AI & Technical question
How would you work with engineering to build a threat model for Lovable’s AI software creation platform? Walk through the components you would analyze first, the abuse or failure modes you would prioritize, and how the output of that threat model would change the roadmap.
- Lovable
- AI & Technical
- Hard
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What this question tests
Whether you can partner with engineering to build a practical threat model for an AI code-generation platform and translate it into roadmap decisions.
How to approach it
- Start with the components most exposed to risk: the code-generation model itself, the runtime environment apps run in, generated app data storage, and the publishing or deployment pipeline.
- Prioritize abuse and failure modes by likelihood and impact: commonly generated insecure patterns (hardcoded secrets, missing auth checks) first, since they are frequent and directly exploitable, before rarer adversarial attack vectors.
- Include prompt injection and malicious use of the platform itself (someone using Lovable to build a malicious app) as a distinct category from accidental insecure code, since the mitigation differs.
- Work with engineering to map each identified risk to a concrete control: static analysis on generated code, secure defaults in scaffolding, and runtime sandboxing for generated apps.
- Rank the threat model output by severity and prevalence, and translate the top items directly into roadmap entries with clear ownership, rather than leaving the threat model as a document nobody acts on.
- Revisit the threat model on a recurring cadence, since new generation capabilities or attack patterns will surface new risks over time.
What a strong answer includes
- Separates accidental insecure code from deliberate malicious use as two distinct threat categories requiring different controls.
- Prioritizes commonly generated insecure patterns first because they are both frequent and high-impact, a genuine risk-based reasoning.
- Shows concrete translation from threat to control, for example secure defaults in scaffolding, not just naming abstract risks.
- Proposes a recurring cadence for revisiting the threat model, recognizing the risk landscape shifts as the platform's capabilities grow.
Common mistakes
- Listing generic security concerns without prioritizing by likelihood and impact.
- Not distinguishing accidental insecure code generation from deliberate platform misuse.
- Treating the threat model as a one-time document rather than a living input to the roadmap.
Likely follow-up questions
- How would you prioritize between a high-severity but rare risk and a lower-severity but very common one?
- What would you do if a mitigation for one risk category degrades the developer experience significantly?
- How would you know the threat model is actually complete enough to act on?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Making AI Product Decisions
- Chapter 8: Designing Evals for AI Products
- Chapter 6: Designing AI Agents and Tooling