How effective are smart contract analysis tools? evaluating smart contract static analysis tools using bug injection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Security attacks targeting smart contracts have been on the rise, which have led to financial loss and erosion of trust. Therefore, it is important to enable developers to discover security vulnerabilities in smart contracts before deployment. A number of static analysis tools have been developed for finding security bugs in smart contracts. However, despite the numerous bug-finding tools, there is no systematic approach to evaluate the proposed tools and gauge their effectiveness. This paper proposes SolidiFI, an automated and systematic approach for evaluating smart contract static analysis tools. SolidiFI is based on injecting bugs (i.e., code defects) into all potential locations in a smart contract to introduce targeted security vulnerabilities. SolidiFI then checks the generated buggy contract using the static analysis tools, and identifies the bugs that the tools are unable to detect (false-negatives) along with identifying the bugs reported as false-positives. SolidiFI is used to evaluate six widely-used static analysis tools, namely, Oyente, Securify, Mythril, SmartCheck, Manticore and Slither, using a set of 50 contracts injected by 9369 distinct bugs. It finds several instances of bugs that are not detected by the evaluated tools despite their claims of being able to detect such bugs, and all the tools report many false positives
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it