Common Vulnerabilities and Exposures (CVEs)

• Common Vulnerabilities and Exposures is a standardized framework that uniquely identifies, categorizes, and tracks security vulnerabilities across platforms.
• It underpins coordinated vulnerability disclosure and enables risk assessment, patch automation, and cross-platform security analytics.
• Automation via machine learning and NLP pipelines enhances CVE analysis, enabling efficient classification and timely remediation insights.

The Common Vulnerabilities and Exposures (CVE) system is a standardized reference framework for cataloging and tracking publicly known information security vulnerabilities in software and hardware. Each CVE entry represents a unique, platform-agnostic identifier for a specific vulnerability, providing a foundation for coordinated vulnerability disclosure, risk assessment, remediation, and downstream analysis within the global cybersecurity ecosystem. The CVE corpus plays a pivotal role in software maintenance, security research, and operational risk management by enabling reliable cross-referencing, aggregation, and characterization of vulnerabilities across products, vendors, and platforms.

1. Foundation and Structure of CVEs

A CVE entry encapsulates several core components: a unique identifier (e.g., CVE-2023-12345), a brief natural language description, references to advisory or technical documentation, and, increasingly, rich metadata such as Common Weakness Enumeration (CWE) and Common Platform Enumeration (CPE) links. The CVE records are curated and published through entities such as the MITRE Corporation and are further aggregated in the National Vulnerability Database (NVD), with over 300,000 CVEs reported since 1999 (Fayyazi et al., 22 Oct 2024). Structured referencing, completeness, and machine readability are key attributes, with widespread consumption across vulnerability management, patch automation, and security analytics.

Notably, CVEs are intertwined with related classification systems. The CWE system provides a dictionary of software and hardware weakness types, while CVSS (Common Vulnerability Scoring System) assigns quantitative risk scores to individual CVEs for prioritization purposes. Modern CVE records also increasingly link to exploitation details, severity metrics, and environment-specific impacts, although these fields remain inconsistently populated depending on the data source and time of disclosure (Poisson et al., 2023).

2. Workflows, Coordination, and Reporting Dynamics

The lifecycle of a CVE spans from vulnerability discovery and disclosure through assignment, coordination, assessment, and, ultimately, remediation. Coordination is often facilitated by processes such as public mailing lists (e.g., oss-security) and global stakeholders including vendors, project maintainers, coordinating bodies (e.g., MITRE), and national databases (Ruohonen et al., 2020). The coordination process is socio-technical, with delays shaped more by procedural, infrastructural, and social network variables than solely by technical characteristics.

Empirical studies show that the end-to-end delay from CVE assignment request to NVD registration exhibits a median lag of 15 days, with significant variance driven by annual, monthly, and even weekend-specific temporal controls. Regression analyses reveal that a high degree of participant involvement (e.g., "too many cooks" in network terms), message length, and content entropy are associated with longer delays, as is excessive referencing of many external domains. Conversely, concise, well-supported requests—especially those referencing bug trackers and authoritative evidence—correlate with faster registration. Technical features, such as the underlying CWE type or CVSS impact, account for less of the variance in coordination timing (Ruohonen et al., 2020).

3. Machine Learning–Driven Characterization and Automation

Automated processing of CVE entries is increasingly important for scalability, completeness, and consistency. Recent work demonstrates that extracting vulnerability characteristics—such as root cause, exploit class, and mitigation guidance—from free-text CVE descriptions can be efficiently automated through NLP pipelines and machine learning classifiers.

A standard pipeline involves:

• Text normalization (lowercasing, removal of URLs and non-text characters),
• Tokenization,
• Stop word removal,
• Stemming or lemmatization, and
• Conversion to a TF-IDF (term frequency–inverse document frequency) representation:

$$\text{TF-IDF}(w, d) = \text{TF}(w, d) \times \log\left(\frac{N}{\text{df}(w)}\right)$$

where $\text{TF}(w, d)$ is the normalized frequency of word $w$ in document $d$, $\text{df}(w)$ is the number of documents containing $w$, and $N$ is the corpus size (Gonzalez et al., 2019).

Among various classifiers—including Naïve Bayes, random forests, ensembles, and AdaBoost–SVM hybrids—the Support Vector Machine (SVM) demonstrated superior performance for both efficiency and accuracy (SMO–SVM achieved 72.88% accuracy, kappa 0.71), often matching more complex ensemble methods while being far less resource intensive (Gonzalez et al., 2019). For applied tasks such as automatic IoT vulnerability type classification, SVMs, enhanced by CPE-derived features and robust pre-processing, achieved per-class precision and recall in the 70–85% range for major IoT device categories, though performance decreased for rarer classes, highlighting the limitation of the source CVE records themselves (Blinowski et al., 2020).

Advanced models such as hierarchical neural network architectures (e.g., ThreatZoom) and Transformer-based Siamese networks (e.g., V2W-BERT) have been developed to map CVE descriptions to CWE classes with high fidelity—reaching up to 97% accuracy in random partitions and 94% on temporally split data—by leveraging both statistical (TF-IDF, n-gram) and deeply contextual semantic features (Aghaei et al., 2020, Das et al., 2021). These automation pipelines enable large-scale, continuous population of vulnerability characteristics that would be too labor-intensive for manual curation.

4. Data Quality, Coverage Gaps, and Limitations

Despite the foundational importance of the CVE database, the corpus exhibits notable limitations. Coverage of certain domains—such as privacy-related vulnerabilities—remains exceedingly sparse, with only about 0.1% of CVEs and 4.45% of CWEs explicitly referencing privacy threats in a census of over 156,000 CVEs and 922 weakness types (Sangaroonsilp et al., 2021). These deficiencies leave significant gaps relative to the full taxonomy of privacy and security issues identified in research and practice. As a mitigation, proposals have been put forth to augment the CWE schema with new privacy weakness classes accompanied by template-driven, LaTeX-style data representations.

Other common problems include inconsistent granularity, missing or ambiguous links to product or exploit metadata, and, in legacy records, the absence of key fields (e.g., numerical CVSS vectors or detailed CPE lists). These issues hamper downstream automated classification, analytics, and integration with frameworks such as MITRE ATT&CK, and underscore the need for continuous improvement to the underlying data standards and curation processes (Blinowski et al., 2020, Sangaroonsilp et al., 2021).

5. Applications and Benchmarking in Security Research

CVE records are a cornerstone for vulnerability management systems, threat intelligence, and academic research in software security. Automated systems now leverage structured CVE data and curated vocabularies for a variety of applications:

• Vulnerability prediction and severity estimation using code-integration datasets (e.g., CVEfixes, MegaVul), which systematically align CVE entries with code changes, repair commits, and rich meta-data (Bhandari et al., 2021, Ni et al., 18 Jun 2024).
• Mapping of CVEs to attack techniques (TTPs) as formalized in the MITRE ATT&CK knowledge base, using domain-specific LLMs, hierarchical neural models, and self-distillation approaches for high-fidelity, automated impact prediction (Ampel et al., 2021, Aghaei et al., 2023, Høst et al., 25 Aug 2025).
• Benchmarking the exploitability and effectiveness of AI agents in real-world attack scenarios through sandboxes (e.g., CVE-Bench), which reproducibly host critical-severity vulnerabilities and track agent progress using detailed grading and task-specific metrics (Zhu et al., 21 Mar 2025).
• Construction of curated datasets with verifiable exploits for reproducibility in fuzzer benchmarking, patch validation, and AI-driven code auditing workflows (e.g., CVE-GENIE) (Ullah et al., 1 Sep 2025).

The importance of CVEs for open-source project management is highlighted by empirical studies of vulnerability lifecycles. Typical CVE “lifetime” (from disclosure or commit to final fix) has a median of 34 days, with language attributes (e.g., managed memory), project team size, and developer activity significantly influencing remediation timelines. Survival analysis and hazard modeling underscore the non-trivial interplay of technical and process factors in vulnerability resolution (Przymus et al., 4 Apr 2025).

6. Impact of Automation, Evidence-based Remediation, and Future Directions

Automation of characterization and enrichment of CVE data (including CVSS vector prediction, CWE/ATT&CK mapping, and exploit reproduction) has enabled cybersecurity operations to scale, reduce manual burden, and accelerate mitigation. State-of-the-art systems (e.g., CVEDrill, ProveRAG, TRIAGE) now regularly outperform general-purpose LLMs and manual analyses, with reported CVSS prediction accuracies for key metrics up to 96%, ATT&CK mapping F1-scores above 95%, and evidence-backed recommendations with over 99% alignment to ground-truth data (Aghaei et al., 2023, Fayyazi et al., 22 Oct 2024, Høst et al., 25 Aug 2025).

Automated provenance tracking and retrieval-augmented generation, as implemented in ProveRAG, addresses the challenge of hallucinated or outdated recommendations common in LLM-powered security analysis. These systems enforce self-critique and external evidence comparison to provide actionable, verifiable insights for newly emerging CVEs, even those appearing after LLM training cutoffs (Fayyazi et al., 22 Oct 2024).

Current challenges include maintaining up-to-date and comprehensive data curation (especially as over 25,000 new CVEs are reported annually), improving representation for under-served areas (e.g., privacy, UI-based exploits), and integrating hybrid/hierarchical classification pipelines. Future research directions focus on expanding context-aware, multimodal knowledge extraction, closing annotation and coverage gaps, and further refining hybrid architectures that combine rule-based and data-driven inference for robust, real-time vulnerability analytics (Poisson et al., 2023, Høst et al., 25 Aug 2025, Ullah et al., 1 Sep 2025).

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In summary, CVEs are foundational infrastructure for vulnerability management, underpinning research, operations, and collaborative response across the cybersecurity landscape. Recent advances in automation, semantic enrichment, and evidence-based analysis have significantly improved their reliability and utility, with ongoing research aimed at extending coverage, reducing manual effort, and supporting proactive remediation at scale.