🗓️ This Week

• Completed two more sections of the SwiftUI tutorial 🦾 As I continue working through the tutorial, I can feel my understanding of SwiftUI fundamentals becoming more solid🔥
• It was my first time posting a standalone article about reverse engineering📝 If you're interested, feel free to check it out 👇
• I started creating UI designs for my future portfolio website in Figma. I was able to roughly sketch out the overall structure of the site, but I also realized how difficult it is to create modern and stylish UI designs. (It really made me realize I don’t have much design sense yet 😂💦)
• While struggling with the design process, I came across several articles about Figma MCP. That made me interested in exploring how generative AI could help with UI design ideas, so I decided to start researching Figma MCP further.
• Completed Securing AI Systems room from the AI Security Learning Path on TryHackMe this week🤖

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📱 iOS (SwiftUI)

• Worked through the SwiftUI tutorial and completed "Create an Algorithm for Badges" and "Add inclusive features"

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🌐 Web Development

• Posted my weekly dev log on Dev.to and a standalone article about my first attempt at reverse engineering 📝
• Created rough portfolio website UI layouts in Figma
• Used shadcn/ui component library design templates in Figma
• Started learning UI design in Figma using community resources

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🔐 Security (TryHackMe)

• Completed Securing AI Systems room (part of the AI Security Learning Path) on TryHackMe.

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💡 Key Takeaways

📱 SwiftUI Learning

Add inclusive features

• Learned that SwiftUI automatically adapts UI elements for Light and Dark Mode by default.
• Learned how to preview and compare Light and Dark Mode layouts in the Xcode canvas.
• Understood that system-provided semantic styles help SwiftUI automatically adjust UI appearance.
• Learned that SwiftUI uses view modifiers to customize ScrollView behavior.
• Understood that .scrollBounceBehavior(.basedOnSize) only enables bouncing when the content is larger than the visible area.
• Learned that .defaultScrollAnchor(.center, for: .alignment) centers smaller content inside a ScrollView.
• Learned that the dynamicTypeSize modifier can be applied to any SwiftUI view.
• Learned how AttributedString(localized:) supports localization-aware text in SwiftUI.
• Understood that (inflect: true) automatically changes words like “Day” and “Days” based on the number value.
• Learned that SwiftUI can apply different font styles to specific parts of an AttributedString.
• Realized that Apple provides built-in grammar inflection support for more natural localized UI text.

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🌐 Web Development Learning

• Reviewed several useful functions in Figma
• Learned the importance of focusing on the overall page layout before designing detailed UI components

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🔐 TryHackMe Learning

Securing AI Systems

Task 2 Anatomy of an AI System

• Traditional apps use deterministic logic, while AI systems rely on probabilistic model inference.
• AI systems accept free-form natural language, making input validation much harder.
• Prompt Construction combines the system prompt, user input, and retrieved context before sending data to the LLM.
• RAG allows LLMs to retrieve external knowledge from a vector store or other data sources.
• A vector store contains embedded representations of internal documentation for retrieval-augmented generation (RAG)
• Trust boundaries are points where data moves between different security contexts.
• Major trust boundaries include user-to-system, system-to-LLM, LLM-to-tools, and system-to-user.
• LLM-to-tools is especially sensitive because model output can trigger real actions.
• Tool layers may execute database queries, API calls, or file operations on behalf of the LLM.
• Security controls are needed at every boundary to reduce prompt injection and data leakage risks.

Task3 The AI Attack Surface

• Studied the OWASP LLM Top 10 and how major risks affect AI-integrated systems.
• Learned that MITRE ATLAS documents adversary tactics and exploitation techniques for AI systems.
• Studied how the NIST AI RMF approaches AI security from a governance and risk management perspective.
• Learned the difference between OWASP (vulnerabilities), ATLAS (attack techniques), and NIST AI RMF (risk governance).

Task4 System-Level Threats

• Studied how LLM10 Unbounded Consumption can cause resource exhaustion and extreme cost increases through excessive requests.
• Learned that system prompts may leak internal rules, tool information, and architecture details if exposed.(LLM07).
• Understood that LLM output must never be trusted as safe input for downstream systems.(LLM05)
• Learned that Excessive Agency occurs when AI systems are given unnecessary permissions, tools, or autonomy.(LLM06)
• Learned that users may unintentionally leak sensitive information when using AI systems.(LLM02)
• Studied how the OWASP LLM risks relate to the CIA triad across confidentiality, integrity, and availability.

Task 5 Secure Design Patterns

• Learned that security controls are most effective when applied during the design stage of AI systems.
• Learned that layered controls reduce the risk of end-to-end attack success.
• Understood the importance of least privilege for AI tools, API tokens, and database access.
• Learned that write operations should require human approval before execution.
• Studied how input and output validation reduce prompt injection and downstream injection risks.
• Learned that MLSecOps integrates security throughout the AI and machine learning lifecycle.

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🚀 Next Week

• Complete the badge algorithm in the SwiftUI tutorial.
• Continue posting small articles on Dev.to.
• Explore Figma MCP and experiment with generating UI design ideas using AI.
• Continue working on the AI Security Learning Path.

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🌈 Goals for This Year

📱 iOS (SwiftUI)

• Build a solid foundation in SwiftUI and create at least one iOS app.

🌐 Web Development

• Continue posting learning logs on Dev.to and eventually turn them into a portfolio site using React Router v7.

🔐 Security (TryHackMe)

• Continue learning cybersecurity on TryHackMe.