|categories: lightning, denial-of-service
Jamming is a denial-of-service attack on Lightning channels. The attacker initiates payments (jams) that consume the resources of routing nodes and then fail. Jamming decreases the profitability and reliability of routing nodes. The attack is cheap and difficult to prevent.
Channels have two types of limited resources: capacity (the number of coins locked-up at channel opening) and payment slots. A channel can only hold up to a certain number of in-flight payments simultaneously. Each in-flight payment occupies one payment slot. This limitation stems from the fact that each channel state is encoded in a commitment transaction, which must be publishable on-chain to enable dispute resolution. Each in-flight payment is represented by an output in the commitment transaction, increasing its weight. Transactions above a certain weight limit are considered non-standard and not propagated. Therefore, the Lightning protocol limits the number of payment slots per channel at 483.
Depending on which resource is primarily targeted, jamming may be capacity-based or slot-based.
In capacity-based jamming, the attacker targets capacity by sending high-value jams.
A payment of
X coins locks
X coins in-flight in every channel along the route.
Slot-based jamming targets payment slots of victim channels.
A slot-based attack is more capital-efficient as jams may have lower value.
Jamming may also be classified as controlled and uncontrolled. In controlled jamming, the attacker runs both the sender and the receiver of jams. The receiver can keep jams in-flight for hours or even days by not revealing the payment secret. In uncontrolled jamming, the attacker sends unsolicited “fake” payments to arbitrary targets. Uncontrolled jams fail within seconds, but they too consume the resources of routing nodes. The attacker may lock up the resources of routing nodes by sending many uncontrolled jams.
Preventing jamming is non-trivial for two main reasons. First, Lightning payments are onion-routed to enhance privacy. Intermediary nodes only know the immediate previous and next node in the route, but not the ultimate sender or receiver. This means that routing nodes cannot easily rate-limit or ban attackers. Second, failed payment attempts are free. The attacker only bears the cost of temporary lock-up of their own resources.
Preventing jamming would improve Lightning reliability and facilitate its widespread adoption. If jamming is not efficiently prevented, routing nodes could request identification from their peers, diminishing Lightning’s privacy guarantees.
Restricting payment forwarding may deter jamming to some extent. Node configuration doesn’t require changes in the Lightning protocol. Configuration changes may include:
- Decrease payment timeouts (against controlled jamming). The attacker would have to send more jams per unit time to keep a channel jammed. The drawback is a shorter time window to claim in-flight payments after channel closure.
- Increase the minimal payment amount (against slot-based jamming). The attacker would need more of their own capital temporarily locked-up. The drawback is the loss of potential revenue from forwarding honest low-value payments.
- Divide payment slots into groups for different amount buckets. A routing node would forward small payments through a subset of slots, always keeping some slots open for larger payments. This increases the attacker’s capital requirements. The drawback, again, is lost revenue: some honest payments may be rejected if their slot bucket is full, even if other slots are available.
- Limit the number of in-flight payments from less trustworthy peers (i.e., a local reputation system). This may hinder attacks coming from an immediate peer but may also affect honest peers that are unknowingly forwarding jams.
Configuration changes discourage simplistic attacks but don’t solve the issue fundamentally.
Proposed anti-jamming countermeasures include reputation systems and fees for failed payments. Both approaches have benefits and drawbacks. Reputation schemes weaken privacy guarantees, whereas extra fees introduce new game-theoretical and UX challenges. A practical solution could combine the two approaches.
Direct peer reputation is a scheme by which nodes assign reputation scores to their direct peers. Bob decreases Alice’s reputation if a payment she asked him to forward doesn’t resolve quickly. The fundamental drawback of direct peer reputation is that it punishes honest routing nodes who unknowingly forward jams. The attacker, in turn, can deny responsibility by claiming to be an honest routing node.
The Provable blaming scheme aims to properly assign blame for stuck payments. If a node delays payment resolution, the previous node in the route should close their shared channel. Consider a route: Alice - Bob - Charlie - Dave. Suppose that Alice and Dave are cooperating to jam the channel between Bob and Charlie. Alice initiates the payment, and Dave doesn’t reveal the payment secret. After a grace period, Charlie is supposed to close the channel to Dave. If Charlie doesn’t prove the closure to Bob, Bob should blame Charlie for the delay and close the channel to him. If Bob doesn’t prove the closure to Alice, Alice should blame Bob and close the channel to him. Each node either punishes the presumed attacker or gets punished by its upstream peer. The drawback of provable blaming is that closing channels threatens the overall network connectivity. Channel closure also requires paying on-chain fees.
Another approach is to require nodes to commit to some scarce resource in exchange for payment credits, which are then spent to forward payments.
One such resource is ownership of a Bitcoin unspent transaction output (UTXO), as described in the stake certificates proposal (an instance of a fidelity bond). A routing node assigns credits to the sender based on the value of a UTXO it owns. UTXO ownership may be proven in zero-knowledge to preserve privacy. The routing node would only know that the sender controls some UTXO from the current UTXO set that has the value in a certain range.
Many design and implementation questions remain open:
- Which ZK scheme should be used?
- What is the optimal credits-to-UTXO-value function?
- When should stake certificate expire?
Another way to assign payment credits is proof-of-work (PoW) (mailing list thread, another thread). To forward a payment, the sender provides a proof of performed computation to a routing node. The work should be specific to the payment. Implementing a PoW-based scheme also has open questions:
- How to set the difficulty? A low difficulty wouldn’t sufficiently deter attackers, while a high difficulty would deter honest users.
- Which function should be used for PoW? A SHA-256-based PoW scheme would provide a strong advantage to already deployed Bitcoin mining farms. Alternative “ASIC-resistant” hash functions have been designed to discourage hardware specialization.
Currently, LN fees are paid only if the payment succeeds. Upfront fees, in contrast, are paid to initiate a payment. Upfront fees may be refundable or non-refundable. Non-refundable upfront fees impose a cost on failed payment attempts.
In the simplest forward upfront fee scheme, each node pays a fee to the next node to forward a payment. Fee amounts should decrease along the route to provide motivation for routing nodes. If fees are equal on all hops, the attacker who controls the sender and the receiver would pay zero fee. The drawback of forward upfront fees is that routing nodes are motivated to deliberately fail payments and keep the fee.
In the Reverse upfront fees scheme, nodes pay to the previous node (that is, for receiving a forwarding request, instead of sending it). Fee amounts for reverse fees should increase along the route. The fee should be zero during a grace period: no fees are paid if a payment resolves quickly. Without a grace period, an uncontrolled jamming attack would force nodes to pay fees with no cost for the attacker. It is an open question how long the grace period should be and when exactly it should start.
The Bidirectional upfront fee scheme combines forward and reverse upfront fees. Forward fees are non-refundable, whereas reverse fees are only refundable within the grace period.
Nested incremental routing addresses the incentive compatibility concern with upfront fees (routing nodes deliberately failing payments). The sender builds a route iteratively, unlike the current protocol, where the route is fully determined before the payment is initiated. To send a payment to Dave, Alice first communicates with Bob and offers him an upfront fee to forward a payment to Charlie. Bob then offers an upfront fee to Charlie. If Charlie cannot forward the payment, Bob tries forwarding it through his other peer Carol, and so on. At any point, the maximum fee value that a routing node can steal is one-hop worth of upfront fees. In contrast, if the route is fully determined by the sender, the upfront fee is proportional to the route length, providing a stronger incentive for routing nodes to misbehave (especially those close to the sender). The drawback of nested incremental routing is high communication cost. Multiple round trips increase payment latency and facilitate timing-based privacy attack.
UX is a key challenge for upfront fee schemes. Users may not be prepared to pay for failed payment attempts. (This may be less of an issue if most attempts succeed.) Professional routing nodes may also choose to voluntarily refund upfront fees if a payment fails.
Another important consideration is whether fee amounts should be proportional to the time it took a payment to resolve. Fees schemes with constant amounts are simpler but don’t reflect the fact that the longer a payment is held, the more resources it consumes. Time-dependent fee amounts require a trusted high-precision time source (timestamps in Bitcoin blocks are not fine-grained enough).
- Cristina Pérez-Solà et al. in LockDown: Balance Availability Attack against Lightning Network Channels focus on the capacity-based jamming attack and quantify its efficiency in terms of the ratio of the victim’s locked capital to the attacker’s capital.
- Rohrer et al. in Discharged Payment Channels: Quantifying the Lightning Network’s Resilience to Topology-Based Attacks analyze attacks on the LN including jamming (“channel griefing”). The authors focus on the attacker’s strategies, namely, which nodes or channels should be prioritized as targets, given a network graph.
- Mizrahi and Zohar in Congestion Attacks in Payment Channel Networks describe controlled slot-based jamming. The authors consider three potential goals for the attacker: blocking channels with most funds, separating as many pairs of nodes as possible, and cutting a victim node from the rest of the network. The paper also compares default parameters for the three prevalent LN implementations (LND, c-lightning, and Eclair) and estimates their respective shares in the public network. As countermeasures, the authors suggest shorter timelocks, shorter routes, peer reputation, and preventing looped payments.
- Naumenko and Riard in Stake Certificates propose using UTXO ownership proofs (or stake certificates) as a countermeasure against jamming.
There has been no commentary on this problem so far
PTLC Cycle Jamming is a special case of jamming for a new proposed type of LN channels (PTLC-based). With PTLC-based channels, hops within one route are unlinkable, which makes jamming attacks more efficient.