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Moderate 60 Bitcoin

ln: use outer onion values for trampoline NextPacketDetails

Public commit record

What the developer wrote

Authored by Carla Kirk-Cohen

85/100 · Strong
ln: use outer onion values for trampoline NextPacketDetails

When we receive trampoline payments, we first want to validate the
values in our outer onion to ensure that we've been given the amount/
expiry that the sender was intending us to receive to make sure that
forwarding nodes haven't sent us less than they should.
✓ Specific, descriptive subject✓ Names a concrete action or component✓ Provides detailed explanatory context✓ Explains rationale or failure mode
The short version

What changed, and why it matters

This change fixes how trampoline-style Lightning payments decide how much money and when (time lock) to forward to the next hop. Previously the code used values from the inner trampoline onion, which could differ from what the forwarding node actually received in the outer onion. Now it uses the outer onion values, so a forwarding node cannot silently pay the next hop less than it should while still satisfying the inner trampoline instructions. This prevents a potential fee/amount mismatch in multi-hop trampoline routing.

Recommended action

Review the full trampoline payment flow to confirm outer_hop_data is authenticated and cannot be manipulated independently of the inner trampoline payload. Ensure the removed blinded-forward constraints are enforced elsewhere before this point. Consider adding regression tests that verify a forwarding node cannot substitute lower outer-onion values for trampoline forwards.

Security signals we found

01

Amount/CLTV mismatch between outer and inner trampoline onions

02

Removal of blinded-forward amount/cltv recalculation in favor of outer onion values

03

Trampoline forwarding logic change in payment onion decoding

04

Potential underpayment or premature expiry to next trampoline hop

Risk score

Why this scored 60/100

Our methodology →
Potential impact 18/30
Exploitability 14/25
Stealth signal 10/15
Affected reach 8/15
Confidence 7/10
Evidence quality 3/5
Human-validated context

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