arXiv subjects

Yikun Li

Publications and source records attributed to Yikun Li.

At least 19 recordsLinked to original sources

arXiv2026

CoSA: Context-Aware Severity Assessment via Context Analysis with Large Language Models

Accurate vulnerability severity assessment is essential for prioritizing remediation, yet manually assessing Common Vulnerability Scoring System (CVSS) base metrics remains labor-intensive. Existing automated approaches often fail to capture the repository-level evidence required for assessing many CVSS base metrics. Such repository-aware assessment is challenging because relevant evidence is scattered across the entire repository under heavy noise. To address these challenges, we present CoSA, a Context-aware vulnerability Severity Assessment approach that infers CVSS base metrics from repository artifacts. CoSA constructs a code property graph (CPG) and applies a two-stage repository-pruning strategy: lightweight static pruning to preserve structurally proximal context, followed by an agentic large language model (LLM)-guided pruning step to retain CVSS-relevant context while collecting supporting evidence. The LLM then consolidates the retrieved repository context into compact, CVSS metric-wise textual summaries, which are fed into a lightweight transformer predictor. We also construct a higher-quality repository-level dataset comprising 6,816 CVSS labeled instances spanning 90 Common Weakness Enumeration (CWE) types. Experiments on real-world vulnerabilities show that CoSA consistently outperforms function-level and pure-LLM baselines. It improves prediction accuracy by 14.4% and Macro-F1 by 15.3% over the best-performing baseline, suggesting that explicit, metric-oriented repository context retrieval is crucial for practical and reliable automated severity assessment.

cs.CR↗

arXiv2026

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vulnerable functions require evidence from outside the function to be classified correctly. Agentic reinforcement learning (RL) could close this gap by enabling a model to gather that evidence itself, but it lacks a reliable reward, since a reward defined on the final verdict alone can be obtained without performing any investigation. We propose VulAgentRL, an agentic RL framework for interprocedural vulnerability detection built on a Code Property Graph (CPG). The CPG serves two roles: at inference time the policy queries it for callers, callees, dataflow, and other queries, and at training time the same graph verifies the evidence the policy cites. Because every CPG node carries a persistent integer identifier, this verification is an exact comparison rather than a textual match, so the reward credits verdicts that are supported by evidence. We further initialize the policy by distilling teacher investigations, and show that this warm start is necessary, since RL cannot acquire tool-use behavior it never samples. Under a repository-level split that prevents leakage, VulAgentRL outperforms state-of-the-art baselines, including frontier models, on the strict pair-wise-correct metric while issuing fewer tool calls, and its advantage persists on an out-of-distribution corpus and under class imbalance.

cs.CR↗

arXiv2026

Astrophysical Population Coordinates for Supermassive Black Hole Binaries in Pulsar Timing Array Inference

Pulsar timing arrays can probe the population physics of supermassive black-hole binaries through the nanohertz gravitational-wave background. We construct a phenomenological forward model that follows source abundance, binary residence time, the high-mass population, finite-source strain moments, and the pulsar timing response. The simulated observables constrain three standardized population coordinates: $\beta$, which controls the residence-time and spectral response; $\phi_{eff}$, which describes source normalization after accounting for its covariance with $\beta$; and $m_{eff}$, which is dominated by the high-mass cutoff. In the evaluation ensemble, the posterior-mean correlations with the simulated values are $0.928$, $0.926$, and $0.884$, with central 90 per cent coverages of $0.938\pm0.015$, $0.871\pm0.021$, and $0.898\pm0.019$, respectively. Frequency-resolved observables are most important for $\beta$, and strain moments beyond a common-process power law provide sensitivity to the normalization and high-mass coordinates; the fourth strain moment identifies $m_{eff}$ with rare, massive binaries. These coordinates quantify the relative sensitivity of the adopted PTA summaries within this population model, for which nearby population realizations retain substantial posterior overlap.

astro-ph.HE↗

arXiv2026

Efficient Robust Constrained Signal Detection via Kolmogorov Width Approximations

Robust statistical inference often faces a severe computational-statistical gap when dealing with complex parameter spaces. We investigate minimax signal detection in the Gaussian sequence model under strong $\epsilon$-contamination, where the signal belongs to a general prior constraint $K$. Existing optimal tests require computing the exact Kolmogorov $k$-width of $K$, a computationally intractable task for general non-trivial sets. We bridge this gap by proposing a polynomial-time testing framework that universally applies to balanced, type-2, and exactly 2-convex constraints. By leveraging a semidefinite programming relaxation and a modified ellipsoid method equipped with an approximate subgradient oracle, we efficiently approximate the Kolmogorov widths. Remarkably, our unconditional efficient algorithm achieves a robust detection boundary that matches existing upper bounds up to a mere polylogarithmic factor. This establishes a computationally tractable testing solution for a broad class of structured signals without requiring prior knowledge of their exact geometric complexity.

math.ST↗

arXiv2026

An Execution-Verified Multi-Language Benchmark for Code Semantic Reasoning

Evaluating whether large language models (LLMs) can recover execution-relevant program structure, rather than only produce code that passes tests, remains an open problem. Existing code benchmarks emphasize test-passing outputs, from standalone programming tasks (HumanEval, MBPP, LiveCodeBench) to repository repair (SWE-Bench); this is useful, but offers limited diagnostic signal about which program semantics a model can recover from source. We introduce TraceEval, to our knowledge the first execution-verified, multi-language benchmark for code semantic reasoning: recovering a program's runtime call structure from source code. Unlike prior call-graph benchmarks that rely on static-tool output or hand-annotated ground truth, every positive edge in TraceEval is mechanically witnessed by validation execution, eliminating annotator disagreement and label noise for observed behavior. TraceEval consists of (i) 10,583 real-world programs (2,129 test, 8,454 train) extracted from 1,600+ open-source repositories across Python, JavaScript, and Java via an LLM-assisted harness-generation pipeline with tracer validation; and (ii) a reproducible pipeline that converts any open-source repository into new verified benchmark instances. We evaluate 10 LLMs at zero-shot on the held-out test split. The strongest model, Claude-Opus-4.6, reaches an average F1 of 72.9% across the three languages. To demonstrate the train split's utility as a supervision substrate, we fine-tune the Qwen2.5-Coder family on it: lifts of up to +55.6 F1 bring tuned Qwen2.5-Coder-32B to 71.2%, within 1.7 F1 of zero-shot Claude-Opus-4.6. We release the benchmark, pipeline, baselines, and a datasheet at https://github.com/yikun-li/TraceEva

cs.SE↗

arXiv2026

TitanCA: Lessons from Orchestrating LLM Agents to Discover 100+ CVEs

Software vulnerabilities remain one of the most persistent threats to modern digital infrastructure. While static application security testing (SAST) tools have long served as the first line of defense, they suffer from high false-positive rates. This article presents TitanCA, a collaborative project between Singapore Management University and GovTech Singapore that orchestrates multiple large language model (LLM)-powered agents into a unified vulnerability discovery pipeline. Applied in open-source software, TitanCA has discovered 203 confirmed zero-day vulnerabilities and yielded 118 CVEs. We describe the four-module architecture, i.e., matching, filtering, inspection, and adaptation, and share key lessons from building and deploying an LLM-based vulnerability discovery solution in practice.

cs.CR↗

arXiv2026

Revisiting Vulnerability Patch Identification on Data in the Wild

Attacks can exploit zero-day or one-day vulnerabilities that are not publicly disclosed. To detect these vulnerabilities, security researchers monitor development activities in open-source repositories to identify unreported security patches. The sheer volume of commits makes this task infeasible to accomplish manually. Consequently, security patch detectors commonly trained and evaluated on security patches linked from vulnerability reports in the National Vulnerability Database (NVD). In this study, we assess the effectiveness of these detectors when applied in-the-wild. Our results show that models trained on NVD-derived data show substantially decreased performance, with decreases in F1-score of up to 90\% when tested on in-the-wild security patches, rendering them impractical for real-world use. An analysis comparing security patches identified in-the-wild and commits linked from NVD reveals that they can be easily distinguished from each other. Security patches associated with NVD have different distribution of commit messages, vulnerability types, and composition of changes. These differences suggest that NVD may be unsuitable as the \textit{sole} source of data for training models to detect security patches. We find that constructing a dataset that combines security patches from NVD data with a small subset of manually identified security patches can improve model robustness.

cs.SE↗

arXiv2026

Real time filtering algorithms

This paper presents a systematic review of recent advances in nonlinear filtering algorithms, structured into three principal categories: Kalman-type methods, Monte Carlo methods, and the Yau-Yau algorithm. For each category, we provide a comprehensive synthesis of theoretical developments, algorithmic variants, and practical applications that have emerged in recent years. Importantly, this review addresses both continuous-time and discrete-time system formulations, offering a unified review of filtering methodologies across different frameworks. Furthermore, our analysis reveals the transformative influence of artificial intelligence breakthroughs on the entire nonlinear filtering field, particularly in areas such as learning-based filters, neural network-augmented algorithms, and data-driven approaches.

math.OC↗

arXiv2026

Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection

Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches still operate at the function level, where models are asked to predict whether a single function is vulnerable without inter-procedural context. In practice, vulnerability presence and root cause often depend on contextual information. Naively appending such context is not a reliable solution: real-world context is long, redundant, and noisy, and we find that unstructured context frequently degrades the performance of strong fine-tuned code models. We present CPRVul, a context-aware vulnerability detection framework that couples Context Profiling and Selection with Structured Reasoning. CPRVul constructs a code property graph, and extracts candidate context. It then uses an LLM to generate security-focused profiles and assign relevance scores, selecting only high-impact contextual elements that fit within the model's context window. In the second phase, CPRVul integrates the target function, the selected context, and auxiliary vulnerability metadata to generate reasoning traces, which are used to fine-tune LLMs for reasoning-based vulnerability detection. We evaluate CPRVul on three high-quality vulnerability datasets: PrimeVul, TitanVul, and CleanVul. Across all datasets, CPRVul consistently outperforms function-only baselines, achieving accuracies ranging from 64.94% to 73.76%, compared to 56.65% to 63.68% for UniXcoder. Specifically, on the challenging PrimeVul benchmark, CPRVul achieves 67.78% accuracy, outperforming prior state-of-the-art approaches, improving accuracy from 55.17% to 67.78% (22.9% improvement). Our ablations further show that neither raw context nor processed context alone benefits strong code models; gains emerge only when processed context is paired with structured reasoning.

cs.CR↗

arXiv2026

PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing

Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with Large Language Model (LLM)-powered agents, but existing approaches either rely on a single generic agent that struggles in complex scenarios or narrowly specialized agents that cannot adapt to diverse vulnerability types. We therefore introduce PenForge, a framework that dynamically constructs expert agents during testing rather than relying on those prepared beforehand. By integrating automated reconnaissance of potential attack surfaces with agents instantiated on the fly for context-aware exploitation, PenForge achieves a 30.0% exploit success rate (12/40) on CVE-Bench in the particularly challenging zero-day setting, which is a 3 times improvement over the state-of-the-art. Our analysis also identifies three opportunities for future work: (1) supplying richer tool-usage knowledge to improve exploitation effectiveness; (2) extending benchmarks to include more vulnerabilities and attack types; and (3) fostering developer trust by incorporating explainable mechanisms and human review. As an emerging result with substantial potential impact, PenForge embodies the early-stage yet paradigm-shifting idea of on-the-fly agent construction, marking its promise as a step toward scalable and effective LLM-driven penetration testing.

cs.SE↗

arXiv2025

GenSIaC: Toward Security-Aware Infrastructure-as-Code Generation with Large Language Models

In recent years, Infrastructure as Code (IaC) has emerged as a critical approach for managing and provisioning IT infrastructure through code and automation. IaC enables organizations to create scalable and consistent environments, effectively managing servers and development settings. However, the growing complexity of cloud infrastructures has led to an increased risk of misconfigurations and security vulnerabilities in IaC scripts. To address this problem, this paper investigates the potential of Large Language Models (LLMs) in generating security-aware IaC code, avoiding misconfigurations introduced by developers and administrators. While LLMs have made significant progress in natural language processing and code generation, their ability to generate secure IaC scripts remains unclear. This paper addresses two major problems: 1) the lack of understanding of security weaknesses in IaC scripts generated by LLMs, and 2) the absence of techniques for enhancing security in generating IaC code with LLMs. To assess the extent to which LLMs contain security knowledge, we first conduct a comprehensive evaluation of base LLMs in recognizing major IaC security weaknesses during the generation and inspection of IaC code. Then, we propose GenSIaC, an instruction fine-tuning dataset designed to improve LLMs' ability to recognize potential security weaknesses. Leveraging GenSIaC, we fine-tune LLMs and instruct models to generate security-aware IaC code. Our evaluation demonstrates that our models achieve substantially improved performance in recognizing and preventing IaC security misconfigurations, e.g., boosting the F1-score from 0.303 to 0.858. Additionally, we perform ablation studies and explore GenSIaC's generalizability to other LLMs and its cross-language capabilities.

cs.CR↗

arXiv2025

Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework

Current learning-based Automated Vulnerability Repair (AVR) approaches, while promising, often fail to generalize effectively in real-world scenarios. Our diagnostic analysis reveals three fundamental weaknesses in state-of-the-art AVR approaches: (1) limited cross-repository generalization, with performance drops on unseen codebases; (2) inability to capture long-range dependencies, causing a performance degradation on complex, multi-hunk repairs; and (3) over-reliance on superficial lexical patterns, leading to significant performance drops on vulnerabilities with minor syntactic variations like variable renaming. To address these limitations, we propose SeCuRepair, a semantics-aligned, curriculum-driven, and reasoning-enhanced framework for vulnerability repair. At its core, SeCuRepair adopts a reason-then-edit paradigm, requiring the model to articulate why and how a vulnerability should be fixed before generating the patch. This explicit reasoning enforces a genuine understanding of repair logic rather than superficial memorization of lexical patterns. SeCuRepair also moves beyond traditional supervised fine-tuning and employs semantics-aware reinforcement learning, rewarding patches for their syntactic and semantic alignment with the oracle patch rather than mere token overlap. Complementing this, a difficulty-aware curriculum progressively trains the model, starting with simple fixes and advancing to complex, multi-hunk coordinated edits. We evaluate SeCuRepair on strict, repository-level splits of BigVul and newly crafted PrimeVul_AVR datasets. SeCuRepair significantly outperforms all baselines, surpassing the best-performing baselines by 34.52% on BigVul and 31.52% on PrimeVul\textsubscript{AVR} in terms of CodeBLEU, respectively. Comprehensive ablation studies further confirm that each component of our framework contributes to its final performance.

cs.SE↗

arXiv2025

SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios

Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benchmarks have provided valuable insights, but they fail to capture scenarios in which vulnerabilities are actually introduced by human developers, making fair comparisons between humans and agents infeasible. We therefore introduce SecureVibeBench, a benchmark of 105 C/C++ secure coding tasks sourced from 41 projects in OSS-Fuzz for code agents. SecureVibeBench has the following features: (i) realistic task settings that require multi-file edits in large repositories, (ii)~aligned contexts based on real-world open-source vulnerabilities with precisely identified vulnerability introduction points, and (iii) comprehensive evaluation that combines functionality testing and security checking with both static and dynamic oracles. We evaluate 5 popular code agents like OpenHands, supported by 5 LLMs (e.g., Claude sonnet 4.5) on SecureVibeBench. Results show that current agents struggle to produce both correct and secure code, as even the best-performing one, produces merely 23.8\% correct and secure solutions on SecureVibeBench. Our code and data are on https://github.com/iCSawyer/SecureVibeBench.

cs.SE↗

arXiv2025

PatchSeeker: Mapping NVD Records to their Vulnerability-fixing Commits with LLM Generated Commits and Embeddings

Software vulnerabilities pose serious risks to modern software ecosystems. While the National Vulnerability Database (NVD) is the authoritative source for cataloging these vulnerabilities, it often lacks explicit links to the corresponding Vulnerability-Fixing Commits (VFCs). VFCs encode precise code changes, enabling vulnerability localization, patch analysis, and dataset construction. Automatically mapping NVD records to their true VFCs is therefore critical. Existing approaches have limitations as they rely on sparse, often noisy commit messages and fail to capture the deep semantics in the vulnerability descriptions. To address this gap, we introduce PatchSeeker, a novel method that leverages large language models to create rich semantic links between vulnerability descriptions and their VFCs. PatchSeeker generates embeddings from NVD descriptions and enhances commit messages by synthesizing detailed summaries for those that are short or uninformative. These generated messages act as a semantic bridge, effectively closing the information gap between natural language reports and low-level code changes. Our approach PatchSeeker achieves 59.3% higher MRR and 27.9% higher Recall@10 than the best-performing baseline, Prospector, on the benchmark dataset. The extended evaluation on recent CVEs further confirms PatchSeeker's effectiveness. Ablation study shows that both the commit message generation method and the selection of backbone LLMs make a positive contribution to PatchSeeker. We also discuss limitations and open challenges to guide future work.

cs.SE↗

arXiv2025

Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses?

Automated vulnerability detection research has made substantial progress, yet its real-world impact remains limited. Prior work found that current vulnerability datasets suffer from issues including label inaccuracy rates of 20%-71%, extensive duplication, and poor coverage of critical Common Weakness Enumeration (CWE). These issues create a significant generalization gap where models achieve misleading In-Distribution (ID) accuracies (testing on splits from the same dataset) by exploiting spurious correlations rather than learning true vulnerability patterns. To address these limitations, we present a three-part solution. First, we introduce BenchVul, which is a manually curated and balanced test dataset covering the MITRE Top 25 Most Dangerous CWEs, to enable fair model evaluation. Second, we construct a high-quality training dataset, TitanVul, comprising 38,548 functions by aggregating seven public sources and applying deduplication and validation using a novel multi-agent LLM pipeline. Third, we propose a Realistic Vulnerability Generation (RVG) pipeline, which synthesizes context-aware vulnerability examples for underrepresented but critical CWE types through simulated development workflows. Our evaluation reveals that In-Distribution (ID) performance does not reliably predict Out-of-Distribution (OOD) performance on BenchVul. For example, a model trained on BigVul achieves the highest 0.703 ID accuracy but fails on BenchVul's real-world samples (0.493 OOD accuracy). Conversely, a model trained on our TitanVul achieves the highest OOD performance on both the real-world (0.881) and synthesized (0.785) portions of BenchVul, improving upon the next-best performing dataset by 5.3% and 11.8% respectively, despite a modest ID score (0.590). Augmenting TitanVul with our RVG further boosts this leading OOD performance, improving accuracy on real-world data by 5.8% (to 0.932).

cs.CR↗

arXiv2025

VulCoCo: A Simple Yet Effective Method for Detecting Vulnerable Code Clones

Code reuse is common in modern software development, but it can also spread vulnerabilities when developers unknowingly copy risky code. The code fragments that preserve the logic of known vulnerabilities are known as vulnerable code clones (VCCs). Detecting those VCCs is a critical but challenging task. Existing VCC detection tools often rely on syntactic similarity or produce coarse vulnerability predictions without clear explanations, limiting their practical utility. In this paper, we propose VulCoCo, a lightweight and scalable approach that combines embedding-based retrieval with large language model (LLM) validation. Starting from a set of known vulnerable functions, we retrieve syntactically or semantically similar candidate functions from a large corpus and use an LLM to assess whether the candidates retain the vulnerability. Given that there is a lack of reproducible vulnerable code clone benchmarks, we first construct a synthetic benchmark that spans various clone types. Our experiments on the benchmark show that VulCoCo outperforms prior state-of-the-art methods in terms of Precision@k and mean average precision (MAP). In addition, we also demonstrate VulCoCo's effectiveness in real-world projects by submitting 400 pull requests (PRs) to 284 open-source projects. Among them, 75 PRs were merged, and 15 resulted in newly published CVEs. We also provide insights to inspire future work to further improve the precision of vulnerable code clone detection.

cs.SE↗

arXiv2025

Compact Varactor-Integrated RIS for Wideband and Continuously Tunable Beamforming

This letter presents a novel Reconfigurable Intelligent Surface (RIS) that features a low-profile structure, wide operating bandwidth, and continuous phase control. By incorporating a middle patch layer without introducing an additional air gap, the proposed design maintains a thin form factor, while achieving a smooth 310{\deg} phase shift over 10\% bandwidth at 6.1 GHz with excellent reflection. A fabricated 10*10 RIS array exhibits stable performance, enabling precise beam control across a 600 MHz bandwidth. These results highlight the potential of the proposed low-profile, wideband RIS with continuous phase tuning for next-generation wireless communication systems.

eess.SP↗

arXiv2025

R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation

Large language models (LLMs) have shown promising performance in software vulnerability detection, yet their reasoning capabilities remain unreliable. We propose R2Vul, a method that combines reinforcement learning from AI feedback (RLAIF) and structured reasoning distillation to teach small code LLMs to detect vulnerabilities while generating security-aware explanations. Unlike prior chain-of-thought and instruction tuning approaches, R2Vul rewards well-founded over deceptively plausible vulnerability explanations through RLAIF, which results in more precise detection and high-quality reasoning generation. To support RLAIF, we construct the first multilingual preference dataset for vulnerability detection, comprising 18,000 high-quality samples in C\#, JavaScript, Java, Python, and C. We evaluate R2Vul across five programming languages and against four static analysis tools, eight state-of-the-art LLM-based baselines, and various fine-tuning approaches. Our results demonstrate that a 1.5B R2Vul model exceeds the performance of its 32B teacher model and leading commercial LLMs such as Claude-4-Opus. Furthermore, we introduce a lightweight calibration step that reduces false positive rates under varying imbalanced data distributions. Finally, through qualitative analysis, we show that both LLM and human evaluators consistently rank R2Vul model's reasoning higher than other reasoning-based baselines.

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