🗓️ This Week

• 
  Completed the SwiftUI app development tutorial and tested the app I built on a real iPhone🦾


• Learned the overall flow of building an iOS app with SwiftUI.


• Organized my app ideas in Notion because I want to start building a real app next week🤔 I also realized that starting with a small app is important at this stage💡


• Worked on the UI design for my portfolio site using the official shadcn/ui Figma template. I want to finish one design direction next week and then move on to implementation.


• Worked on the AI Threat Modelling room from the AI Security Learning Path on TryHackMe this week🤖

📱 iOS (SwiftUI)

• Completed the SwiftUI app development tutorial.


• Learned how to run an app on a real iPhone by connecting it to my computer, without joining the Apple Developer Program yet.

🌐 Web Development

• Posted my weekly dev log on Dev.to📝


• Worked on my portfolio site UI design using the official shadcn/ui Figma template.

🔐 Security (TryHackMe)

• Worked on the AI Threat Modelling room (part of the AI Security Learning Path) on TryHackMe.

💡 Key Takeaways

📱 SwiftUI Learning

🔬 What I learned from testing an app on a real iPhone

• I need to connect my iPhone, choose it as the run destination, and configure Signing & Capabilities before running the app.


• A unique Bundle Identifier is needed to identify the app.


• Xcode uses my personal Apple Account to sign the app for testing on my iPhone.


• I need to enable Developer Mode and trust the developer profile on the iPhone before launching the app.


• Preview data and real app data are handled differently in SwiftUI/SwiftData.


• In previews, sampleContainer is used, so includeSampleMoments is passed as true.


• The diary entries I create on the real iPhone are saved in the app’s local storage on the device.


• The data is not automatically saved to my Apple Account or iCloud.

🦄 What I learned from organizing ideas for my first iOS app

• As a beginner developer, I should not try to build a large, market-ready app from the beginning.


• Large apps usually include many hidden complexities, such as data management, user experience design, error handling, security, performance, and long-term maintenance.


• Since I have a full-time job, my development time is limited, so starting too big would make it harder to keep making steady progress.


• Starting with a small app helps me focus on one or two core ideas and understand each part more deeply.


• Building something small first also makes it easier to finish, test, and improve the app step by step.


• I realized that the goal at this stage is not to compete with polished apps on the market, but to build practical experience by completing small projects.


• This approach should help me stay motivated while gradually improving my skills in real app development.

🌐 Web Development Learning

• Learned more about how to create UI designs in Figma and how to design screens using components.


• Practiced building a consistent portfolio site design based on a design system instead of creating each part separately.

🔐 TryHackMe Learning

AI Threat Modelling

Task2 AI-Specific Assets and Attack Surfaces

• AI systems add new assets such as training data, model weights, embedding vectors, system prompts, feature stores, and model registries.


• I learned that poisoned training data can corrupt a model’s behaviour at the source.


• I learned that model weights are especially valuable because they define what the model has learned.


• If model weights are stolen, an attacker may obtain a functional copy of the AI system.


• I learned that system prompts can reveal the model’s behaviour, constraints, business logic, and security guardrails.


• Embedding vectors are important in systems such as RAG pipelines, recommendation engines, and fraud detection systems.


• Manipulating embeddings can change what information the model sees at query time.


• Feature stores are important because they provide preprocessed data used as real-time model inputs.


• Tampering with feature stores can affect model decisions without changing the model itself.


• A compromised model registry can allow an attacker to replace a legitimate model with a backdoored one.


• AI threat modelling needs to consider not only traditional application risks, but also AI-specific assets, behaviours, and failure modes.

Task3 Data Supply Chain and STRIDE's Gaps

• I learned that AI systems have a data supply chain in addition to a traditional software supply chain.


• Each stage of the AI data supply chain can become a point of compromise.


• Data poisoning can start from data collection, labelling, or other early pipeline stages.


• If poisoned data reaches training, the damage can become embedded in the model’s weights.


• A compromised model registry can allow attackers to replace a validated model with a backdoored one.


• A backdoored model may look normal until it receives specific trigger inputs in production.


• LLM-based systems can introduce additional injection points through retrieval pipelines during inference.


• AI threats such as data poisoning, adversarial manipulation, tool misuse, and model theft do not always fit neatly into traditional STRIDE categories.


• The key takeaway is that AI threat modelling must cover the full data supply chain and AI-specific failure modes.

Task4 Adapting STRIDE for AI Systems

• I learned that STRIDE does not need to be replaced, but it needs to be adapted for AI systems.


• In AI systems, familiar STRIDE categories can appear in new forms, such as data source impersonation, data poisoning, model extraction, and inference cost exploitation.


• AI decisions are harder to audit because the model version, input features, prompts, and retrieved context may not be fully recorded.


• Tool-enabled AI systems can make privilege risks broader, because a jailbroken model may misuse databases, email systems, or other connected tools.


• The key takeaway is that STRIDE is still a useful starting point, but some AI risks require additional AI-specific frameworks such as MITRE ATLAS.

Task5 MITRE ATLAS: The AI Threat Technique Catalogue

• I learned that MITRE ATLAS is an AI-focused knowledge base for adversary tactics and techniques against AI and ML systems.


• ATLAS complements STRIDE by adding AI-specific technical detail, documented techniques, mitigations, and real-world case studies.


• I learned key ATLAS techniques such as Data Poisoning, Model Extraction, Evade ML Model, LLM Prompt Injection, and Backdoor ML Model.


• ATLAS helps turn broad STRIDE findings into specific, actionable threat findings with technique IDs and defensive guidance.


• Real-world cases such as ShadowRay and Morris II show that AI threats are not only theoretical, but can affect real AI infrastructure and RAG-based systems.

🚀 Next Week

• Start building a small iOS app based on the app ideas I organized this week.


• Finish one portfolio site UI design in Figma and start implementing it in code.


• Continue posting small articles on Dev.to.


• 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.