Large language models (LLMs) and agentic frameworks are increasingly used for development tasks such as automated program repair (APR). Prior work has identified security risks in LLM-generated code, but most studies focus on synthetic, simplified, or isolated tasks. We present a large-scale security analysis of LLM-generated patches using 20,000+ GitHub issues from SWE-bench. We evaluate developer-written patches, two standalone LLMs (Llama 3.3 Instruct-70B and GPT-5.4 nano), and four agentic frameworks (OpenHands, AutoCodeRover, HoneyComb, and SWE-agent). We also analyze code, issue, and project-level factors associated with insecure patches. Under our evaluated SWE-bench setup, Llama 3.3 introduced 135 manually confirmed vulnerabilities across the train and test sets, compared with 12 in developer-written patches for the same issues. On the SWE-bench test set, GPT-5.4 nano introduced 3 vulnerabilities compared with 2 in developer patches. All four evaluated agentic frameworks also introduced vulnerabilities. OpenHands had the highest retained count, with 44 vulnerabilities across 11 issue instances. Vulnerabilities are associated with broader code modifications and with issues missing specific information. These results suggest that APR systems require proactive risk assessment methods that consider both generated code and issue context.

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