Finding and Understanding Miscompilation Bugs in the Solidity Compiler

2026-07-09 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Blockchain & Distributed Ledger Technology, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

SolSmith, a semantics-aware differential fuzz testing tool, has identified 25 previously unnoticed miscompilation bugs in the Solidity compiler over three years, some persisting for multiple years. This tool rigorously tests code generation and optimization components by generating semantically valid Solidity programs and comparing their execution traces across different compiler configurations (e.g., optimized vs. unoptimized, legacy vs. IR-based). The analysis revealed that over two-thirds (17 out of 25) of these miscompilation bugs were in the optimizer, with 14 specifically in the Yul optimizer. The most common root cause was insufficient safety checks in optimization rules. SolSmith also found 164 internal compiler errors, with 76 caught before release. The median miscompilation bug persisted for 134 days, and the tool's operation cost under \$15,000 over three years, averaging less than \$1,000 per bug.

Key takeaway

For AI Security Engineers or Smart Contract Developers evaluating compiler integrity, you should integrate semantics-aware differential fuzz testing into your CI/CD pipeline. This approach proactively identifies miscompilation bugs and internal errors, significantly reducing the risk of deploying vulnerable or incorrect smart contract code. By catching defects early, you can avoid costly post-deployment fixes and enhance the overall security posture of your blockchain applications.

Key insights

Semantics-aware differential fuzz testing effectively uncovers critical miscompilation bugs and internal errors in smart contract compilers.

Principles

• Compiler optimization rules require rigorous, side-effect-aware validation.
• Proactive fuzz testing catches bugs before production deployment.
• Semantic expressiveness in test inputs correlates with bug-finding potential.

Method

SolSmith generates semantically valid Solidity programs, compiles them in differing configurations (control vs. experiment), and compares runtime execution traces to detect divergences.

In practice

• Implement differential fuzz testing with semantics-aware program generation.
• Focus testing on compiler optimization and translation components.
• Exclude environment-dependent instruction results from trace comparisons.

Topics

• Solidity Compiler
• Differential Fuzz Testing
• Smart Contract Security
• Ethereum Blockchain
• Miscompilation Bugs
• Compiler Optimization

Code references

• ethereum/solidity

Best for: Research Scientist, Software Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.