Evaluation of Dynamic Analysis Tools for Software Security
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.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".