Short Paper: SlotCollider - Detecting Storage Slot Collisions in Ethereum Smart Contracts
Bibliographic record
Abstract
Smart contract contains both the code and its internal storage data on the chain. This storage data needs to be properly aligned and laid out in the storage trie. Otherwise, it could lead to storage collisions resulting in unexpected behavior or storage-based security exploits such as the Audius hack [1]. In this paper, with our SlotCollider tool, we explore slot collisions that occur when two or more variables share the same storage slots in a proxy contract. Many existing tools rely solely on bytecode analysis for collision detection, but this approach suffers from both false positives and negatives, and is not enough to fully understand the storage layout and complex data types. SlotCollider addresses these issues with the source code based analysis of smart contracts and also incorporates on-chain data for further precision. We evaluated SlotCollider on a set of 4,890 smart contracts that detected an additional 6,558 storage collisions that were missed by other tools [2]. The SlotCollider outperforms existing tools in detection and provides more accurate and reliable results to detect storage collisions in Ethereum smart contracts.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".