Using Static Analysis Tools for Smart Contract Security

Detailed Instructions

Begin by defining the analysis scope. Determine if you are auditing a single contract, a complex DeFi protocol with multiple interacting contracts, or a specific vulnerability class like reentrancy or integer overflows. For a lending protocol, your scope must include the core lending logic, oracle integrations, and governance mechanisms. Establish your requirements: Do you need to integrate the tool into a CI/CD pipeline, or is this a one-time manual audit? Must it support specific compiler versions (e.g., Solidity 0.8.x) or custom EVM opcodes? This initial scoping prevents tool mismatch and ensures the selected analyzer can handle the contract's complexity and your operational needs.

• Sub-step 1: List all smart contract files and their interdependencies.
• Sub-step 2: Document the target EVM chain (e.g., Mainnet, Arbitrum) and any chain-specific considerations.
• Sub-step 3: Prioritize vulnerability classes based on the contract's function (e.g., access control for admin functions).

solidityCopy
// Scope example: A simple ERC-20 with minting and pausing.
contract MyToken is ERC20, Ownable {
    bool public paused;
    function mint(address to, uint256 amount) external onlyOwner whenNotPaused { ... }
}
// Key requirements: Check for owner privileges, pausing logic, and standard ERC-20 compliance.

Tip: For large codebases, create a dependency graph to visualize contract interactions and identify high-risk entry points for deeper analysis.

Detailed Instructions

Research and compare established static analysis tools. Slither is a powerful open-source framework for Solidity that performs data flow analysis and has a large set of built-in detectors. Mythril uses concolic analysis and taint checking to find security issues. Security-focused linters like Solhint can enforce code standards. Evaluate each tool against your requirements. Check if they detect the vulnerability types in your scope (e.g., Slither's reentrancy-eth, unchecked-transfer). Assess the false positive rate by testing on a known code sample. Consider the output format; Slither provides JSON for CI integration, while Mythril offers more detailed execution traces. For a comprehensive audit, plan to use a primary tool (like Slither) supplemented by a linter (Solhint) for style and basic issues.

• Sub-step 1: Install candidate tools (e.g., pip install slither-analyzer).
• Sub-step 2: Run each on a test contract and compare the issues flagged.
• Sub-step 3: Review the tools' documentation for custom detector support if needed.

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# Example evaluation command for Slither
slither . --exclude-informational --filter-paths node_modules --json slither-report.json

Tip: The Ethereum Security Tooling Survey and audits from reputable firms often provide benchmarks on tool effectiveness for different vulnerability classes.

Detailed Instructions

Proper configuration is critical for accurate analysis. First, ensure you have the correct Solidity compiler version. Most tools rely on the solc binary. Use solc-select or nvm to match the version specified in the contract's pragma (e.g., pragma solidity ^0.8.19;). Next, resolve all dependencies. If the project uses OpenZeppelin or other libraries, install them via npm (npm install @openzeppelin/contracts) or specify the correct remappings for the analyzer. For Slither, you may need a slither.config.json file to set remappings and exclude directories. Configure the tool's analysis depth and timeout settings; for complex contracts, increase Mythril's --max-depth from its default of 12 to 22 to explore longer execution paths.

• Sub-step 1: Check pragma solidity statements in all contracts.
• Sub-step 2: Install dependencies listed in package.json or hardhat.config.js.
• Sub-step 3: Create a configuration file for the primary tool with custom parameters.

jsonCopy
// Example slither.config.json
{
  "filter_paths": ["node_modules", "test"],
  "solc_remaps": [
    "@openzeppelin/=node_modules/@openzeppelin/",
    "@chainlink/=node_modules/@chainlink/"
  ]
}

Tip: Run slither-check-erc for contracts claiming ERC compliance to verify they match the standard's specification.

Detailed Instructions

Execute the configured tool on your target codebase. For Slither, run slither . --exclude-informational. For Mythril, use myth analyze <contract_file.sol> --solc-json remappings.json. The initial output will contain findings of varying severity. Begin triage by categorizing each finding: High (e.g., reentrancy, integer overflow), Medium (e.g., gas inefficiencies, weak PRNG), Low/Informational (e.g., coding style). Manually review each finding in the context of the code. A reported unchecked-transfer may be a false positive if the token is known to revert on failure (like USDC). Use the tool's --exclude-dependencies flag to focus on your code. Document confirmed issues and the rationale for dismissing false positives to maintain an audit trail.

• Sub-step 1: Run the primary analysis command and save output to a file.
• Sub-step 2: Map each finding to a line number in the source code for review.
• Sub-step 3: Create a spreadsheet or use a platform like DefectDojo to track issue status.

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# Example Mythril command with increased depth and timeout
myth analyze src/Vault.sol --solc-json config.json --max-depth 20 --execution-timeout 60

Tip: For complex findings, use Slither's --print paths option (e.g., --print path-graph) to visualize the data or control flow leading to the vulnerability.

Detailed Instructions

To prevent regressions, integrate static analysis into the development workflow. Implement a pre-commit hook using Husky or pre-commit to run a linter like Solhint on staged Solidity files. For continuous integration, add a job to your GitHub Actions or GitLab CI pipeline that executes the primary analysis tool on every pull request. Configure the CI job to fail if new high-severity issues are introduced. Use the tool's JSON output format to generate reports. You can also set up differential analysis; Slither can compare two versions of a contract and report only the new issues (slither . --compare master). This ensures security analysis is a consistent gatekeeper, not just a final audit step, embedding security into the Software Development Lifecycle (SDLC).

• Sub-step 1: Add a solhint check to package.json scripts and configure .solhint.json.
• Sub-step 2: Create a .github/workflows/security-analysis.yml file for GitHub Actions.
• Sub-step 3: Configure the CI step to post findings as a comment or fail the build based on severity thresholds.

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# Example GitHub Actions step for Slither
- name: Run Slither Security Analysis
  run: |
    pip install slither-analyzer
    slither . --exclude-informational --exclude-dependencies --fail-high

Tip: Use a security scanner aggregator like smart-contract-sanctuary or a custom script to run multiple tools in parallel and deduplicate findings for broader coverage.

Detailed Instructions

First, map each finding to a standard severity level: Critical, High, Medium, Low, or Informational. A Critical finding indicates a direct loss of funds or contract control, like a reentrancy vulnerability. A High finding represents a significant flaw that could lead to loss under specific conditions, such as improper access control. Next, identify the vulnerability type (e.g., reentrancy, integer overflow, logic error). This classification is crucial for understanding the attack vector. For example, a finding flagged as "SWC-107: Reentrancy" should be treated with the highest priority. Use the tool's provided classification, but always verify it aligns with industry standards like the SWC Registry or OWASP Top 10.

Tip: Never dismiss an Informational finding outright; it may reveal poor patterns that could lead to higher-severity issues later.

Detailed Instructions

Navigate to the exact line of code referenced by the tool. Determine if the vulnerable function is internal/private or external/public, as this affects the attack surface. Check the state variables involved and whether they are updated before or after external calls (the Checks-Effects-Interactions pattern). For a potential integer overflow, verify the data types and the range of possible inputs. Ask: What user role or transaction sequence is needed to exploit this? Is the function protected by a modifier like onlyOwner? For example, a finding in a function guarded by nonReentrant may be a false positive. Trace the execution path manually or using a debugger to confirm the tool's analysis.

solidityCopy
// Example: Analyzing a reentrancy warning
function withdraw() public {
    uint amount = balances[msg.sender];
    (bool success, ) = msg.sender.call{value: amount}(""); // External call before state update
    require(success, "Transfer failed");
    balances[msg.sender] = 0; // State update after external call -> TRUE POSITIVE
}

Tip: Look for surrounding require statements or conditionals that might mitigate the risk.

Detailed Instructions

Exploitability assesses how easy it is for an attacker to trigger the flaw. Consider: Are the required funds or privileges readily accessible? Is the contract on a mainnet with high-value deposits? A publicly callable function with no access control is highly exploitable. Impact measures the potential damage. Would exploitation drain all funds, corrupt critical data, or merely cause a denial-of-service? Quantify the maximum financial loss by examining the contract's ETH/token balances or the value of affected state variables. For a governance contract, impact could be loss of protocol control. Combine these factors to adjust the initial severity rating. A High-severity flaw in an unused, deprecated function may be downgraded, while a Medium flaw in a core vault function should be escalated.

Tip: Use a simple risk matrix: Risk = Likelihood (Low/Med/High) x Impact (Low/Med/High).

Detailed Instructions

Static analysis tools can produce false positives. Common causes include: missing context about inheritance, misinterpreting safe library functions (like OpenZeppelin's SafeMath), or analyzing dead code. To verify, write a simple proof-of-concept test in Foundry or Hardhat that attempts to trigger the vulnerability. If the test fails under expected conditions, the finding is likely a false positive. Also, check if the tool has misidentified a variable—for instance, a private variable mistakenly flagged as publicly readable. Compare findings across multiple tools (e.g., Slither and Mythril); consensus increases confidence. Document the reason for dismissing any finding, such as: "Function is internal and only called after a strict validation check."

solidityCopy
// Tool might flag this as an integer overflow, but it's safe due to Solidity 0.8.x
function add(uint256 a, uint256 b) public pure returns (uint256) {
    return a + b; // Safe: Built-in overflow checks in ^0.8.0
}

Tip: Always review the tool's documentation to understand the limitations of its analysis engines.

Detailed Instructions

Create a prioritized list for remediation. Critical and High severity, validated findings must be addressed immediately before any deployment or as an urgent patch. For each finding, specify the remediation action. For a reentrancy bug, the action is to apply the Checks-Effects-Interactions pattern or use a reentrancy guard. For an access control issue, add a modifier like onlyRole. Assign each fix an owner and a timeline. Also, consider the fix complexity; a simple configuration change is prioritized over a complex architectural rewrite. Update the priority if multiple findings are related—fixing a core logic flaw might resolve several downstream issues. Finally, ensure the plan includes re-running the static analysis after fixes to confirm resolution.

Tip: Use a tracking system (e.g., GitHub Issues) with labels for severity, status, and assigned developer.