2026· International Conference on Security and Cryptography· pp. 1065-1072· 0 citations· 29 references
Computer Science
TL;DR
A hybrid pipeline combining static vulnerability candidate selection using Latent Semantic Analysis with dynamic verification through Large Language Model-assisted fuzzing is presented, which reduces manual effort while still requiring human validation.
Abstract
: As modern software systems grow in complexity, the number of vulnerabilities has grown rapidly, rendering traditional security strategies—including manual code reviews and rule-based static analyzers—increasingly resource-intensive and unable to keep pace with emerging threats. This paper presents a hybrid pipeline combining static vulnerability candidate selection using Latent Semantic Analysis with dynamic verification through Large Language Model-assisted fuzzing. The approach identifies semantically similar code patterns to known vulnerabilities without exact syntactic matches, and automates the generation of fuzzing harnesses and seed inputs to overcome coverage plateaus. We evaluate our pipeline on real-world C libraries (LibTIFF, libexpat, FreeType) with documented vulnerabilities. Results demonstrate that vulnerability candidates are ranked within the Top 3–8% of all functions, and model-generated artifacts yield a 5-fold speedup over random fuzzing. The LLM Gemini 3 Pro achieves the highest harness generation success rate at 83% (Pass@3). Our analysis reveals fundamental limitations: the mixed-vector problem dilutes vulnerability signals in multifunctional code, safety alignment mechanisms can inadvertently neutralize vulnerabilities through defensive code insertion, and static feature extraction without build context achieves only 19.93% recall. The pipeline reduces manual effort while still requiring human validation.
Vulnerability detection via static analysis traditionally relies on security experts encoding insecure coding patterns into algorithmic rules. However, this approach often focuses on syntactic patterns and overlooks deeper semantic information in the code, such as the meanings of variable and function names. As software systems grow more complex, modeling vulnerabilities using only syntactic rules becomes increasingly challenging. In this paper, we propose a semantics-aware approach to detecting software vulnerabilities. We present SETYPE, a semantics-aware type system that can be derived directly from source code based solely on the meanings of symbols and expressions in natural language. In the SETYPE type system, both type inference and checking are performed by Large Language Models (LLMs), and a failed type check indicates a potential vulnerability. We prototype PYSETYPE to demonstrate the feasibility of SETYPE for detecting vulnerabilities in Python web applications. Our evaluation on real-world applications achieves 87% detection precision and 88% detection accuracy. Using PYSETYPE, we identified 15 potential zero-day vulnerabilities, nine of which were confirmed by developers.
Findings show that insecure code generation is not merely a collection of independent defects, but a structured and prompt-conditioned phenomenon, motivating cluster-aware verification and prompt-level intervention for safer LLM-assisted programming.
Software vulnerability detection increasingly relies on learning-based models. However, most existing methods analyze individual functions in isolation, making it difficult to capture vulnerabilities caused by cross-function calls; directly introducing complete call chains can also lead to context expansion and noise accumulation. This paper proposes VulRESC, a vulnerability detection framework based on risk path extraction and interprocedural semantic completion. The method first constructs code property graphs and extracts call paths related to external inputs and high-risk operations through a riskpoint- driven pruning strategy. It then uses large language models to generate structured summaries for callees along the sequence in a bottom-up manner and introduces a variable-name alignment mechanism to bridge the semantic gap across function boundaries. Finally, the proposed DualVulBERT dual-stream model is employed to jointly identify vulnerabilities by fusing source code features and inter-procedural semantic summaries through cross-modal attention. Experimental results on real-world C/C++ vulnerability datasets show that VulRESC achieves an accuracy of 68.03% and an F1-score of 69.37%, outperforming representative existing methods.
Yu-Kun Dong, Shuo Wang, Shanchen Pang· International journal of sof...· 0 citations
ChipFuzzer is proposed, a hardware fuzzing framework that leverages the semantic reasoning capabilities of large language models (LLMs) to improve fuzzing effectiveness and improves average condition coverage and bug detection rate over the strongest baseline.
Kun Wang, Cangyuan Li, Kaiyan Chang et al.· 0 citations
Experimental results show that AST-based structural features substantially improve recall compared with the TF-IDF baseline, while the combined TF-IDF and AST representation maintains this improved performance.
Vani Pasupula, M. N. V. Manikanth, Nagaraju Vassey· International Journal of Cre...· 0 citations
The LLM-Enhanced Component Dependency Evolution Graph (CDEG) framework is proposed, a hybrid representation that fuses structural features extracted by Tree-sitter with semantic embeddings derived from a fine-tuned LLM, effectively distinguishing backported patches from code refactoring.
Yuan-Jun Gao, Hong-Zhou Wu, Yu-Jia Luo et al.· Mathematics· 0 citations
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