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
Suppose information security starts to embrace the reality that failure will happen. In that case, we can move from trying to build the perfectly secure system to continue asking questions like “how much vulnerability do I have and what control do I need to be effective?” This paper proposes Security Chaos Engineering as a method to expose API vulnerabilities and enhance API security. RESTful API has gained popularity in recent years due to its reusability, flexibility and natural adaptation to modern web application, mobile application, and cloud computing. However, ensuring secure API/data access and hence mitigating reputational and/or financial damage to the organization is still in its early stage. Foundational security protection mechanisms include transport layer security, authentication / authorization of the consumer (either individual or application). To complete the spectrum of secure API access and provide advanced protection, there is much more to consider: mitigation of API specific vulnerabilities at design and implementation time. API Security using Chaos Engineering is an approach for learning about system security behavers when using APIs by applying empirical exploration. Security Chaos Engineering is the discipline of experimenting to build confidence in the system’s security and see how a system can withstand threats in production. Security Chaos Engineering isn’t about creating chaos. It is about making the security chaos inherent in the system visible.
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 | 0.000 | 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