Program Analysis for High-Value Smart Contract Vulnerabilities: Techniques and Insights

TL;DR

This paper presents advanced static analysis techniques combined with domain knowledge inference to effectively identify high-value vulnerabilities in smart contracts, achieving practical success in real-world security and bug bounty rewards.

Contribution

It introduces a high-completeness static analysis approach and novel methods for inferring domain knowledge from large contract corpora, enabling effective detection of high-value vulnerabilities.

Findings

Achieved over $3M in bug bounty rewards from high-profile contracts.

Developed techniques for automatic domain knowledge inference from statistical analysis.

Demonstrated that high-value vulnerability detection can tolerate high false-positive rates.

Abstract

A widespread belief in the blockchain security community is that automated techniques are only good for detecting shallow bugs, typically of small value. In this paper, we present the techniques and insights that have led us to repeatable success in automatically discovering high-value smart contract vulnerabilities. Our vulnerability disclosures have yielded 10 bug bounties, for a total of over $3M, over high-profile deployed code, as well as hundreds of bugs detected in pre-deployment or under-audit code. We argue that the elements of this surprising success are a) a very high-completeness static analysis approach that manages to maintain acceptable precision; b) domain knowledge, provided by experts or captured via statistical inference. We present novel techniques for automatically inferring domain knowledge from statistical analysis of a large corpus of deployed contracts, as well as discuss insights on the ideal precision and warning rate of a promising vulnerability detector. In contrast to academic literature in program analysis, which routinely expects false-positive rates below 50% for publishable results, we posit that a useful analysis for high-value real-world vulnerabilities will likely flag very few programs (under 1%) and will do so with a high false-positive rate (e.g., 95%, meaning that only one-of-twenty human inspections will yield an exploitable vulnerability).