Why Proof Aggregation is a Privacy Nightmare Waiting to Happen

Aggregators like zkSync's Boojum or Polygon zkEVM must see raw transaction data to create a proof. This creates a single point of failure where a centralized sequencer or prover has a complete, deanonymized view of user activity across the entire L2.\n- Data Monopoly: One entity sees all transactions before they're batched.\n- Cross-Chain Correlation: Aggregator can link addresses across L1 and L2.

The aggregator's privileged position enables maximal extractable value (MEV) at an unprecedented scale. With full knowledge of the pending transaction batch, they can perform time-bandit attacks or sandwich attacks with perfect foresight.\n- Guaranteed Profit: Reordering or inserting transactions is trivial with batch control.\n- User Pays Twice: Through both explicit fees and implicit MEV loss.

A centralized proof aggregator is a natural compliance choke point. Regulators can compel a single entity to censor transactions or disclose user data, effectively applying Travel Rule logic to the base layer. This undermines the censorship-resistance of the underlying L1.\n- Single Warrant: One legal order can freeze an entire L2.\n- Protocol-Level KYC: Aggregator could be forced to implement identity checks.

The antidote is to decentralize the proving layer itself. Projects like Espresso Systems (shared sequencer) and RISC Zero (general purpose ZK) are building networks where no single node sees the full transaction graph.\n- Distributed Trust: Multiple provers work on encrypted or sharded data.\n- Cryptographic Guarantees: Validity proofs ensure correctness without data visibility.

To break the data monopoly, transaction data must be hidden until execution. This requires encrypted mempools (e.g., FHE-based designs) or Trusted Execution Environments (TEEs) like Intel SGX to process data in an encrypted enclave.\n- Data Obfuscation: Aggregator processes ciphertext, not plaintext.\n- Hardware-Assisted Privacy: TEEs provide a shielded execution environment.

StarkNet's SHARP and Aztec's privacy-focused zkRollup demonstrate viable paths. SHARP aggregates proofs for many apps, diluting individual data exposure. Aztec uses zero-knowledge proofs by default, ensuring transaction details are never revealed to any aggregator.\n- Proof Batching: Anonymity in a larger crowd of transactions.\n- Privacy-by-Design: The aggregator only sees validity proofs, not data.

Proof aggregation services like EigenDA or Near DA become mandatory checkpoints for L2 state transitions. This creates a centralized vector for state-level censorship, where a single entity or regulatory action can freeze $10B+ in TVL across multiple rollups.

• Network Effect Risk: The most cost-effective aggregator becomes a de facto standard.
• Liveness Dependency: Rollup sequencers are now dependent on a third-party's liveness for finality.

Aggregators see the plaintext execution traces and transaction ordering of every rollup they service. This creates the most powerful MEV extraction engine in history, capable of cross-rollup arbitrage on a scale impossible for individual validators.

• Data Monopoly: The aggregator has a unified view of activity across Arbitrum, Optimism, and zkSync.
• Privacy Erosion: User transaction graphs are no longer isolated to a single chain, enabling sophisticated chain analysis.

The economic model for decentralized proof verification (e.g., EigenLayer restaking) creates perverse incentives. A cartel of large restakers can collude to accept invalid proofs, splitting the stolen funds, because the cost of slashing is distributed across the entire pool.

• Safety ≠ Liveness: The system may remain live but become unsafe.
• Fractional Slashing: Attacker's cost is diluted, making $1B+ attacks economically rational.

Using a shared DA layer like Celestia or EigenDA for multiple L2s links their data roots. Correlation attacks on transaction timing and calldata patterns can deanonymize users across supposedly separate chains, breaking a core privacy assumption of multi-chain ecosystems.

• Metadata Mosaic: Isolated data blobs form a composite behavioral graph.
• Chain Abstraction Backfire: Intents routed through UniswapX or Across leave a unified fingerprint on the DA layer.

Aggregated proofs are optimized for L1 verifiers, not end-users. Light clients must now verify a proof-of-a-proof, requiring them to trust an increasingly complex and opaque stack of cryptographic assumptions (ZK-SNARKs, KZG commitments, DAS), moving further from Ethereum's trust-minimized ideal.

• Verification Black Box: Users cannot practically verify state themselves.
• Trust Cascade: Security depends on the weakest link in a multi-layer stack.

A critical bug in a widely-used proof aggregation library (e.g., a Plonky2 or Halo2 implementation) or the underlying DA layer would simultaneously compromise every connected rollup. This creates systemic contagion risk akin to the Terra/Luna collapse, but for technical infrastructure.

• Common Failure Mode: Diversity in proof systems is erased for cost efficiency.
• Cascading Halts: A single bug triggers a mass withdrawal event across all dependent L2s.

Aggregators like EigenDA and Avail become centralized honeypots of execution data. A single compromise reveals the private state of hundreds of rollups and millions of users, violating the core Web3 tenet of data sovereignty.\n- Attack Surface: One exploit, total chain history leak.\n- Scale: A single service securing $10B+ TVL across multiple L2s.

zk-SNARKs (used by zkSync, Scroll) only prove computational correctness; the plaintext input data must still be published for verification. Aggregation amplifies this by creating a canonical, searchable ledger of all private inputs.\n- Reality: Proving you paid a bill reveals the bill amount and recipient.\n- Consequence: Enables chain analysis on a previously impossible, cross-rollup scale.

Proof aggregation creates a natural bottleneck for Maximal Extractable Value (MEV). Entities controlling the aggregation layer (Espresso Systems, Astria) gain a privileged, omniscient view into cross-rollup transaction flow, enabling sophisticated time-bandit attacks and frontrunning.\n- Power: See intent across UniswapX, CowSwap, and L2 bridges simultaneously.\n- Result: Privacy for users, transparency for extractors.

The only fix is encrypting data before it hits the aggregation layer. Protocols must integrate threshold encryption oracles (e.g., FHE networks, Shutter Network) as a non-optional pre-processing step. This moves the trust from a single aggregator to a decentralized set of key holders.\n- Requirement: Data is encrypted client-side or by a decentralized key network.\n- Outcome: Aggregators handle ciphertext, breaking the data monoculture.

Adopt recursive proof systems like Proof-Carrying Data (used in Succinct's SP1) where privacy is a property of the proof itself, not the data availability layer. This allows for selective disclosure and composes privacy-preserving proofs across the stack.\n- Mechanism: Prove statements about encrypted data without decrypting it.\n- Benefit: Enables private cross-rollup messaging and settlements via LayerZero or Hyperlane.

Stop treating privacy as a bolt-on. Protocol architects must design systems where the aggregator is data-blind by default. This requires choosing stacks (Aztec, Aleo) with privacy primitives at the VM level or enforcing encryption gateways for all inbound data. The cost of retrofitting will be catastrophic.\n- Principle: Assume the aggregation layer is hostile.\n- Action: Audit dependency trees for plaintext data leakage to EigenDA, Celestia.