CyberForge framework generates synthetic data to train cybersecurity LLM agents

Researchers have developed CyberForge, a novel framework designed to generate synthetic security training data for large language model (LLM) agents. This framework injects verified vulnerabilities into real C/C++ software projects, ensuring that the injected code is compilable and that the vulnerabilities can be dynamically triggered. The resulting dataset, comprising 1034 validated vulnerabilities across 80 projects, significantly improves the performance of LLM agents on security benchmarks like SEC-bench and PatchEval. AI

IMPACT Enhances LLM agent capabilities in cybersecurity by providing a scalable method for generating realistic vulnerability data.

RANK_REASON The cluster describes a research paper detailing a new framework for generating synthetic data for AI training. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• C Cpp Programming Languages
• GPT 5.4 Mini
• PatchEval
• SEC-Bench

• paper
• product

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