LLMVD.js uses LLM agents to confirm 84% of taint-style vulnerabilities on public benchmarks (vs. <22% for prior tools) and generates validated exploits for 36 of 260 new packages (vs. ≤2 for traditional tools).
Large language model (llm) for software security: Code analysis, malware analysis, reverse engineering
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cs.CR 3years
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UNVERDICTED 3representative citing papers
LLM plus Ghidra extracts 20 protocol elements from Mirai binaries at 100% accuracy and generates pseudo-C2 servers that reproduce 7 of 10 DDoS attack vectors.
LCC-LLM creates a code-centric dataset and RAG-based LLM framework that reaches 0.634 average semantic similarity on 43 malware tasks and 10/10 pass rate in real-world case studies.
citing papers explorer
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Taint-Style Vulnerability Detection and Confirmation for Node.js Packages Using LLM Agent Reasoning
LLMVD.js uses LLM agents to confirm 84% of taint-style vulnerabilities on public benchmarks (vs. <22% for prior tools) and generates validated exploits for 36 of 260 new packages (vs. ≤2 for traditional tools).
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LLM-assisted Generation of Pseudo-C2 Servers for IoT Malware Dynamic Analysis
LLM plus Ghidra extracts 20 protocol elements from Mirai binaries at 100% accuracy and generates pseudo-C2 servers that reproduce 7 of 10 DDoS attack vectors.
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LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution
LCC-LLM creates a code-centric dataset and RAG-based LLM framework that reaches 0.634 average semantic similarity on 43 malware tasks and 10/10 pass rate in real-world case studies.