Reentrancy Redux: The Evolution of Real-World Reentrancy Attacks on Blockchains
Bibliographic record
Abstract
Reentrancy attacks remain a persistent threat to blockchain smart contracts today, causing significant financial losses despite numerous defense mechanisms. This paper presents a comprehensive analysis of 73 real-world reentrancy attacks on EVM-compatible blockchains from 2016 to 2024, investigating the factors contributing to their continued prevalence. Through integrated qualitative and quantitative analyses, we identify key trends in exploited vulnerabilities, track the evolution of attacker techniques, and expose a widening gap between academic research and real-world practice. Our findings reveal that reentrancy attacks are more diverse and sophisticated than previously understood, frequently involving complex interactions across multiple contracts, projects, and even blockchains. Critically, we highlight how attackers are adapting to bypass traditional detection and defense techniques. This research provides crucial insights into the evolving threat landscape, challenges outdated assumptions, and offers practical guidelines for developing more robust and effective reentrancy defenses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".