Evaluation of Dynamic Analysis Tools for Software Security
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
This article discusses the development of secure software by means of dynamic analysis tools. A secure software-based system should have security checks and balances integrated throughout its entire development lifecycle, including its deployment phase. Therefore, this article covers both using software security tools for testing code in development as well as monitoring code in deployment to ensure that it is operating securely. The security issues discussed in this article will be split into two categories – memory safety issues and input validation issues. Memory safety issues concern problems of unauthorized memory access such as buffer overflows, stack overflows, use-after-free, double-free, memory leaks, etc. Although not strictly a memory safety issue, concurrency issues, such as data races, will be considered as memory safety issues in this article. Input validation issues concern problems where untrusted input is directly passed to handlers which are designed to handle both data and commands. Examples of this include path traversal, SQL injection, command injection, JavaScript/HTML injection, etc. As a result of this significant difference between these two types of security vulnerabilities, two sets of tools are evaluated with one set focusing on memory safety issues and the other on input validation issues. This article explores the benefits and limitations of current software dynamic analysis tools by evaluating them against both the authors test cases as well as the OWASP Benchmark for Security Automation and proposes solutions for implementing secure software applications.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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