The paper addresses the gap in understanding real-world LLM-in-the-loop vulnerabilities by creating the LLMCVE dataset and demonstrating that these vulnerabilities are significantly harder to repair t…

The paper analyzes GitHub security advisories for LLM-integrated open-source systems, finding that while most vulnerabilities map to existing code-level weaknesses, the architectural risks like Supply…

The paper introduces a novel multi-LLM orchestration system combined with symbolic execution to successfully detect memory vulnerabilities in uncompilable, incomplete Rust CVE code snippets, achieving…

The paper introduces LogicEval, a systematic framework and dataset (LogicDS) to evaluate automated repair techniques for logical software vulnerabilities, finding that prompt sensitivity and context l…

TitanCA presents a novel, multi-agent LLM orchestration framework that significantly improves vulnerability discovery by reducing false positives and identifying numerous zero-day vulnerabilities.

FuzzingBrain V2 is a multi-agent LLM system that significantly improves automated vulnerability discovery by ensuring all reported bugs are fuzzer-reproducible and handling complex cross-function depe…

SAILOR automates the construction of symbolic execution harnesses by combining static analysis and LLM-based synthesis, significantly improving the scalability and effectiveness of vulnerability disco…

The paper conducts an empirical evaluation of automated vulnerability detection tools across multiple software ecosystems using a curated ground-truth dataset derived from OSV, highlighting systematic…

The paper proposes VulGNN, a lightweight Graph Neural Network (GNN) model, which achieves vulnerability detection performance comparable to large language models (LLMs) while being significantly small…

VulGD is a dynamic, open-access graph database that aggregates cybersecurity data from multiple sources and uses LLM embeddings to improve vulnerability representation and risk assessment.

The paper introduces LLMVD.js, a multi-stage LLM agent pipeline that effectively detects and confirms taint-style vulnerabilities in Node.js packages, achieving significantly higher confirmation rates…

This paper empirically evaluates the security of code generated by seven popular LLMs and finds that all evaluated models generate code containing critical or high-severity vulnerabilities.

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

The paper introduces codebadger, a Model Context Protocol (MCP) server that integrates Joern's Code Property Graph (CPG) with LLMs, enabling large language models to perform large-scale, semantic prog…

This study assesses the effectiveness and cost of four modern LLMs in detecting vulnerabilities that span multiple functions (interprocedural dependencies), finding that Gemini 3 Flash offers strong c…

This paper conducts a large-scale empirical study demonstrating that Java library exploits can accurately identify affected versions, achieving high recall and precision, and proposes strategies for e…

The paper introduces a novel, large-scale dataset of vulnerable code snippets linked to CAPEC and CWE, generated using advanced LLMs, to improve automatic vulnerability detection.

This paper analyzes large-scale reasoning traces from LLM-based binary vulnerability analysis, identifying four structured, token-level implicit patterns that govern how LLMs explore code paths.

The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.

The paper introduces LCC-LLM, a code-centric framework and dataset that significantly improves the reliability of malware attribution and static analysis by grounding LLM reasoning in comprehensive, m…