[{"content":"I’m excited to share that I have completed all Honors program requirements after finishing my final Honors Advanced Studies course in Fall 2025, a full 1.5 years ahead of schedule. The program requirements I completed include the Honors First‑Year Experience, academic writing, a year of community service, foundational courses, two colloquia, an advanced studies course, quantitative coursework, an internship/research experience (Honors Immersed), and the year‑long Bachelor’s Essay.\nThe Honors program connected me with faculty mentors, opened early research and unique learning opportunities, and provided meaningful ways to give back to the campus community. In my Honors Immersed project I trained a model to identify malicious Python packages, and my Bachelor’s Essay produced a tool to help developers identify and visualize potential Python vulnerabilities in the software supply chain. Utilizing the research connections from these projects, I am now working to further develop and publish this work. These experiences strengthened my academic writing, quantitative analysis, project management, scientific communication, and teamwork skills.\nCompleting the Honors program early means I can now shift my focus to publishing my research, securing an internship, and working on career‑advancing personal projects.\n","permalink":"https://mcguire.one/posts/honors/","summary":"Successfully completed Honors course requirements, 1.5 years ahead of schedule!","title":"Honors Complete"},{"content":"Built AISAI to detect Python supply chain vulnerabilities by fusing static analysis (Bandit) with LLM-driven inspection (Ollama + LangChain). Used Dash-Cytoscape and AST to visualize dependency trees and code, benchmarked model performance, and surfaced risky packages for remediation.\n","permalink":"https://mcguire.one/projects/paper-project/","summary":"Researching static-analysis + LLM-assisted detection of Python supply chain vulnerabilities; benchmarking effectiveness across models.","title":"AISAI: Automated Identification of Software Supply Chain Vulnerability"},{"content":"I’ve been developing scripts for our cybersecurity club to streamline competition prep—automating host enumeration, log collection, and quick service health checks. These utilities reduce overhead during SECCDC/PCDC scenarios so we can focus on defense tactics and lessons.\n","permalink":"https://mcguire.one/posts/club-scripts/","summary":"Automating competition prep with small utilities and playbook scripts.","title":"Scripting for Our Cybersecurity Club"},{"content":"Trained a scikit-learn Random Forest model on extracted features from the Backstabber\u0026rsquo;s Knife Collection of malicious Python packages to detect malicious packages.\n","permalink":"https://mcguire.one/projects/python-malware-detector/","summary":"Dash-Cytoscape interface with LangChain, Ollama, and Bandit, paired with scikit-learn Random Forests to flag malicious Python code.","title":"Malicious Python Package Detector"}]