# Slither: A Static Analysis Framework For Smart Contracts

arXiv: https://arxiv.org/abs/1908.09878 · HTML: https://mlsanity.com/paper/1908.09878/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 30 of 89)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023)
- [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation](https://mlsanity.com/paper/2405.02580/) (2024)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion](https://mlsanity.com/paper/2106.09282/) (2021)
- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding](https://mlsanity.com/paper/2309.09826/) (2023)
- [From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security](https://mlsanity.com/paper/2412.15004/) (2024)
- [AI Agent Smart Contract Exploit Generation](https://mlsanity.com/paper/2507.05558/) (2025)
- [Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation](https://mlsanity.com/paper/2411.06221/) (2024)
- [SCALM: Detecting Bad Practices in Smart Contracts Through LLMs](https://mlsanity.com/paper/2502.04347/) (2025)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2506.18245/) (2025)
- [Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities](https://mlsanity.com/paper/2501.07058/) (2025)
- [Guiding LLM-based Smart Contract Generation with Finite State Machine](https://mlsanity.com/paper/2505.08542/) (2025)
- [GasAgent: A Multi-Agent Framework for Automated Gas Optimization in Smart Contracts](https://mlsanity.com/paper/2507.15761/) (2025)
- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities](https://mlsanity.com/paper/2208.13252/) (2022)
- [VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model](https://mlsanity.com/paper/2309.08474/) (2023)
- [Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2407.14838/) (2024)
- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [Do not rug on me: Zero-dimensional Scam Detection](https://mlsanity.com/paper/2201.07220/) (2022)
- [The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey](https://mlsanity.com/paper/2409.02139/) (2024)
- [Detecting Buggy Contracts via Smart Testing](https://mlsanity.com/paper/2409.04597/) (2024)
- [SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework](https://mlsanity.com/paper/2411.19234/) (2024)
- [AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation](https://mlsanity.com/paper/2502.06348/) (2025)
- [SmartLLM: Smart Contract Auditing using Custom Generative AI](https://mlsanity.com/paper/2502.13167/) (2025)
- [Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts](https://mlsanity.com/paper/2304.10726/) (2023)

## Abstract (arXiv)

This paper describes Slither, a static analysis framework designed to provide rich information about Ethereum smart contracts. It works by converting Solidity smart contracts into an intermediate representation called SlithIR. SlithIR uses Static Single Assignment (SSA) form and a reduced instruction set to ease implementation of analyses while preserving semantic information that would be lost in transforming Solidity to bytecode. Slither allows for the application of commonly used program analysis techniques like dataflow and taint tracking. Our framework has four main use cases: (1) automated detection of vulnerabilities, (2) automated detection of code optimization opportunities, (3) improvement of the user's understanding of the contracts, and (4) assistance with code review. In this paper, we present an overview of Slither, detail the design of its intermediate representation, and evaluate its capabilities on real-world contracts. We show that Slither's bug detection is fast, accurate, and outperforms other static analysis tools at finding issues in Ethereum smart contracts in terms of speed, robustness, and balance of detection and false positives. We compared tools using a large dataset of smart contracts and manually reviewed results for 1000 of the most used contracts.