SuperEx Educational Series: Understanding Sybil Attack, the Most Hidden Yet Most Common Systemic Threat in the Crypto World

1. Damage to Consensus Mechanisms (Especially PoA, DAG, DPoS)

Certain consensus mechanisms assign trust levels to nodes based on the number of identities they control. By creating a large number of nodes, an attacker can gain majority voting power and control system decisions.

While Proof-of-Work (PoW) and Proof-of-Stake (PoS) are more resilient to direct Sybil attacks due to their inherent costs, light-client networks, Directed Acyclic Graphs (DAGs), sidechains, and some Layer 2 (L2) solutions remain vulnerable.

2. Damage to Governance Systems (DAOs, Voting, Proposals)

Common issues in governance frameworks include:

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  Amassing voting power through numerous identities.
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  Impersonating community members to influence proposals.
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  Creating a false impression of community consensus using multiple identities.

Manipulation of the governance system can render a Decentralized Autonomous Organization (DAO) effectively powerless.

3. Shock to Airdrop Models

Airdrops are intended to reward genuine early users. However, the presence of numerous Sybil participants allows individuals to:

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  Pose as thousands of users with as many addresses.
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  Perform batch interactions and tasks, ultimately claiming a significant portion of the airdrop allocation (30-60%).

This phenomenon contributes to project failures, as airdrop rewards are diverted from real users to script farms, diminishing incentives for legitimate participants.

4. Pollution of Data Metrics (On-Chain Data No Longer Reliable)

Sybil attacks can severely distort key project metrics such as Total Value Locked (TVL), address count, and transaction volume:

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  A single individual using scripts can generate thousands of active addresses.
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  TVL can be artificially inflated through recursive collateral loops.
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  Transaction volume can be endlessly "farmed" via scripts.

This creates a significant industry challenge: increased data does not always equate to increased accuracy; it may simply reflect inflated "fake prosperity."

5. Threats to Fund Security

In certain Decentralized Finance (DeFi) protocols, attackers use multiple identities to:

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  Apply for loans.
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  Exploit incentive loopholes.
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  Manipulate reward pools.
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  Drain protocol liquidity pools.

Instances of such exploits have been observed throughout the industry.

Step 1: Mass Creation of Wallet Addresses

Attackers create a large volume of wallet addresses, ranging from hundreds to thousands, with professional script farms capable of generating tens of thousands. The cost of creating these addresses is effectively zero, representing a significant risk source in Web3.

Step 2: Disguising as Real Users (Constructing Behavioral Footprints)

Attackers aim to mimic genuine user activity by:

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  Performing batch interactions.
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  Batch minting assets.
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  Executing batch swaps.
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  Interacting across multiple chains.
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  Dispersing gas fees across various accounts.
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  Employing diverse "styles" in their behavioral paths.

This sophisticated mimicry can effectively position one Sybil farmer to appear as thousands of "real users."

Step 3: Evading Anti-Sybil Detection

Current Sybil detection methods often rely on analyzing:

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  IP addresses.
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  Shared interaction patterns across addresses.
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  Similar timing patterns.
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  Identical device fingerprints.
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  Similar gas usage patterns.
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  Frequent inter-address transfers.

However, script farms circumvent these measures using:

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  Bulk VPN switching.
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  Mixers.
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  Proxy pools.
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  Randomized delays.
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  Batched intent-based transactions.
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  Multi-region nodes.
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  Randomized breakpoint logging.

This makes detection exceptionally challenging.

Step 4: Launching the Attack at the Critical Moment

Sybil attacks involve extensive preparation, with attackers meticulously forging behavioral footprints over time to strike decisively at opportune moments. These critical junctures include:

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  Immediately prior to a major airdrop distribution.
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  Before governance votes.
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  When incentive distributions commence.
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  During node elections.
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  During Decentralized Exchange (DEX) liquidity incentive periods.
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  During NFT whitelist minting phases.

At these points, attackers deploy their fabricated identities simultaneously to influence decisions or seize resources.

1. Airdrop Sybils (Most Common, Most Widespread, Biggest Impact)

These attacks often involve:

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  Thousands of interactions.
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  Numerous wallets completing identical quests.
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  Sustained activity over several months to appear as "long-term users."

A project might report having 100,000 users, but a significant portion, potentially 80,000, could be script farms.

2. DAO Governance Attacks

Attackers leverage multiple identities to influence:

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  On-chain rules.
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  Treasury utilization.
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  The project's future development roadmap.
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  Crucial voting processes.

This can lead to complete control over some DAOs.

3. DEX Liquidity Incentive Wash-Farming

Attackers utilize multiple identities to:

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  Rotate Liquidity Provider (LP) positions.
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  Conduct wash trading to inflate volume.
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  Farm transaction fees and rewards.
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  Engage in loop arbitrage.

The outcome is that attackers claim most of the incentives, leaving little for genuine users.

4. NFT Whitelist Sybils

In popular NFT projects, bots and scripts frequently acquire whitelist spots, with a single operator potentially controlling hundreds of these valuable allocations.

This results in:

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  Failure to build a genuine community.
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  Unsustainable floor prices.
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  Misaligned interests between minters and the project.
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  Rapid ecosystem decay.

5. Node Forgery Attacks (Extremely Dangerous to Chain Security)

In certain light-client networks and DAG-based structures, attackers can establish a large number of nodes to simulate network majority status.

This represents the most perilous form of Sybil attack.

1. Behavior Analysis

This is a common approach that examines:

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  Consistency of interaction windows.
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  Regularity of time intervals between actions.
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  Identical usage patterns across multiple addresses.

The limitation is that scripts can evade detection by incorporating random parameters.

2. Graph Analysis

Graph analysis focuses on:

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  Transfer network structures.
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  Interconnections between addresses.
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  Similarities in on-chain transaction paths.

Attackers can bypass this by "cross-mixing" transaction paths.

3. Device Fingerprints + IP Identification

This intuitive method can be circumvented by VPNs, multiple devices, scripts, and proxy pools.

4. Economic Cost Models (On-chain Actions Have a Cost)

Examples include airdrops requiring high gas fees, frequent interactions, and substantial activity. Script farms can still execute these actions, albeit at a slightly higher cost.

5. KYC (Most Effective but Least Decentralized)

Know Your Customer (KYC) procedures are highly effective but compromise:

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  User privacy.
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  Permissionless access.
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  The trustless and permissionless nature of DeFi.

Consequently, most projects cannot widely adopt this approach.

6. Trusted Execution Environments (TEE)

Technologies like Intel SGX and privacy-preserving proofs are being explored, but they are not yet mature enough for widespread implementation.