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...
Yu Nong, Yao Du, Majid Behravan et al.· 0 citations
The impact of Python applications has been evidenced by their widespread presence in some of the most impactful software domains, such as machine learning frameworks and scientific computing platforms. These applications often integrate native code components written in a lower-level programming language like C. This m...
This work investigates the data behind vulnerability analysis through a dataset-centric taxonomy that separates what an artifact is (code, metadata, patches, tests/PoCs, reasoning, traces), how good it is (realism, label evidence, scale, diversity, leakage, availability), and what it is used for.
Yu Nong, Yao Du, Tian-Xiang Xu et al.· 0 citations
PolyFlow is a neural-symbolic framework for statically reasoning about information flow across language boundaries, combining large language models (LLMs) and static analysis synergistically, and is cost-effective and superior to various kinds of state-of-the-art baselines.
Haoran Yang, Zhi-Xuan Zhong, Jiawei Guo et al.· 0 citations
POVGEN, a low-cost neuro-symbolic framework that makes PoV generation cost-effective via semantic focusing and LLM-guided constraint reasoning using open-weight models, and fine-tuned open-weight models match frontier commercial LLMs on key sub-tasks while running locally at no per-sample API cost.
A structural abstraction is proposed that uses a static call graph as a shared backbone and projects driver-specific dynamic coverage onto it to derive driver-induced subgraphs and shows that multi-driver fuzzing is fundamentally a structural exploration problem.
Bai-Hong Chen, Ming Hua, Wei-Feng Pan et al.· 0 citations
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