CVE Disclosure Process: Automation & Coordination

• CVE Disclosure Process is a structured pipeline that standardizes vulnerability reports using both manual methods and machine learning for data normalization.
• It integrates diverse stakeholder coordination and automated techniques to reduce latency and improve the completeness of vulnerability records.
• Emerging decentralized architectures and ML-driven mapping are transforming CVE disclosure by enhancing transparency and prompt risk management.

The Common Vulnerabilities and Exposures (CVE) disclosure process is the structured pipeline by which information about publicly known security vulnerabilities is reported, standardized, disseminated, and linked to mitigation actions and research. CVEs serve as the de facto global vocabulary for security flaws, enabling the coordination of mitigation, automation of risk analysis, and the development of data-driven security tools. The process involves data normalization, coordination among multiple stakeholders, automation via advanced machine learning, challenges relating to data completeness and timeliness, and emerging decentralized models. This article presents a technical survey of the CVE disclosure process with detailed reference to methodologies, state-of-the-art automation, actor roles, empirical metrics, and future directions.

1. Data Normalization, Characterization, and Standardization

The foundational phase of CVE disclosure involves transforming initial vulnerability descriptions—often submitted in free-form text—into standardized, structured reports. Central to this phase are manual and automated methods for extracting and normalizing critical attributes such as product, version, root cause, attack vector, and impact.

Key standardization artifacts include the NIST Vulnerability Description Ontology (VDO), which provides taxonomies for root causes and exploit properties, and metadata fields such as CVSS (severity), CWE (weakness), and CPE (platform enumeration).

Automation of Characterization

Manual characterization is time-intensive and error-prone, often leading to incomplete or inconsistent records (Gonzalez et al., 2019). Recent research demonstrates that machine learning classifiers, specifically Support Vector Machines (SVMs), can be trained to map free-text descriptions to structured VDO attributes. Text preprocessing employs normalization (tokenization, stemming, stop-word removal, and elimination of URLs/punctuation), followed by vectorization using Term Frequency-Inverse Document Frequency (TF-IDF):

$$TF(w, d) = \frac{\text{count of } w \text{ in } d}{\text{total words in } d}$$

$$IDF(w) = \log \frac{\text{Total documents}}{\text{Documents containing } w}$$

$TFIDF(w, d) = TF(w, d) \times IDF(w)$

Among evaluated models, SVM achieves high accuracy (approx. 72.88%) and, though ensemble voting ensembles marginally outperform (74.52%), SVM offers optimal resource–accuracy trade-off and generalizes well with stratified 10-fold cross-validation (Gonzalez et al., 2019).

For CPE annotation—linking vulnerabilities to product/vendor/version—a deep learning-based Named Entity Recognition (NER) pipeline leverages BERT, XLNet, and GPT-2, along with automated annotation and data augmentation, achieving F1 scores up to 95.48% (Hu et al., 22 May 2024). The automated approach outperforms prior heuristics by over 9% across all metrics and dramatically reduces the latency (from 35 days to hours) for NVD to acquire CPE data, enabling near real-time aggregation of asset risk.

2. Stakeholder Coordination and Social-Technical Dynamics

The CVE disclosure lifecycle involves multiple actors, notably vulnerability reporters, vendors, CNAs (CVE Numbering Authorities), coordinating bodies (e.g., MITRE, CERT/CC), and downstream system maintainers. Vulnerability requests traverse a complex, socio-technical workflow, which includes assignment, review, publication, and downstream notification.

Empirical studies of open source projects reveal that the mean time between initial CVE request on public mailing lists and entry into the National Vulnerability Database (NVD) is 77 days (median 15), with heavy-tailed delays (90th percentile at 226 days) (Ruohonen et al., 2020). Regression and network analyses show that coordination delays scale with:

• Number of communicating participants per CVE (SOCDEG metric), with higher “participant degree” linked to longer delays.
• Entropy and length of communication (MSGSLEN, MSGSENT); more verbose, less focused, or entropic email threads increase delays.
• Temporal effects: weekends and certain months are linked to increased latency.
• “Prerequisite constraints”: inclusion of multiple independent bug tracker or repository links (BUGS, NVDREFS metrics) reduces delays.

The coordination model can be formally represented as a bipartite graph where nodes are either participants or CVEs and edges represent communication events; the degree of each CVE node directly models coordination complexity (Ruohonen et al., 2020).

3. Automation, Ranking, and Semantic Mapping

Manual processes for CVE-to-CWE mapping and CVSS scoring present severe scalability bottlenecks. Automating this characterization is critical for maintaining the timeliness and quality of disclosures.

Classification and Ranking

Deep learning models, such as Sentence-BERT and rankT5, are fine-tuned on expertly annotated CVE–CWE pairs to produce ranked lists of the most appropriate CWE weaknesses for a given vulnerability (Haddad et al., 2023). Sentence-BERT computes cosine similarity over paired sentence embeddings:

$$\text{cosine}(s_i, s_j) = \frac{s_i \cdot s_j}{\|s_i\| \cdot \|s_j\|}$$

Fine-tuned models achieve mean reciprocal ranks (MRR) above 0.91 versus 0.15 for classic IR methods (BM25). Such automation minimizes latency and inconsistency, supports human-in-the-loop reinforcement learning for iterative improvement, and allows for continuous adaptation to new vulnerability categories.

Multimodal and RAG-Augmented Patching

Emerging frameworks such as AutoPatch combine multi-agent orchestration with Retrieval-Augmented Generation (RAG) architectures: given LLM-generated code, semantic and taint analysis are used to match to the closest known CVE via a unified similarity score, composed as a weighted sum of fuzzy Jaccard and cosine similarities across keywords, descriptions, variables, and function abstractions (Seo et al., 7 May 2025). Bootstrapping pairwise ranking loss optimizes weights for maximal discriminatory power. Empirically, AutoPatch achieves 90.4% accuracy in CVE matching and 95.0% patching accuracy, while enabling multi-agent, cost-efficient verification and remediation workflows.

4. Timeliness, Data Quality, and Incompleteness

One major challenge highlighted by multiple studies is data incompleteness and update latency at initial disclosure. Statistical analyses across >40,000 reports indicate that 28% of records are published without a CVSS base score and 52% lack a CPE list on first appearance; only 2% lack mitigation detail (Khanmohammadi et al., 2023). The typical average delay until completion (inclusion of missing data) is about 11.6 days for CVSS and 11–12 days for CPE.

This higher-level insight is substantiated by survival analyses: median time to fix vulnerabilities across projects is approximately 34 days, but 20% remain live for over 150 days (Przymus et al., 4 Apr 2025). Factors influencing this “CVE lifetime” include:

• Memory model: managed languages halve median fix time compared to unmanaged.
• Type checking and type safety: dynamically typed and strongly typed languages typically see shorter fix times.
• Access Vector: network-exploitable vulnerabilities resolve faster than those requiring local access.
• Project factors: higher author count increases latency, but elevated activity (commit frequency) reduces it.

Vulnerability management workflows benefit from reliable and timely population of key metadata fields, and organizations have deployed ticketing processes employing canonical product “well-formed names” and NLP-based extraction to compensate for incomplete initial data (Khanmohammadi et al., 2023).

5. Open Source Disclosure, Bottlenecks, and Global Coordination

In open-source ecosystems, primary reporting channels include bug bounty reports and security advisories (e.g., the GitHub Advisory Database). Vulnerabilities propagate through review, CVE assignment (usually via a CNA), and—after a mandatory record is submitted—publication to the NVD (Ayala et al., 29 Jan 2025). A significant bottleneck is observed at the CNA publication step (“Step 6 in the CVE process”), where voluntary participation and backlogs can cause prolonged latency or omission of CVEs. Downstream effects include:

• Automated ecosystem tools (e.g., Dependabot) missing vulnerability notifications due to lack of up-to-date CVE entries.
• Under-notification of dependent projects and protracted exposure.
• Potentially slower overall resolution cycles for vulnerabilities that do ultimately receive a CVE, due to formal process overhead.

Recommendations from empirical studies include default private vulnerability reporting features, automation of CNA step verification, and tooling to identify and flag unpropagated CVE records (Ayala et al., 29 Jan 2025). Explicit process diagrams illustrate chains from initial bug bounty submission→review/triage→CVE request→NVD entry, making clear the points at which delays accumulate.

6. Decentralization, Transparency, and Emerging Architectures

Recent proposals address fundamental limitations in centralized CVE management by introducing permissioned blockchain architectures for decentralized CVE disclosure (Amirov et al., 1 May 2025). In this architecture:

• Only authenticated CNAs possess write permissions, as enforced by X.509 certificate-based identity and endorsement policy on Hyperledger Fabric.
• Submissions and updates are immutably recorded, allowing all stakeholders full auditability and transparency.
• Smart contracts (chaincode) codify CVE process logic, including embargoed disclosures (draft→published status based on timestamp), CNA onboarding/revocation, and accountability.
• Decentralized governance can be extended to DAO structures for dynamic, transparent evolution of the root-of-trust.
• Performance benchmarks show throughput of 200 transactions/second and latency under 2 seconds.

This model alleviates single points of failure inherent in centralized schemes and accommodates broader resilience and auditability requirements.

7. Challenges, Recommendations, and Future Directions

Persisting barriers in CVE disclosure include inconsistent or incomplete reporting, delays in propagation, non-standardized technical communication, and human resource limitations for manual labeling or triage. Recommended interventions include:

• Adoption of state-of-the-art machine learning (SVM, BERT, rankT5) for characterization, mapping, and prioritization tasks to expedite and standardize data intake.
• Proactive direct notifications to affected projects to reduce reporting-to-fix delay, as project-level fixes regularly outpace the reporting/awareness interval (Nakano et al., 2020).
• Formalization of vulnerability management protocols, enrichment of metadata (CVSS, CPE, CWE), and reinforcement of best practices (e.g., clear SECURITY.md files for OSS).
• Further integration and cross-linking of tool ecosystems (e.g., Goblin, OSV.dev, OpenDigger) for richer, multi-modal analysis and propagation tracking (Yang-Smith et al., 7 Feb 2025).
• Continuous upgrading of data augmentation and annotation pipelines to handle domain drift and entity evolution (Hu et al., 22 May 2024).
• Further paper of anonymized “silent repairs”, embargo policy impact, and the sociotechnical implications of decentralized disclosure and autonomous governance (Przymus et al., 4 Apr 2025, Amirov et al., 1 May 2025).

In summary, the CVE disclosure process has evolved from a labor-intensive, manual pipeline into an increasingly automated and collaborative system, underpinned by normalization ontologies, ML-driven extraction and mapping, decentralized coordination, and adaptive feedback. The result is improved timeliness, data quality, and actionable vulnerability intelligence for global software ecosystems.