Death by a thousand bytes? Assessing the strategic effects of wiper attacks
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
Wipers, pieces of malware specifically designed to destroy data on a computer system, have become an increasingly significant tool of cyberwarfare. Yet, much uncertainty remains with regard to their strategic effectiveness. What exact effects do wiper attacks produce in the ‘real world’? Offering a tentative model for impact assessment, we conduct an in-depth analysis of six well-documented cases of wiper attacks. We demonstrate that wipers do inflict serious damage to information systems and can generate significant operational disruptions within targeted entities, but generally fail to produce systemic shocks or enduring outcomes. The study then highlights several factors that help explain why organisations prove surprisingly resilient when targeted by a wiper, thus reducing such attacks’ strategic magnitude. We conclude by emphasising alternative ways in which wipers may be used by nation states in the future, potentially to greater effect.
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 | 0.001 | 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