Ransomware Readiness Assessment Tool
Bibliographic record
Abstract
Global ransomware activity in June 2025 recorded 463 confirmed incidents, representing a 15% decline compared to May, yet demonstrating a notable escalation in attack sophistication. The Qilin group dominated the threat landscape by exploiting critical Fortinet zero-day vulnerabilities and introducing a novel “Call Lawyer” feature to intensify extortion pressure. Concurrently, Fog employed stealthier intrusion methods through the abuse of legitimate and open- source tools for data exfiltration and defense evasion. The Anubis ransomware variant incorporated a destructive file-wiping mechanism, ensuring permanent data loss even after ransom payments. Professional services, healthcare, and information technology sectors emerged as the most affected industries worldwide, with the United States remaining the primary target, followed by Canada and the United Kingdom. Newly identified actors, including Teamxxx, Warlock, and former Black Basta affiliates, expanded the ransomware ecosystem by exploiting remote management software vulnerabilities and Microsoft Teams phishing campaigns for initial access. Adversaries further leveraged trusted cloud platforms such as Google Drive and OneDrive for covert command-and-control operations. The findings indicate that modern ransomware campaigns increasingly integrate financial extortion with espionage-oriented objectives, heightening strategic cyber risk and reinforcing the necessity for enhanced patch management and layered defense mechanisms.
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".