Large language models (LLMs) have advanced automated code generation but often produce code with critical security flaws, including buffer overflows, memory leaks, and unsafe file handling.While prior work emphasizes post-hoc vulnerability detection, we introduce a framework for secure-by-construction code generation via parameter-efficient fine-tuning (PEFT).We construct a secure training dataset by automatically fixing 7 high-impact vulnerability types in 37,540 C code samples from CodeNet, achieving 95.36% CWE reduction.We then apply prompt and prefix tuning to four open-source models (CodeGen-16B/6B-multi and StarCoder2-7B/3B), updating fewer than 1% of the parameters.On the LLMSe-cEval benchmark, our approach increases secure code generations from 20 to 36 for StarCoder2-3B and from 10 to 27 for CodeGen-6B.These results demonstrate that PEFT can substantially improve code security without full model retraining.