Launching a Governance Attack Simulation (Wargaming) Protocol

Decentralized governance is a core innovation of Web3, enabling communities to manage protocols through token-based voting. However, this system introduces unique attack vectors like vote manipulation, proposal spam, and treasury draining. A governance attack simulation is a structured exercise where security researchers role-play as attackers to probe these weaknesses. The goal is not to break the system maliciously, but to discover flaws in the governance smart contracts, economic incentives, or social processes. This practice, often called wargaming, is essential for protocols managing significant value, as seen in DAOs like Uniswap, Compound, and Aave.

Launching a simulation requires a clear scope and rules of engagement. First, define the attack surface: this includes the governance contract (e.g., OpenZeppelin's Governor), the token contract, the treasury, and any delegated voting systems. Researchers then develop attack scenarios, such as a 51% token attack to pass malicious proposals, a flash loan attack to temporarily acquire voting power, or a governance fatigue attack designed to spam the system with proposals. Each scenario is executed in a forked mainnet environment using tools like Hardhat or Foundry, allowing for safe, realistic testing without risking real funds.

The technical setup involves forking the target blockchain. Using Foundry's cast command, you can fork Ethereum mainnet at a specific block: anvil --fork-url $RPC_URL --fork-block-number 19238201. This creates a local testnet replica. Next, impersonate a whale account to simulate an attacker with large capital: cast rpc anvil_impersonateAccount 0xWhaleAddress. You can then directly interact with the forked governance contracts to test proposal creation, voting, and execution. This method provides a high-fidelity environment to audit the on-chain logic and discover vulnerabilities like insufficient proposal delay or flawed quorum calculations.

Beyond technical exploits, simulations must assess social and economic layers. An attacker might exploit voter apathy to pass proposals with low turnout or use bribery markets like Hidden Hand to concentrate voting power. Testing these scenarios involves analyzing historical voting data, simulating incentive structures, and modeling voter behavior. The findings are documented in a report detailing the vulnerability, its impact (e.g., Critical, High), a proof-of-concept, and recommended mitigations, such as implementing a timelock, adjusting quorum thresholds, or adding proposal sponsorship requirements.

Ultimately, governance wargaming shifts security from reactive to proactive. By continuously stress-testing the assumptions and code of a DAO, teams can harden their systems against both known and novel attacks. This process builds E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) for the protocol, assuring users and stakeholders that their governed assets are protected. Regular simulations should be integrated into the development lifecycle, especially before major protocol upgrades or treasury allocations.

A governance attack simulation, or wargame, requires a controlled environment to safely test exploits. You will need a forked mainnet state of the target protocol. Use tools like Foundry's anvil or Hardhat Network to fork Ethereum mainnet at a specific block. This creates a local sandbox with real contract states and token balances. For example, to fork mainnet with Anvil: anvil --fork-url $RPC_URL --fork-block-number 19283746. This allows you to interact with live protocol contracts without spending real ETH.

Your development environment must include a smart contract testing framework. Foundry is highly recommended for its speed, built-in fuzzing (forge test), and cheat codes (vm.prank, vm.startPrank) that are essential for simulating malicious actors. Alternatively, Hardhat with TypeScript offers a robust ecosystem. Install Foundry via curl -L https://foundry.paradigm.xyz | bash and run foundryup. You will write attack simulations in Solidity as test contracts, not scripts, to leverage the full testing suite.

You must acquire the target protocol's governance tokens to simulate voting power. On your forked network, impersonate a whale address using vm.startPrank(WHALE_ADDRESS) in Foundry or hardhat_impersonateAccount in Hardhat. Then, interact directly with the token contract to transfer tokens to your test contract address. Understanding the governance mechanism is critical: study the voting contract ABI, proposal lifecycle, quorum requirements, and timelocks. These details are found in the protocol's documentation and verified Etherscan contracts.

Finally, structure your simulation as a standalone test file. Import the target protocol's interfaces using GitHub submodules or package managers like npm or forge install. A basic test structure in Foundry includes a setUp() function to fork the chain and a test function prefixed with test_. For example, test_SimulateProposalHijack() would contain the logic for proposing, voting on, and executing a malicious proposal. Always document each step of the attack vector clearly within the test comments.

Governance wargaming involves systematically testing a protocol's on-chain governance mechanisms against potential attack vectors. The core objective is to stress-test the assumptions and parameters of a governance system—such as proposal thresholds, voting periods, and quorum requirements—in a controlled, off-chain environment. This practice is critical because governance attacks, like proposal spam, vote manipulation, or treasury drain proposals, can lead to catastrophic fund loss or protocol takeover. Unlike traditional smart contract audits, wargaming focuses on the social and economic layers of a protocol, examining how tokenomics and voter behavior can be weaponized.

Launching a simulation requires a detailed model of the target protocol's governance contracts. You must replicate the exact logic of the Governor contract (e.g., OpenZeppelin's Governor, Compound's Governor Bravo), the token contract with its delegation mechanics, and any time-lock or execution modules. The simulation environment, often built with frameworks like Foundry or Hardhat, loads a forked state of the mainnet or a custom snapshot. This allows you to interact with the contract clones using test accounts that represent various actors: a malicious attacker, large token holders (whales), passive delegates, and protocol defenders.

A fundamental concept is the attack narrative, which defines a specific scenario to test. Common narratives include testing the minimum proposal submission threshold by simulating a whale acquiring just enough tokens, or exploiting a low quorum by timing a proposal when voter participation is historically low. Another critical test is for vote buying or bribery mechanics, where an attacker incentivizes delegates to vote a certain way. Each narrative is executed by scripting a series of transactions that mirror a real attacker's actions, from acquiring governance tokens to submitting and passing a malicious proposal.

The analysis phase involves reviewing the simulation results to answer key questions: Was the attack successful? What was the financial cost to the attacker? Which governance parameters were the decisive factors? Findings are documented as parameter vulnerabilities (e.g., "A 1% proposal threshold is too low") or logic flaws (e.g., "The timelock does not delay updateGovernance function"). The final output is a set of actionable recommendations for the protocol team, which may include adjusting numeric parameters, adding new security modules like a governance security council, or implementing defensive mechanisms such as proposal vetting by a multisig before execution.

A governance attack simulation framework is a specialized testing environment designed to replicate the core components of a live Decentralized Autonomous Organization (DAO). This includes a forked version of the target protocol's smart contracts, a simulated token distribution, and a set of agent-based models representing various stakeholders like honest voters, whales, and malicious actors. The primary goal is to create a deterministic sandbox where you can programmatically execute governance proposals, manipulate voting power, and observe the system's behavior under adversarial conditions. Tools like Foundry or Hardhat are typically used to manage this forked blockchain environment.

To begin, you must fork the mainnet state of the protocol you intend to analyze. Using Foundry's cast or an RPC provider like Alchemy, you can create a local snapshot at a specific block. This snapshot includes the exact state of all relevant contracts and token balances. Next, you deploy your simulation engine—a set of orchestrator contracts and scripts that can programmatically create proposals, delegate tokens, and cast votes. A critical component is the agent model, which defines the logic for different participant types. For example, a MaliciousWhale agent might be programmed to accumulate tokens via flash loans and vote in a specific pattern to pass a malicious proposal.

Your framework needs to instrument the simulation to collect crucial data. This means logging every transaction, vote cast, and state change. Key metrics to track include proposal success rate, voter turnout, cost of attack (in gas and borrowed capital), and the time to execution. You should also implement checks for specific vulnerabilities, such as whether a proposal can bypass timelocks or if voter apathy thresholds can be exploited. Structuring your simulation as a repeatable test suite allows you to run hundreds of iterations with varying parameters to identify statistical weaknesses in the governance system.

Finally, consider the initial conditions of your simulation. You must accurately model the real-world token distribution—who holds the tokens, what percentage is staked or delegated, and what the historical voter participation looks like. Incorrect assumptions here will invalidate your results. A robust setup also includes failure scenarios and sensitivity analysis, testing how changes in parameters like quorum requirements or proposal duration affect the system's resilience. This foundational step transforms abstract attack concepts into executable, measurable experiments, providing the empirical data needed to understand and fortify governance mechanisms.

The core of a governance attack simulation is modeling the vote power distribution of a protocol. You need to ingest real on-chain data to create a realistic starting point. This involves querying a blockchain indexer like The Graph for a snapshot of token holdings and delegated votes for a specific DAO or protocol at a given block. Tools like Dune Analytics or Flipside Crypto can also provide this aggregated data. The simulation's initial state is a mapping of addresses to their voting power, which forms the basis for all subsequent attack modeling.

With the real distribution loaded, you can programmatically define attack scenarios. A common simulation tests proposal tipping points. You calculate the minimum coalition size needed to pass or veto a proposal based on the protocol's quorum and approval thresholds. For example, you might write a script that iteratively adds the largest remaining token holders to a "colluding" group until the sum of their votes exceeds 51% of the circulating supply or meets a specific proposal's passing criteria. This reveals the protocol's vulnerability to a minimum viable collusion attack.

More advanced simulations model vote buying and bribery markets. This involves creating a separate economic model within the simulation where attackers have a budget (e.g., in a stablecoin) to bribe smaller voters. The script must calculate a cost function: the price per vote needed to incentivize delegation or a specific vote, often inversely related to the voter's existing stake. Simulations can test if it's cheaper to bribe a few large delegates or many small holders, providing insights into the economic resilience of the governance system. Platforms like Dark Forest demonstrate the value of this wargaming approach.

Finally, you must implement the simulation logic and analyze the output. Using a framework like Foundry for Solidity-based DAOs or a Python script with web3.py, you can create a forked mainnet environment. The script executes the attack strategy—pooling votes, casting them on a simulated proposal—and outputs key metrics: the success/failure state, total attack cost, number of co-conspirators needed, and the resulting change in protocol state. This data is crucial for generating a risk assessment report that quantifies governance centralization risks.

A proposal logic exploit occurs when a malicious actor submits a governance proposal that, if passed, executes code with unintended consequences. Unlike simple treasury drains, these exploits abuse the legitimate proposal execution pathway. Common patterns include proposals that: - Upgrade a contract to a malicious implementation - Change critical protocol parameters (like fee recipients or admin keys) - Add a backdoor via a seemingly benign library update. The goal of simulation is to model this attack vector in a forked mainnet environment.

To begin, you need a forked local blockchain. Using tools like Foundry's anvil or Hardhat Network, fork the mainnet state at a specific block. This creates a sandboxed replica of the live protocol, including its current governance contracts and token holdings. You will simulate from the perspective of an attacker who has acquired enough voting power (e.g., through token borrowing or purchasing) to pass a proposal. The simulation must accurately replicate the proposal lifecycle: submission, voting period, timelock delay, and execution.

The core of the simulation is writing the malicious proposal payload. In Solidity, this is a function call to a target contract. For example, a proposal might call upgradeTo(address newImplementation) on a proxy contract, where newImplementation is an attacker-controlled contract with a hidden selfdestruct() function. You must encode this call data and submit it through the governance proposal interface, such as Governor Bravo's propose() function. The simulation should then advance blocks to simulate the voting period and quorum being met.

After the voting period, you must simulate the timelock execution phase, if the protocol uses one. This involves queueing the proposal and then executing it after the delay. The critical moment is observing the state change post-execution. Did the protocol's admin role change? Were funds transferred? Was a critical function disabled? Tools like Foundry's forge test suite or Hardhat's console.log are essential for tracking these state changes and asserting that the exploit would succeed.

Beyond a single exploit, effective wargaming involves testing defensive mechanisms. Simulate scenarios where: - A vigilant token holder attempts to veto or vote against the proposal. - The protocol's security council uses an emergency power to cancel a queued proposal. - A snapshot of the proposal triggers off-chain alerts from watchdogs. Testing these interactions reveals whether the protocol's social and technical safeguards are effective under attack conditions.

Document the simulation's parameters and outcomes meticulously. Record the attacker's required capital, the exploit's technical steps, the on-chain function calls, and the final impact. This creates a reproducible test case that can be added to the protocol's own test suite, turning a theoretical attack into a concrete regression test. This process transforms wargaming from an audit exercise into a continuous security practice for the protocol team.

Begin by systematically reviewing the simulation's event logs and transaction data. Identify the specific steps where the attack succeeded: Was it a flash loan exploit to acquire voting power? A proposal timing attack? Or a manipulation of delegation logic? Tools like Tenderly's debugger or OpenZeppelin's Defender Sentinels are invaluable for tracing the exact execution path. Categorize each finding by severity (e.g., Critical, High, Medium) and the component affected (e.g., voting contract, treasury module, timelock). This creates a prioritized roadmap for remediation.

Quantify the impact of each successful attack vector. Don't just note that an attack "worked"; calculate the potential financial loss, the percentage of governance power that could be usurped, or the duration of protocol control. For example, you might find a vulnerability allowing an attacker with 5% of tokens to pass a malicious proposal due to low quorum, putting 100% of the treasury at risk. This data is crucial for justifying resource allocation to fixes and for communicating risk to stakeholders in a governance post-mortem.

Translate your analysis into specific, testable code changes. If the simulation revealed a vulnerability in proposal submission, the fix might involve adding a minProposalDelay or a minimumProposalThreshold. For each fix, write a corresponding test in your wargaming framework to verify the attack no longer succeeds. This creates a regression test suite for your governance system. Reference established libraries like OpenZeppelin's Governor contracts for secure patterns, and always submit changes through the governance process itself for community review before deployment.

Finally, document the entire process and outcomes. Create a public post-mortem report detailing the vulnerabilities found, the fixes implemented, and the lessons learned. This transparency builds trust with your community and contributes to the broader ecosystem's security knowledge. Establish a schedule for recurring wargames, especially before major protocol upgrades or significant changes in token distribution, to ensure your governance defenses evolve alongside new threats.

To move from concept to a functional protocol, begin with a minimum viable product (MVP). Deploy a fork of a real DAO's governance contracts (e.g., Uniswap, Compound) to a testnet like Sepolia or a local Hardhat node. Implement a basic simulation engine that can replay historical proposals and track state changes. Your initial attack library should include common vectors like flash loan vote manipulation, proposal spam, and time-based attacks. Use a framework like Foundry to write and run your simulation scripts, as its speed and fuzzing capabilities are ideal for this use case.

The next phase involves integrating with live governance data. Use subgraphs from The Graph or direct RPC calls to index proposal data, delegate voting power, and token holdings from mainnet. Your protocol should be able to pull a live proposal, simulate it under various attack conditions, and generate a risk report. Consider implementing a keeper or oracle service to trigger simulations automatically when new proposals are submitted. This transforms your tool from a manual testing suite into a proactive monitoring system for DAOs.

Finally, focus on community and adoption. Open-source your simulation engine and attack modules on GitHub to establish credibility and invite peer review. Engage with DAO security teams by offering to run simulations on their upcoming proposals. The long-term goal is to integrate your protocol as a standard check in the governance workflow, similar to how smart contract audits are required before upgrades. Continued development should explore advanced simulations, like cross-chain governance attacks or manipulations involving liquid staking derivatives and restaking protocols, which present novel attack surfaces.