The cryptocurrency industry has been plagued by hacking incidents and theft since the inception of Bitcoin. Between 2012 and 2024, over 1,740 documented security incidents across the blockchain ecosystem have resulted in approximately $33.744 billion in losses. This trend has escalated in recent years, with crypto security incidents totaling $1.8 billion in 2023 and rising to $2.308 billion by 2024.
Hackers continue to exploit the industry's defenses, leveraging the decentralization and anonymity of crypto assets, which make them attractive targets. Each attack serves as a critical warning, underscoring the necessity of constant vigilance regarding security.
To safeguard funds effectively, crypto participants must enhance their security awareness. This educational series aims to address key security topics, beginning with Sybil attacks.

What Is a Sybil Attack and Why Is It Named "Sybil"?
Sybil attacks can be characterized as:
- •A primary tool for airdrop hunters.
- •A significant source of vulnerabilities in governance voting systems.
- •An inexpensive method for manipulating on-chain data.
- •A root cause of project failures, data inflation, ecosystem collapse, and the draining of liquidity pools.
Even if the concept seems distant, your assets, the Decentralized Applications (DApps) you use, and airdrop programs you've participated in may have already been subtly impacted by Sybil attacks.
A Sybil attack occurs when an attacker creates a multitude of fake identities, such as fake wallet addresses, nodes, or accounts, to unfairly influence the allocation of power, resources, or incentives within a system.
The term "Sybil" originates from a case study of dissociative identity disorder, detailing an individual with 16 distinct personalities. This mirrors the behavior of Sybil attacks, where a single real-world controller impersonates numerous independent participants within the blockchain context.
In centralized systems, identifying Sybil attacks is often straightforward through tracking factors like identical IP addresses, devices, or multi-account operations. However, Web3 environments offer attackers natural camouflage:
- •Infinite address generation is possible.
- •No phone number or ID verification is required.
- •Real-world identity binding is unnecessary.
- •Wallets are free to create.
- •Nodes can be faked.
- •Behavior can be scripted.
These factors significantly reduce the cost and difficulty of launching Sybil attacks, making them the most pervasive and hidden security threats in Web3.
Why Sybil Attacks Constitute "Structural Damage" to Blockchain Systems
The potency of Sybil attacks stems not from the attacker's strength but from a fundamental assumption in blockchain systems: the prevalence of a "majority of honest participants." When attackers achieve majority status through the creation of numerous fake identities, the system's balance is compromised.
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:
- •Amassing voting power through numerous identities.
- •Impersonating community members to influence proposals.
- •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:
- •Pose as thousands of users with as many addresses.
- •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:
- •A single individual using scripts can generate thousands of active addresses.
- •TVL can be artificially inflated through recursive collateral loops.
- •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:
- •Apply for loans.
- •Exploit incentive loopholes.
- •Manipulate reward pools.
- •Drain protocol liquidity pools.
Instances of such exploits have been observed throughout the industry.
How Sybil Attacks Are Carried Out: Attack Path Analysis
While specific implementations vary, Sybil attacks generally follow a clear set of steps.
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:
- •Performing batch interactions.
- •Batch minting assets.
- •Executing batch swaps.
- •Interacting across multiple chains.
- •Dispersing gas fees across various accounts.
- •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:
- •IP addresses.
- •Shared interaction patterns across addresses.
- •Similar timing patterns.
- •Identical device fingerprints.
- •Similar gas usage patterns.
- •Frequent inter-address transfers.
However, script farms circumvent these measures using:
- •Bulk VPN switching.
- •Mixers.
- •Proxy pools.
- •Randomized delays.
- •Batched intent-based transactions.
- •Multi-region nodes.
- •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:
- •Immediately prior to a major airdrop distribution.
- •Before governance votes.
- •When incentive distributions commence.
- •During node elections.
- •During Decentralized Exchange (DEX) liquidity incentive periods.
- •During NFT whitelist minting phases.
At these points, attackers deploy their fabricated identities simultaneously to influence decisions or seize resources.
The Most Common Sybil Attack Scenarios in Crypto
1. Airdrop Sybils (Most Common, Most Widespread, Biggest Impact)
These attacks often involve:
- •Thousands of interactions.
- •Numerous wallets completing identical quests.
- •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:
- •On-chain rules.
- •Treasury utilization.
- •The project's future development roadmap.
- •Crucial voting processes.
This can lead to complete control over some DAOs.
3. DEX Liquidity Incentive Wash-Farming
Attackers utilize multiple identities to:
- •Rotate Liquidity Provider (LP) positions.
- •Conduct wash trading to inflate volume.
- •Farm transaction fees and rewards.
- •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:
- •Failure to build a genuine community.
- •Unsustainable floor prices.
- •Misaligned interests between minters and the project.
- •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.
Six Mainstream Anti-Sybil Mechanisms
While the industry has developed a comprehensive toolkit for combating Sybil attacks, no single method is foolproof.
1. Behavior Analysis
This is a common approach that examines:
- •Consistency of interaction windows.
- •Regularity of time intervals between actions.
- •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:
- •Transfer network structures.
- •Interconnections between addresses.
- •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:
- •User privacy.
- •Permissionless access.
- •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.
Three Fundamental Web3 Properties That Make Sybil Attacks Persistent
- Costless Address Creation: It is impossible to prevent individuals from generating vast numbers of addresses.
- Inherently Anonymous User Identities: Blockchains are designed to function without requiring real-world identity verification.
- Disguised On-chain Behavior:
- •Scripts can simulate infinite users.
- •Transaction paths can be mimicked.
- •Interactions can be duplicated.
- •Suspicious activity can be concealed.
- Attraction of Script Farms to Incentives: Wherever financial incentives exist, Sybil attacks will likely follow.
Future Anti-Sybil Trends in Web3
- •Zero-Knowledge (ZK)-based Identity (ZK-ID): Enabling verification of real human users without revealing their personal information.
- •Soulbound Identity Systems: Enhancing the non-transferability of accounts to prevent identity theft and manipulation.
- •Dynamic Participation Rating (DPR): Assigning greater weight to participants demonstrating genuine and consistent engagement.
- •Cross-Chain Identity: Evaluating user authenticity based on aggregated behavior across multiple blockchain networks.
- •High-Frequency Behavioral Graphing (with AI): Leveraging artificial intelligence to play a central role in future Anti-Sybil defense systems.
Conclusion: Sybil Attacks Will Never Disappear, but They Can Be Managed
Sybil attacks are an inherent structural feature of Web3 and will persist. However, the industry must focus on:
- •Understanding their mechanisms.
- •Developing effective identification methods.
- •Implementing robust management strategies.
- •Building Sybil-resistant systems.
- •Designing incentive models resilient to such attacks.
This responsibility lies not only with project teams but is also crucial for the sustainable and healthy growth of the industry.
- •Comprehending Sybil attacks provides insight into the "real world" of Web3.
- •Understanding how to defend against them illuminates the future direction of Web3.


