How Reliable Are NVD CWE Labels? A Large-Scale Semantic Audit with Seclometry

• URL: http://arxiv.org/abs/2608.21977v1
• Date: Sat, 22 Aug 2026 14:27:21 GMT
• Title: How Reliable Are NVD CWE Labels? A Large-Scale Semantic Audit with Seclometry
• Abstract summary: CWE labels in the National Vulnerability Database (NVD) are widely treated as ground truth for vulnerability search, scanner evaluation, benchmark construction, learning-based security tools, and vulnerability prioritization.<n>This paper presents a large-scale, code-semantics-grounded measurement of CWE labeling quality in NVD.<n>Applying CWEAgent to 15,556 open-source CVEs disclosed from 2017-2026, we find that only 49.70% of NVD CWE labels exactly match the code-grounded label.
• Score: 14.297363838133457
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: CWE labels in the National Vulnerability Database (NVD) are widely treated as ground truth for vulnerability search, scanner evaluation, benchmark construction, learning-based security tools, and vulnerability prioritization. Yet their reliability has not been systematically measured at scale, despite growing concerns about NVD's enrichment backlog and anecdotal reports of inaccurate, ambiguous, or missing labels. This paper presents a large-scale, code-semantics-grounded measurement of CWE labeling quality in NVD. We build CWEAgent, a validated auditing instrument based on seclometry, a structured representation of vulnerability semantics that captures the root cause, trigger condition, violated security property, exploit mechanism, and impact of vulnerable code. On a manually curated benchmark of 100 open-source CVEs, CWEAgent achieves 85% top-1 accuracy and 92% ambiguity-aware accuracy. Applying CWEAgent to 15,556 open-source CVEs disclosed from 2017-2026, we find that only 49.70% of NVD CWE labels exactly match the code-grounded label. Another 31.37% are defensible alternatives under taxonomy ambiguity, while 3.63% are evidence-inconsistent likely errors. Label reliability varies sharply by assigning organization and weakness type, and apparent project- or language-level differences are largely composition effects of those underlying weakness types. Evidence-inconsistent labels have also increased over time. Through manual review of 434 confirmed mislabels, we identify six recurring error patterns, showing that CWE noise is a structural problem in vulnerability metadata rather than isolated annotation mistakes.

Related papers

• Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
  Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
  arXiv  Detail & Related papers  (2026-09-14T17:44:51Z)
• Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text [3.2712318205295907]
  Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.<n>We study this problem as fine-grained inconsistency.<n>A fine-tuned 300M encoder reaches 61.9% accuracy, compared with 61.5% for a LoRA-adapted Qwen3.5-9B model and 61.3% for GPT-adapted generative models.
  arXiv  Detail & Related papers  (2026-07-29T01:03:53Z)
• Fantastic Adaptive Taxonomies and How to Use Them [100.20614173157708]
  AdaMAST converts execution traces into a compact, evidence-grounded failure taxonomy.<n>No code is hand-authored, no trace human-annotated.<n>In agent-system search, taxonomy-coded diagnoses of failed candidates outperform free-form.
  arXiv  Detail & Related papers  (2026-07-17T17:41:16Z)
• VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilog [12.133186244228037]
  We propose an embedding-based bug-detection framework that detects and classifies bugs at both module and line-level granularity.<n>Our method achieves about 89% precision in identifying common CWEs such as CWE-1244 and CWE-1245, and 96% accuracy in detecting line-level bugs.
  arXiv  Detail & Related papers  (2026-04-15T19:25:57Z)
• Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study [51.717224133855886]
  Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments.<n>We analyze 17,022 skills (sampled from 170,226 on SkillsMP) using static analysis, sandbox testing, and manual inspection.<n>We identify 520 vulnerable skills with 1,708 issues and derive a taxonomy of 10 leakage patterns (4 accidental and 6 adversarial)
  arXiv  Detail & Related papers  (2026-04-03T14:50:16Z)
• Enhancing and Reporting Robustness Boundary of Neural Code Models for Intelligent Code Understanding [21.582594699123277]
  We propose ENBECOME, a novel black-box training-free and lightweight adversarial defense.<n> ENBECOME operates solely during inference, introducing random, semantics-preserving perturbations to input code snippets.<n>Results show that ENBECOME significantly reduces attack success rates while maintaining high accuracy.
  arXiv  Detail & Related papers  (2026-03-25T09:31:20Z)
• What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs [46.325755802511026]
  We developLM, a dual-method pipeline that integrates two approaches based on a Large Language Model (LLM) and a fine-tuned small language model.<n>LM successfully identified 111 of 5,140 recent Linux kernel patches addressing OOB or UAF vulnerabilities, with 90 true positives confirmed by manual verification.
  arXiv  Detail & Related papers  (2025-09-26T18:06:36Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• Weakly Supervised Vulnerability Localization via Multiple Instance Learning [46.980136742826836]
  We propose a novel approach called WAVES for WeAkly supervised Vulnerability localization via multiplE inStance learning.<n>WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements.<n>Our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
  arXiv  Detail & Related papers  (2025-09-14T15:11:39Z)
• SliceLocator: Locating Vulnerable Statements with Graph-based Detectors [33.395068754566935]
  SliceLocator identifies the most relevant taint flow by selecting the highest-weighted flow path from all potential vulnerability-triggering statements.<n>We demonstrate that SliceLocator consistently performs well on four state-of-the-art GNN-based vulnerability detectors.
  arXiv  Detail & Related papers  (2024-01-05T10:15:04Z)
• VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification [4.837912059099674]
  VFFINDER is a graph-based approach for automated silent vulnerability fix identification. It distinguishes vulnerability-fixing commits from non-fixing ones using attention-based graph neural network models. Our results show that VFFINDER significantly improves the state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109% in F1.
  arXiv  Detail & Related papers  (2023-09-05T05:55:18Z)
• Robust Deep Semi-Supervised Learning: A Brief Introduction [63.09703308309176]
  Semi-supervised learning (SSL) aims to improve learning performance by leveraging unlabeled data when labels are insufficient. SSL with deep models has proven to be successful on standard benchmark tasks. However, they are still vulnerable to various robustness threats in real-world applications.
  arXiv  Detail & Related papers  (2022-02-12T04:16:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.