simple matter to connect it back to Alice or Bob. In addition, the Taproot update of 2021 makes it difficult to distinguish between channel opening and closing transactions and other specific kinds of Bitcoin transactions. Hence, while unannouned channels are not completely private, they do provide some privacy benefits when used carefully.
As covered in Denial of Service, nodes that open public channels expose themselves to the risk of a series of attacks on their channels. While mitigations are being developed on the protocol level, there are many steps that a node can take to protect against denial of service attacks on their public channels:
- Minimum HTLC size
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On channel open, your node can set the minimum HTLC size that it will accept. Setting a higher value ensures that each of your available channel slots cannot be occupied by a very small payment.
- Rate limiting
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Many node implementations allow nodes to dynamically accept or reject HTLCs that are forwarded through your node. Some useful guidelines for a custom rate limiter are as follows:
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Limit the number of commitment slots a single peer may consume
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Monitor failure rates from a single peer, and rate limit if their failures spike suddenly
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- Shadow channels
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Nodes that wish to open large channels to a single target can instead open a single public channel to the target and support it with further private channels called shadow channels. These channels can still be used for routing but are not announced to potential attackers.
At present, Lightning nodes struggle with bootstrapping inbound liquidity. While there are some paid solutions to acquiring inbound liquidity, like swap services, channel markets, and paid channel opening services from known hubs, many nodes will gladly accept any legitimate looking channel opening request to increase their inbound liquidity.
Stepping back to the context of Bitcoin, this can be compared to the way that Bitcoin Core treats its incoming and outgoing connections differently out of concern that the node may be eclipsed. If a node opens an incoming connection to your Bitcoin node, you have no way of knowing whether the initiator randomly selected you or is specifically targeting your node with malicious intent. Your outgoing connections do not need to be treated with such suspicion because either the node was selected randomly from a pool of many potential peers or you intentionally connected to the peer manually.
The same can be said in Lightning. When you open a channel, it is done with intention, but when a remote party opens a channel to your node, you have no way of knowing whether this channel will be used to attack your node or not. As several papers note, the relatively low cost of spinning up a node and opening channels to targets is one of the significant factors that make attacks easy. If you accept incoming channels, it is prudent to place some restrictions on the nodes you accept incoming channels from. Many implementations expose channel acceptance hooks that allow you to tailor your channel acceptance policies to your preferences.
The question of accepting and rejecting channels is a philosophical one. What if we end up with a Lightning Network where new nodes cannot participate because they cannot open any channels? Our suggestion is not to set an exclusive list of "mega-hubs" from which you will accept channels, but rather to accept channels in a manner that suits your risk preference.
Some potential strategies are:
- No risk
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Do not accept any incoming channels.
- Low risk
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Accept channels from a known set of nodes that you have previously had successful channels open with.
- Medium risk
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Only accept channels from nodes that have been present in the graph for a longer period and have some long-lived channels.
- Higher risk
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Accept any incoming channels, and implement the mitigations described in Routing Considerations.
In summary, privacy and security are nuanced, complex topics, and while many researchers and developers are looking for network-wide improvements, it’s important for everyone participating in the network to understand what they can do to protect their own privacy and increase security on an individual node level.
In this chapter, we used many references from ongoing research on Lightning security. You may find these useful articles and papers listed by topic in the following lists.
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Jordi Herrera-Joancomartí et al. "On the Difficulty of Hiding the Balance of Lightning Network Channels". Asia CCS '19: Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security (July 2019): 602–612.
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Utz Nisslmueller et al. "Toward Active and Passive Confidentiality Attacks on Cryptocurrency Off-Chain Networks." arXiv preprint, https://arxiv.org/abs/2003.00003 (2020).
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Sergei Tikhomirov et al. "Probing Channel Balances in the Lightning Network." arXiv preprint, https://arxiv.org/abs/2004.00333 (2020).
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George Kappos et al. "An Empirical Analysis of Privacy in the Lightning Network." arXiv preprint, https://arxiv.org/abs/2003.12470 (2021).
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Ayelet Mizrahi and Aviv Zohar. "Congestion Attacks in Payment Channel Networks." arXiv preprint, https://arxiv.org/abs/2002.06564 (2020).
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Marty Bent, interview with Joost Jager, Tales from the Crypt, podcast audio, October 2, 2020, https://anchor.fm/tales-from-the-crypt/episodes/197-Joost-Jager-ekghn6.