Privatized espionage: <scp>NSO</scp> Group Technologies and its Pegasus spyware
Bibliographic record
Abstract
Abstract Advanced cyber technology like NSO Group Technologies' (NSO) controversial Pegasus spyware blurs distinctions between “good” and “bad.” This case follows the Israeli‐based international leader in cyber espionage and developer NSO and one of its co‐founders, Shalev Hulio from its creation in 2010 to the present. It includes NSO's acquisition by US‐based private equity fund Francisco Partners in 2014. NSO's re‐acquisition in 2019 by co‐founders Hulio and Omri Lavie with funding support from London‐based private equity fund Novalpina Capital. During this time, Pegasus had helped capture Mexican drug baron El Chapo, prevented terrorist attacks and broken up pedophilia, sex, and drug‐trafficking rings. But Pegasus also contributed to the murder of Washington Post reporter Jamal Khashoggi as well as other illegal incidents against dissidents, journalist, and governments. As the case suggests, controlling access to such powerful technology that involves accountability, responsibility, and enforceability within a firm and within nations appears illusive.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".