Security patches are essential for enhancing the stability and robustness of projects in the open-source software community. While vulnerabilities are officially expected to be patched before being disclosed, patching vulnerabilities is complicated and remains a struggle for many organizations. To patch vulnerabilities, security practitioners typically track vulnerable issue reports (IRs), and analyze their relevant insecure code to generate potential patches. However, the relevant insecure code may not be explicitly specified and practitioners cannot track the insecure code in the repositories, thus limiting their ability to generate patches. In such cases, providing examples of insecure code and the corresponding patches would benefit the security developers to better locate and resolve the actual insecure code. In this paper, we propose PatUntrack, an automated approach to generating patch examples from IRs without tracked insecure code. PatUntrack utilizes auto-prompting to optimize the Large Language Model (LLM) to make it applicable for analyzing the vulnerabilities described in IRs and generating appropriate patch examples. Specifically, it first generates the completed description of the Vulnerability-Triggering Path (VTP) from vulnerable IRs. Then, it corrects potential hallucinations in the VTP description with external golden knowledge. Finally, it generates Top-K pairs of Insecure Code and Patch Example based on the corrected VTP description. To evaluate the performance of PatUntrack, we conducted experiments on 5,465 vulnerable IRs. The experimental results show that PatUntrack can obtain the highest performance and improve the traditional LLM baselines by +17.7% (MatchFix) and +14.6% (Fix@10) on average in patch example generation. Furthermore, PatUntrack was applied to generate patch examples for 76 newly disclosed vulnerable IRs. 27 out of 37 replies from the authors of these IRs confirmed the usefulness of the patch examples generated by PatUntrack, indicating that they can benefit from these examples for patching the vulnerabilities.