Make notifications great again: learning how to notify in the age of large-scale vulnerability scanning
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
As large-scale vulnerability detection becomes more feasible, it also increases the urgency to find effective largescale notification mechanisms to inform the affected parties. Researchers, CERTs, security companies and other organizations with vulnerability data have a variety of options to identify, contact and communicate with the actors responsible for the affected system or service. A lot of things can – and do – go wrong. It might be impossible to identify the appropriate recipient of the notification, the message might not be trusted by the recipient, it might be overlooked or ignored or misunderstood. Such problems multiply as the volume of notifications increases. In this paper, we undertake several large-scale notification campaigns for a vulnerable configuration of authoritative nameservers. We investigate three issues: What is the most effective way to reach the affected parties? What communication path mobilizes the strongest incentive for remediation? And finally, what is the impact of providing recipients a mechanism to actively demonstrate the vulnerability for their own system, rather than sending them the standard static notification message. We find that retrieving contact information at scale is highly problematic, though there are different degrees of failure for different mechanisms. For those parties who are reached, notification significantly increases remediation rates. Reaching out to nameserver operators directly had better results than going via their customers, the domain owners. While the latter, in principle, have a stronger incentive to care and their request for remediation would trigger the commercial incentive of the operator to keep its customers happy, this communication path turned out to have slightly worse remediation rates. Finally, we find no evidence that vulnerability demonstrations did better than static messages. In fact, few recipients engaged with the demonstration website.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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