Where Do Smart Contract Security Analyzers Fall Short?

TL;DR

This study evaluates six popular smart contract security analyzers on real-world contracts, revealing significant accuracy and usability issues that hinder adoption despite their potential to detect vulnerabilities.

Contribution

It provides a comprehensive benchmarking of analyzers on real contracts and links their performance gaps to developer perceptions, offering targeted improvement recommendations.

Findings

Accuracy varies widely among analyzers (F1 scores 31.2% to 94.6%)

High false-positive rates up to 32.6% impact trust

Long runtimes (>700 seconds) hinder practical use

Abstract

Smart contracts underpin high-value ecosystems such as decentralized finance (DeFi), yet recurring vulnerabilities continue to cause losses worth billions of dollars. Although numerous security analyzers that detect such flaws exist, real-world attacks remain frequent, raising the question of whether these tools are truly effective or simply under-used due to low developer trust. Prior benchmarks have evaluated analyzers on synthetic or vulnerable-only contract datasets, limiting their ability to measure false positives, false negatives, and usability factors that drive adoption. To close this gap, we present a mixed-methods study that combines large-scale benchmarking with practitioner insights. We evaluate six widely used analyzers (i.e., Confuzzius, Dlva, Mythril, Osiris, Oyente, and Slither) on 653 real-world smart contracts that cover three high-impact vulnerability classes from the OWASP Smart Contract Top Ten (i.e., reentrancy, suicidal contract termination, and integer arithmetic errors). Our results show substantial variation in accuracy (F1 = 31.2 to 94.6%), high false-positive rates (up to 32.6%), and runtimes exceeding 700 seconds per contract. We then survey 150 professional developers and auditors to understand how they use and perceive these tools. Our findings reveal that excessive false positives, vague explanations, and long analysis times are the main barriers to trust and adoption in practice. By linking measurable performance gaps to developer perceptions, we provide concrete recommendations for improving the precision, explainability, and usability of smart-contract security analyzers.