🗓️ 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🤖
📱 iOS (SwiftUI)
- Worked through the SwiftUI tutorial and completed "Create an Algorithm for Badges" and "Add inclusive features"
🌐 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
🔐 Security (TryHackMe)
- Completed Securing AI Systems room (part of the AI Security Learning Path) on TryHackMe.
💡 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
ScrollViewbehavior. - 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 aScrollView. - Learned that the
dynamicTypeSizemodifier 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.
🌐 Web Development Learning
- Reviewed several useful functions in Figma
- Learned the importance of focusing on the overall page layout before designing detailed UI components
🔐 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.
🚀 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.
🌈 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.
Top comments (2)
Hello Good friend
Interesting take on iOS and web dev this week! I noticed you mentioned secure enclaves — have you explored how they interact with GPU-based confidential computing, especially with frameworks like VoltageGPU for encrypted ML inference? Just curious how that fits into the broader security pipeline.