arXiv subjects

Yu Nong

Publications and source records attributed to Yu Nong.

7 recordsLinked to original sources

arXiv2026

The Data Problem in Software Vulnerability Analysis: Artifacts, Quality, and Consumption

Learning- and LLM-based software vulnerability analysis is only as trustworthy as the data it is trained and evaluated on, yet that data is rarely examined as a first-class object. We investigate 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. From a systematically assembled corpus of 1522 papers covering 2016-2026 plus foundational earlier work we deep-code a tiered set of 111 anchor papers, backing every affirmative rubric-graded value with a verbatim span, and we report, per attribute, both how much it has been studied and how well datasets achieve it. The results trace an evidence ladder: executable artifacts are the only major type where 15 of the 24 datasets are both graded real-world and carry labels that received an independent check, while code-sample datasets-the largest category in both the auto-tagged corpus and the anchor set-are the least realistic: 20 of the 41 draw their vulnerabilities from authentic projects or CVEs, but only 3 keep the sample at the unit the code is deployed in, and only 2 do both-though these are coarse component tests, and just one code-sample dataset meets the codebook's stricter full-context real-world grade. Among these, leakage goes unaddressed by 49 of the 90 datasets where it applies, more than a quarter say nothing about availability, reasoning data has arrived only recently and is mostly model-generated, and primary trace corpora remain limited to three datasets, the total after a corpus-wide screen and a full-text check of every candidate it surfaced, with further datasets releasing traces secondarily behind benchmarks and harnesses.

cs.SE↗

arXiv2026

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

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.

cs.CR↗

arXiv2026

Neuro-Symbolic Proof-of-Vulnerability Generation with Open-Weight Models

Software vulnerabilities are persistent, but validating them remains difficult: a Proof-of-Vulnerability (PoV) requires a concrete input that triggers the vulnerable behavior, yet public triggering inputs are often unavailable for disclosed vulnerabilities. Existing techniques make different tradeoffs in effectiveness, scalability, cost, and controllability, leaving room for complementary designs. To complement them, we present 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. POVGEN first localizes vulnerability-relevant regions (utilizing patch information if available), then performs path-sensitive reachability analysis, and finally generates PoVs by extracting and solving constraints with LLM-guided reasoning backed by an SMT solver. POVGEN successfully generates PoVs for 78.98% of vulnerabilities in a recent benchmark, outperforming fuzzing (up to 50.20%) and symbolic execution (2.45%). On 250 real-world CVEs without public PoVs, it generates valid PoVs for 74.80% of cases and reproduces 65.1% when without patch information. The fine-tuned open-weight models match frontier commercial LLMs on key sub-tasks (i.e., the core constraint-reasoning steps) while running locally at no per-sample API cost. Applying the generated PoVs revealed six flawed patches in disclosed CVEs (all subsequently fixed) and five previously unreported vulnerabilities (of which four have been confirmed and fixed by the developers).

cs.SE↗

arXiv2024

APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching

Timely and effective vulnerability patching is essential for cybersecurity defense, for which various approaches have been proposed yet still struggle to generate valid and correct patches for real-world vulnerabilities. In this paper, we leverage the power and merits of pre-trained language language models (LLMs) to enable automated vulnerability patching using no test input/exploit evidence and without model training/fine-tuning. To elicit LLMs to effectively reason about vulnerable code behaviors, which is essential for quality patch generation, we introduce vulnerability semantics reasoning and adaptive prompting on LLMs and instantiate the methodology as APPATCH, an automated LLM-based patching system. Our evaluation of APPATCH on 97 zero-day vulnerabilities and 20 existing vulnerabilities demonstrates its superior performance to both existing prompting methods and state-of-the-art non-LLM-based techniques (by up to 28.33% in F1 and 182.26% in recall over the best baseline). Through APPATCH, we demonstrate what helps for LLM-based patching and how, as well as discussing what still lacks and why.

cs.CR↗

arXiv2024

VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs

Detecting vulnerabilities is vital for software security, yet deep learning-based vulnerability detectors (DLVD) face a data shortage, which limits their effectiveness. Data augmentation can potentially alleviate the data shortage, but augmenting vulnerable code is challenging and requires a generative solution that maintains vulnerability. Previous works have only focused on generating samples that contain single statements or specific types of vulnerabilities. Recently, large language models (LLMs) have been used to solve various code generation and comprehension tasks with inspiring results, especially when fused with retrieval augmented generation (RAG). Therefore, we propose VulScribeR, a novel LLM-based solution that leverages carefully curated prompt templates to augment vulnerable datasets. More specifically, we explore three strategies to augment both single and multi-statement vulnerabilities, with LLMs, namely Mutation, Injection, and Extension. Our extensive evaluation across four vulnerability datasets and DLVD models, using three LLMs, show that our approach beats two SOTA methods Vulgen and VGX, and Random Oversampling (ROS) by 27.48%, 27.93%, and 15.41% in f1-score with 5K generated vulnerable samples on average, and 53.84%, 54.10%, 69.90%, and 40.93% with 15K generated vulnerable samples. Our approach demonstrates its feasibility for large-scale data augmentation by generating 1K samples at as cheap as US$ 1.88.

cs.SE↗

arXiv2024

Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities

Security vulnerabilities are increasingly prevalent in modern software and they are widely consequential to our society. Various approaches to defending against these vulnerabilities have been proposed, among which those leveraging deep learning (DL) avoid major barriers with other techniques hence attracting more attention in recent years. However, DL-based approaches face critical challenges including the lack of sizable and quality-labeled task-specific datasets and their inability to generalize well to unseen, real-world scenarios. Lately, large language models (LLMs) have demonstrated impressive potential in various domains by overcoming those challenges, especially through chain-of-thought (CoT) prompting. In this paper, we explore how to leverage LLMs and CoT to address three key software vulnerability analysis tasks: identifying a given type of vulnerabilities, discovering vulnerabilities of any type, and patching detected vulnerabilities. We instantiate the general CoT methodology in the context of these tasks through VSP , our unified, vulnerability-semantics-guided prompting approach, and conduct extensive experiments assessing VSP versus five baselines for the three tasks against three LLMs and two datasets. Results show substantial superiority of our CoT-inspired prompting (553.3%, 36.5%, and 30.8% higher F1 accuracy for vulnerability identification, discovery, and patching, respectively, on CVE datasets) over the baselines. Through in-depth case studies analyzing VSP failures, we also reveal current gaps in LLM/CoT for challenging vulnerability cases, while proposing and validating respective improvements.

cs.CR↗

arXiv2023

VGX: Large-Scale Sample Generation for Boosting Learning-Based Software Vulnerability Analyses

Accompanying the successes of learning-based defensive software vulnerability analyses is the lack of large and quality sets of labeled vulnerable program samples, which impedes further advancement of those defenses. Existing automated sample generation approaches have shown potentials yet still fall short of practical expectations due to the high noise in the generated samples. This paper proposes VGX, a new technique aimed for large-scale generation of high-quality vulnerability datasets. Given a normal program, VGX identifies the code contexts in which vulnerabilities can be injected, using a customized Transformer featured with a new value-flowbased position encoding and pre-trained against new objectives particularly for learning code structure and context. Then, VGX materializes vulnerability-injection code editing in the identified contexts using patterns of such edits obtained from both historical fixes and human knowledge about real-world vulnerabilities. Compared to four state-of-the-art (SOTA) baselines (pattern-, Transformer-, GNN-, and pattern+Transformer-based), VGX achieved 99.09-890.06% higher F1 and 22.45%-328.47% higher label accuracy. For in-the-wild sample production, VGX generated 150,392 vulnerable samples, from which we randomly chose 10% to assess how much these samples help vulnerability detection, localization, and repair. Our results show SOTA techniques for these three application tasks achieved 19.15-330.80% higher F1, 12.86-19.31% higher top-10 accuracy, and 85.02-99.30% higher top-50 accuracy, respectively, by adding those samples to their original training data. These samples also helped a SOTA vulnerability detector discover 13 more real-world vulnerabilities (CVEs) in critical systems (e.g., Linux kernel) that would be missed by the original model.

cs.SE↗