Slither - Catches the Bugs That Drain Protocols

Slither is a static analysis tool built specifically for smart contracts by the Trail of Bits team who got tired of seeing the same vulnerabilities over and over in production code. Released in October 2018, it's become the go-to tool for catching bugs before they drain wallets.

Core Purpose and Functionality

Smart contract security isn't about following best practices - it's about not getting rekt in prod. Most analyzers were built for regular software and shit the bed when they hit proxy patterns or diamond inheritance. Slither was built from day one for the weird stuff that happens in Solidity.

Takes about a second to run, maybe less. Parses pretty much all Solidity code correctly, including the weird edge cases that make other analyzers shit the bed. I've thrown contracts with 50+ inheritance levels at it and it just works.

Under the hood, Slither converts your Solidity into SlithIR, its intermediate representation. This isn't academic wankery - it's what lets Slither track data flow through complex proxy patterns. The DAO hack pattern still shows up in 2025, and Slither catches it every time.

Key Capabilities

Slither has 99+ detectors that catch everything from "your contract just got drained" level bugs to gas optimizations. They're sorted by severity (High, Medium, Low, etc.) so you know what to fix first and what can wait.

Slither also has visualization tools that generate inheritance graphs and call flow diagrams. Useful for understanding WTF your contract is actually doing, especially when you inherit from 15 OpenZeppelin contracts.

Performance and Accuracy

Academic tools can't handle this complexity; Slither makes it look simple. The research paper shows it parses 99.9% of Solidity correctly, but here's what matters: it catches real bugs that drain millions.

Trail of Bits uses this in their audits and maintains a trophy list of vulnerabilities Slither caught in production. It would've caught the signature verification bypass that cost Wormhole $325M if they'd bothered to run static analysis on their upgrade code. Same with the re-entrancy bug that drained $180M from bZx - Slither flags that pattern immediately.

Industry Adoption

Big DeFi protocols use Slither to avoid getting rekt. It's integrated into MythX and Consensys Diligence security platforms. OpenZeppelin runs it during their audits.

Development teams put Slither in their CI pipelines to catch bugs before mainnet. Hardhat and Foundry users set it up to fail builds when it finds critical issues.

Some IEEE research tested Slither against other tools on 47,000 contracts. Academic validation aside, it just catches more real bugs faster than the alternatives, and doesn't break when you throw complex proxy patterns at it.

Installation

Just use pip - it's the easiest way to get Slither running:

pip install slither-analyzer

Gotcha: If you're on Python 3.12+, you might hit dependency conflicts. Use pip install slither-analyzer --break-system-packages or better yet, use a virtual environment.

If you want bleeding edge features, grab it from GitHub:

git clone https://github.com/crytic/slither.git && cd slither
python3 -m pip install .

Version warning: Slither 0.11.3 (April 2025) completely shits the bed with Solidity 0.8.28+ due to AST changes. Found this out the hard way when it kept throwing parsing errors on fresh contracts. Stick with Solidity 0.8.27 until Trail of Bits gets their shit together.

Or use the Docker container if you don't want to deal with Python dependency hell:

docker pull trailofbits/eth-security-toolbox
docker run -it -v /path/to/contracts:/share trailofbits/eth-security-toolbox

Basic Usage

The CLI is simple. Point it at a contract:

slither /path/to/contract.sol

If you're using Hardhat or Foundry, just run it from your project root:

slither .  # Analyzes everything

You can plug it into CI with GitHub Actions to automatically check PRs.

Advanced Features

SlithIR

The secret sauce is SlithIR - Slither's intermediate representation that turns messy Solidity into clean instructions. This lets it track data flow through complex contract hierarchies and catch bugs that other tools miss.

Custom Detectors

You can write your own detectors using the Python API. It gives you access to the contract structure and call graphs, so you can find project-specific bugs the built-in detectors don't catch.

Visualization Tools

The printers generate useful diagrams:

• inheritance-graph: Shows which contracts inherit from what
• call-graph: Maps out function call patterns
• cfg: Control flow graphs for complex functions
• vars-and-auth: Which functions can access which variables

Integrations

Works with GitHub Actions for automated scans and pre-commit hooks to block bad code.

Pairs well with other Trail of Bits tools like Echidna for fuzzing and Manticore for symbolic execution.

Performance

Slither is fast because it reuses your existing build artifacts through crytic-compile instead of recompiling everything. For big projects, you can run only specific detectors to speed things up.

Production Usage

Big protocols like Aave run Slither in their deployment pipelines. Takes maybe 30 seconds to analyze their entire V3 codebase - 200+ contracts with crazy inheritance hierarchies.

Chainlink blocks PRs that don't pass Slither. Learned this shit the hard way when my PR sat there for 3 days because I ignored what I thought was a bullshit medium-severity warning about uninitialized storage variables. Turned out it was a real bug.

ConsenSys Diligence processes 10,000+ contracts monthly with Slither. I've seen them catch reentrancy bugs in "audited" protocols that cost hundreds of millions when they finally got exploited. The Python API is what lets MythX and other platforms integrate it seamlessly.

Advanced Detection

Newer versions have taint analysis that tracks data flow through inheritance chains to catch subtle bugs like cross-function reentrancy. The control flow analysis finds dead code and infinite loops.

Works great with Echidna fuzzing. Use Slither results to guide where Echidna should focus its testing - catches edge cases neither tool finds alone.