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LLMVul, a vulnerability-labeled dataset of LLM-generated C/C++ functions mined from real production repositories, enables reproducible research on vulnerability detection, security evaluation of LLM-generated code, and analysis of vulnerability patterns in AI-assisted software development.
Aggressive path randomization can cut attack success rates to as low as 4-20% while boosting network throughput by nearly 31%.