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
Fuzz testing is an automated testing technique where random data is used as an input to software systems in order to reveal security bugs/vulnerabilities. Fuzzed inputs must be binaries embedded with compiled bytecodes when testing against ActionScript virtual machines (AVMs). The current fuzzing method for JavaScript-like virtual machines is very limited when applied to compiler-involved AVMs. The complete source code should be both grammatically and semantically valid to allow execution by first passing through the compiler. In this paper, we present ScriptGene, an algorithmic approach to overcome the additional complexity of generating valid ActionScript programs. First, nearly-valid code snippets are randomly generated, with some controls on instruction flow. Second, we present a novel mutation method where the former code snippets are lexically analyzed and mutated with runtime information of the AVM, which helps us to build context for undefined behaviours against compiler-check and produce a high code coverage. Accordingly, we have implemented and evaluated ScriptGene on three different versions of Adobe AVMs. Results demonstrate that ScriptGene not only covers almost all the blocks of the official test suite (Tamarin), but also is capable of nearly twice the code coverage. The discovery of six bugs missed by the official test suite demonstrates the effectiveness, validity and novelty of ScriptGene.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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