CookPal AI — agentic threat model

Not certain from the listing — likely utilizes a commercial or open-source LLM to parse and format unstructured social media posts. The primary threat is indirect prompt injection embedded within imported recipe text, which could manipulate the model's formatting behavior or voice guidance output.

Not certain from the listing — maintains a database of user-saved recipes and parsed content. Risks include data exfiltration of private collections or database poisoning if malicious recipe payloads are stored and subsequently rendered to users.

Not certain from the listing — orchestrates URL scraping and voice guidance. Vulnerabilities could arise from insecure tool integration during the scraping of external social media platforms, or from voice command parsers being tricked by injected instructions.

Not certain from the listing — deployed as a closed-source freemium service. Standard web/mobile application security risks apply, such as insecure API endpoints for recipe synchronization and lack of sandboxing for the scraping components.

Not certain from the listing — no mention of guardrails, content filtering, or monitoring. A lack of input validation could allow offensive, malicious, or physically dangerous instructions (e.g., incorrect cooking temperatures) to be read aloud via voice guidance.

Not certain from the listing — likely relies on standard OAuth or basic authentication for user accounts. There are no indications of advanced compliance frameworks, data deletion guarantees, or rigorous privacy controls for user-submitted data.

The agent operates as a standalone personal assistant with no described multi-agent interactions, marketplace integrations, or collaborative agent-to-agent ecosystems.