Kitchendary — agentic threat model

Not certain from the listing — likely utilizes a third-party commercial LLM (e.g., OpenAI or Anthropic) for the AI recipe generator and chat. Primary threats include prompt injection leading to bypassed dietary restrictions or generation of unsafe/toxic cooking instructions.

Not certain from the listing — ingests data from external URLs (TikTok, Instagram, YouTube) and stores user-generated recipe databases. Threats include data poisoning via malicious recipe imports, embedding inversion, or SSRF/exfiltration during the scraping process.

Not certain from the listing — likely uses a lightweight orchestration framework to parse scraped recipe data and format grocery lists. Threats include insecure tool integration where the URL parser handles malformed or malicious payloads.

Not certain from the listing — likely deployed on standard cloud infrastructure (AWS/GCP) supporting web and mobile clients. Threats include container compromise if the recipe-scraping microservice is not properly sandboxed from the main application database.

Not certain from the listing — no public details on LLM guardrails or monitoring. Threats include a lack of observability into prompt injection attempts or drift in the quality and safety of generated recipes.

Not certain from the listing — as a consumer-grade freemium app, it likely lacks formal enterprise compliance (e.g., SOC2). Threats include weak authorization controls in the collaborative family/partner sharing features, potentially allowing unauthorized access to shared calendars.

Not certain from the listing — the agent operates in isolation without multi-agent orchestration or marketplace integrations. Threats at this layer are currently negligible.