Directed or Undirected: Investigating Fuzzing Strategies in a CI/CD Setup (Registered Report)
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
Fuzzing best practices suggest that fuzzing should be run for at least 24 hours, if not longer. This recommendation makes it hard to integrate fuzzing into CI/CD contexts, to rapidly check a commit for bugs. Existing studies on CI/CD fuzzing simulated a CI/CD environment by running undirected fuzzers on Magma benchmark programs, which have multiple bugs injected into a single version of the program. Directed fuzzers, such as AFLGo, aim to generate inputs that reach specific target locations in the program being fuzzed. Thus, they should be more effective at fuzzing in a CI/CD environment. In this study, we propose to evaluate both directed and undirected fuzzers in a simulated CI/CD environment. Like prior work, we will use Magma as a source of benchmarks, and run fuzzers for 10 minutes. Unlike prior work, we will start the fuzzing process from a saturated corpus, rather than Magma's default corpus. Also unlike prior work, we will run the fuzzers on versions of Magma programs with a single bug injected. To deal with the threat that Magma patches give directed fuzzers access to too precise information as to the bug location, we will also conduct experiments where we add additional lines of target code, to evaluate the sensitivity of directed fuzzers. Our registered report gives preliminary results on a small subset of benchmarks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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