Weekly Dev Log 2026-W07

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🗓️ 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

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📱 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.