How Can I Aggregate Multiple zkBridge Attestations Into a Single Proof to Cut Gas Fees on Ethereum?

A practical, engineering-first guide to batching zkBridge attestations with recursive proofs, blob-first data pipelines, and BLS-backed attestations—so you slash on-chain gas by 70–99% without compromising security.

In short: compress many attestations into one succinct proof, ship data via blobs, keep public inputs tiny, and verify once on L1.

TL;DR

Verifying a single Groth16 proof on Ethereum typically costs ~220k gas; STARK verification is >1M gas. Aggregating N attestations into one proof turns N verifications into 1, cutting gas by 70–95%+ depending on your design. (medium.com)
Make your pipeline blob‑first. After Dencun (Mar 13, 2024) and Pectra (May 7, 2025), Ethereum provides cheap, short‑lived blob space plus a point‑evaluation precompile (0x0A, 50k gas) and higher blob capacity (target/max 6/9). Calldata for data‑heavy posts got pricier (EIP‑7623), so you want proofs on-chain and bulk data in blobs. (blocknative.com)
Three battle-tested aggregation patterns:
1. Header batching via recursive proofs (Polyhedra zkBridge style).
2. Message-level batching under one or more headers (membership proofs verified in‑circuit).
3. Off-chain verification networks + on-chain BLS aggregate attestations (now cheap with EIP‑2537). (rdi.berkeley.edu)

Why aggregate zkBridge attestations at all?

Every zkBridge hop has two big L1 costs:

Computation: verifying pairings/MSMs in the verifier contract.
Data: shipping proof bytes and inputs (formerly calldata-heavy).

Measured baselines:

Groth16 verify on BN254: ~207–220k gas plus ~7k per public input.
STARK verifiers: >1M gas.
Point-evaluation for blob KZG: fixed 50k gas. (medium.com)

When you verify each attestation separately, costs scale linearly. With aggregation, you amortize verification into one constant-cost check, and move bulk data to blobs.

Pattern A — Batch block headers with recursive proofs (validity light clients)

This is the approach popularized by Polyhedra’s zkBridge: prove many source-chain headers off-chain, compress recursively, and verify one succinct Groth16 on the destination chain.

How it works (concretely):

Generate a fast “inner” proof for header validations (e.g., via deVirgo / GKR).
Wrap that proof with a Groth16 “outer” proof so the on-chain verifier stays ≈constant.
Make the batch size N tunable: bigger N → lower cost per header but more latency.
Store or emit the new light-client state in the updater contract once the proof verifies. (rdi.berkeley.edu)

Numbers to budget for:

Two-layer recursion shrinks on-chain verify to ~220–230k gas, versus tens of millions of gas for naïve light clients.
Teams commonly collect N headers and prove them together; N can be dialed based on SLA and gas markets. (rdi.berkeley.edu)

Why it’s attractive to decision‑makers:

Security: validity proofs—no external trust.
Cost: near-constant gas per batch.
Flexibility: dial latency vs. cost by tuning N and prover horsepower (deVirgo scales across M machines roughly linearly in M). (blog.polyhedra.network)

Best emerging practice

Keep public inputs tiny—ideally just the final light-client commitment(s) and a batched header commitment. Gas grows with public IO, not with “stuff hidden inside the SNARK.” (medium.com)

Pattern B — Aggregate message attestations under Merkle commitments

When the business object is “many application messages” rather than “many headers,” batch them under roots and prove the inclusions inside one circuit.

Design sketch that works today:

For each source block i, collect all messages destined for chain D.
Build a Merkle tree of messages; keep the root R_i.
Circuit checks:
a) R_i is authentic to block i (via the chain’s header fields and receipt/state proofs).
b) Each message’s Merkle branch to R_i.
c) Optional: additional predicates (e.g., nonce monotonicity, replay protection).
Recursively fold multiple blocks’ message trees into one proof with public input = a “root of roots” plus minimal metadata. One on-chain verify authorizes K messages.

Why it’s cheaper:

You don’t pay 220k gas per message; you pay ≈220–350k gas once per batch. (medium.com)

Implementation tips that move the needle:

Avoid bringing large Keccak footprints on‑circuit. If you must, batch them efficiently or leverage newer Keccak-batching approaches (e.g., “keccacheck”) to lower constraints. Otherwise, use layouts that rely on KZG / commitments aligned with blob data. (iacr.org)
Separate data from verification: keep bulk message payloads in blobs and verify only commitments on-chain. Use the 0x0A point‑evaluation precompile when you need to authenticate blob contents against commitments. (core.eips.fyi)

Pattern C — Let a verification layer batch for you; verify one BLS aggregate attestation on L1

Instead of verifying every ZK proof directly on Ethereum, you can:

Verify proofs on a specialized verification layer (or committee).
Get an aggregated BLS attestation over the verification result set.
Verify that one aggregate signature on Ethereum using native BLS12‑381 precompiles added in Pectra (EIP‑2537). (eips.ethereum.org)

What changed in 2025:

Pectra added seven BLS12‑381 precompiles (0x0b–0x11), including multi‑pairing checks and MSMs. This makes aggregate signature verification cheap and secure (~128‑bit), and often cheaper per pairing than legacy BN254. (eips.ethereum.org)

Concrete economics (representative figures):

Direct Groth16 on Ethereum: >250k gas each.
Via a verification layer: ≈350k gas base to accept a batch, ≈“hundreds” of proofs amortized to sub‑1k gas each on-chain, because you verify one attestation once. (docs.alignedlayer.com)

When to choose this:

You prioritize extreme cost reduction and low on-chain complexity, and you’re comfortable with the verification layer’s trust and liveness model.

Make the pipeline blob‑first (post‑Pectra reality)

Two protocol shifts matter for cost control:

Blobs are cheap, temporary DA

EIP‑4844 introduced blobs (≈128 KiB each, pruned after ~18 days) with their own fee market—generally 10–100x cheaper than calldata. Verification contracts can authenticate blob content via the 0x0A precompile (50k gas). (blocknative.com)

Pectra tilted the field toward blobs even more

EIP‑7691 increased blob capacity (target/max 6/9), while EIP‑7623 raised floor costs for data‑heavy calldata. Net: use blobs for data, keep calldata minimal. (eips.ethereum.org)

Practical knobs to turn:

Fill your blobs: you pay for the whole blob; pack batches to near 128 KiB to avoid waste. (github.com)
Failover logic: only fall back to calldata during extreme blob congestion; with 6/9 blobs and asymmetric fee responsiveness, blob prices decay faster in lulls and rise slower during surges. (eips.ethereum.org)

Worked example: batching 64 zkBridge attestations into one proof

Scenario

You receive 64 attestations (message inclusions across several source headers) every 2 minutes.
You target “under 1 minute” end-to-end on Ethereum mainnet.

Design

Use a recursion tree to fold 64 leaf checks into one Groth16 wrapper.
Public inputs: a single commitment to all included messages, latest verified source header commitment(s), and anti‑replay metadata.
Data path: publish per‑message payloads and auxiliary paths in blobs; keep only the final commitments in calldata.
Verification: one call to your Groth16 verifier; optionally, one or two KZG point‑evaluation checks if you also need to bind the batch to an on-chain referenced blob commitment. (core.eips.fyi)

Budget

On-chain verify: ≈220–300k gas (Groth16, low public IO).
Two KZG point evaluations (if used): 2 × 50k = 100k gas.
Total per batch: ≈320–400k gas instead of 64 × 220k = 14.1M gas. That’s >97% gas reduction in the verifier step. (medium.com)

Latency

Proving time is the new bottleneck. With distributed provers (e.g., deVirgo), header proofs in the wild are reported in the 10–20s range; message circuits vary with depth and hashing. Use recursion and GPU/clustered proving to keep within your SLO. (blog.polyhedra.network)

Implementation checklist (what we deploy for clients)

Circuits and recursion strategy

Leaf: verify per‑attestation membership against source headers; normalize formats early.
Mid-node: fold 8–16 leaves per recursion node (good balance for Halo2/Plonk-style recursion).
Wrapper: Groth16 on BN254 or BLS12‑381; keep public inputs to a handful of 32‑byte words. (medium.com)

Public IO discipline

Expose only commitments (root(s), batch metadata hash, destination address).
Avoid exposing per-message fields as public inputs; verify inside the circuit to keep verifier gas flat. (medium.com)

Verifier contracts

Cache verification keys; isolate upgrade paths.
If you rely on an off-chain verification network, add a BLS12‑381 fast-aggregate verify path (k=2 pairing typical), now native via EIP‑2537. (eips.ethereum.org)

Blob integration

Batch off-chain data and auxiliary paths into full blobs; store versioned hashes on-chain.
Use the point‑evaluation precompile when you must check blob content authenticity in contracts. (core.eips.fyi)

Batching policy

Dynamic N based on blob base fee and demand. With EIP‑7691’s 6/9 knobs, prices drop faster when under target; let your scheduler auto‑increase N in cheap windows. (eips.ethereum.org)

Observability and fallback

Metrics: prove time distribution, queue age, blob fill %, and on-chain verify gas.
Fallback: per‑attestation verification path for “trickle” traffic; switch to aggregation above a threshold.

Gas math you can actually plan around

Groth16 verify baseline: ~207–220k fixed + ~7k per public input (BN254). Keep inputs tiny to stay near the floor. (medium.com)
STARK verifier on L1: often >1M gas—prefer “STARK → SNARK wrapper” when you need on-chain verification. (docs.alignedlayer.com)
BLS12‑381 aggregate signature verify: two-pairing path is ~100–130k gas range with EIP‑2537; scale to distinct‑message aggregates by passing n+1 pairs into one call. (eips.ethereum.org)
KZG point evaluation (0x0A): 50k gas each. (core.eips.fyi)

Rule of thumb for CFOs

64 separate L1 zk verifies: multi‑million gas.
One aggregated verify + a couple of KZGs: few hundred thousand gas. Your savings are typically 90–99% on the verification line item.

Engineering pitfalls (and how to avoid them)

Keccak on-circuit is expensive. Batch it aggressively or lean on newer “Keccak in ZK” research if your format forces it. Where possible, base your commitments on KZG or SNARK‑friendly hashes. (iacr.org)
Don’t let calldata balloon. Post data in blobs; expose only commitments in calldata. EIP‑7623 made data‑heavy calldata structurally pricier. (eips.ethereum.org)
Public input creep breaks your gas model. Audit public IO before shipping; 10 extra inputs adds ~70k gas to every verify. (medium.com)
Latency vs. cost: aggregation lowers cost, not necessarily latency. If you need seconds-level SLAs, consider folding/IVC to “stream‑aggregate” rather than holding for huge batches. (7blocklabs.com)

Where the ecosystem is heading (and why it helps you)

Production‑grade recursive stacks are already live in zk bridges. Polyhedra reports two‑layer recursion with constant‑ish verify costs and tunable batching. (rdi.berkeley.edu)
BLS12‑381 is first‑class on Ethereum since Pectra—so aggregated attestations are now cheap and secure to check on L1. (blog.ethereum.org)
Blob capacity increased (EIP‑7691) with asymmetric fee responsiveness—faster cost decay when demand is low. This makes “blob‑first batching” both cheaper and more predictable. (eips.ethereum.org)

A step‑by‑step plan you can execute this quarter

Pick your aggregation pattern (A, B, or C) based on trust model and SLA.
Refactor the prover pipeline for recursion: leaf → mid → wrapper.
Redesign public IO: one or two 32‑byte commitments only.
Move data to blobs; add KZG checks if you need on-chain authenticity proofs for blob content.
Ship EIP‑2537 code paths for aggregated BLS attestations if you use an external verifier network.
Implement a dynamic batcher that reacts to blob base fee and queue depth.
Instrument everything; set SLOs for “time to proof,” “gas per verify,” and “blob fill %.” (core.eips.fyi)

Frequently asked executive questions

How much will we save?
Typical clients see verification gas drop from millions to a few hundred thousand per batch—90–99% savings—plus reduced variance from blob-first DA. Exact savings depend on your public inputs and batch size. (medium.com)
Will this slow us down?
Aggregation cuts cost; latency depends on proving time and batch policy. Use folding/IVC or smaller recursion fan‑outs to keep latency under your SLA. (7blocklabs.com)
Is it safe?
Validity-based patterns (A, B) rely on ZK soundness and Ethereum execution; pattern C adds a verifier network/committee trust assumption but keeps on-chain checks minimal via native BLS. (eips.ethereum.org)

References and further reading

Polyhedra zkBridge architecture: deVirgo + recursive proofs; constant-ish on-chain cost; header batching. (rdi.berkeley.edu)
Batch sizes and gas: Groth16 ≈220k; add ~7k per public input. (medium.com)
Dencun/EIP‑4844: blobs, 0x0A precompile (50k), blob economics. (blocknative.com)
Pectra mainnet and EIP‑2537 (BLS12‑381 precompiles). (blog.ethereum.org)
Blob capacity increase and fee responsiveness (EIP‑7691); calldata repricing (EIP‑7623). (eips.ethereum.org)

If you’re planning a zkBridge deployment or retrofitting an existing bridge to aggregate attestations, 7Block Labs can deliver a blob‑first, recursion‑powered pipeline with predictable gas and latency envelopes—using the exact design patterns above.