Design and Implementation of SFCI: A Tool for Security Focused Continuous Integration
Bibliographic record
Abstract
Software security is a component of software development that should be integrated throughout its entire development lifecycle, and not simply as an afterthought. If security vulnerabilities are caught early in development, they can be fixed before the software is released in production environments. Furthermore, finding a software vulnerability early in development will warn the programmer and lessen the likelihood of this type of programming error being repeated in other parts of the software project. Using Continuous Integration (CI) for checking for security vulnerabilities every time new code is committed to a repository can alert developers of security flaws almost immediately after they are introduced. Finally, continuous integration tests for security give software developers the option of making the test results public so that users or potential users are given assurance that the software is well tested for security flaws. While there already exists general-purpose continuous integration tools such as Jenkins-CI and GitLab-CI, our tool is primarily focused on integrating third party security testing programs and generating reports on classes of vulnerabilities found in a software project. Our tool performs all tests in a snapshot (stateless) virtual machine to be able to have reproducible tests in an environment similar to the deployment environment. This paper introduces the design and implementation of a tool for security-focused continuous integration. The test cases used demonstrate the ability of the tool to effectively uncover security vulnerabilities even in open source software products such as ImageMagick and a smart grid application, Emoncms.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".