Security Patch Management: Share the Burden or Share the Damage?
Bibliographic record
Abstract
Patch management is a crucial component of information security management. An important problem within this context from a vendor's perspective is to determine how to release patches to fix vulnerabilities in its software. From a firm's perspective, the issue is how to update vulnerable systems with available patches. In this paper, we develop a game-theoretic model to study the strategic interaction between a vendor and a firm in balancing the costs and benefits of patch management. Our objective is to examine the consequences of time-driven release and update policies. We first study a centralized system in a benchmark scenario to find the socially optimal time-driven patch management. We show that the social loss is minimized when patch-release and update cycles are synchronized. Next, we consider a decentralized system in which the vendor determines its patch-release policy and the firm selects its patch-update policy in a Stackelberg framework, assuming that release and update policies are either time driven or event driven. We develop a sufficient condition that guarantees that a time-driven release by the vendor and a time-driven update by the firm is the equilibrium outcome for patch management. However, in this equilibrium, the patch-update cycle of the firm may not be synchronized with the patch-release cycle of the vendor, making it impossible to achieve the socially optimal patch management in the decentralized system. Therefore, we next examine cost sharing and liability as possible coordination mechanisms. Our analysis shows that cost sharing itself may achieve synchronization and social optimality. However, liability by itself cannot achieve social optimality unless patch-release and update cycles are already synchronized without introducing any liability. Our results also demonstrate that cost sharing and liability neither complement nor substitute each other. Finally, we show that an incentive-compatible contract on cost sharing can be designed to achieve coordination in case of information asymmetry.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".