Solar — agentic threat model

Not certain from the listing — The specific foundation models used for code generation are not disclosed. Standard LLM threats like prompt injection could lead to the generation of vulnerable or malicious application code.

Not certain from the listing — No details are provided regarding how codebase context, schemas, or RAG data are stored. Risks include data exfiltration of proprietary code or database credentials.

The agent orchestrates full-stack app generation and deployment. Threats include tool misuse, where the agent might execute destructive commands or integrate insecure third-party APIs during the automated build process.

Because the agent deploys production apps with live data, infrastructure threats are critical. These include container compromise, privilege escalation on the hosting platform, and the exposure of deployment secrets or API keys.

Not certain from the listing — There is no mention of guardrails, runtime monitoring, or evaluation frameworks to detect if the agent is generating insecure code or performing unauthorized deployment actions.

Not certain from the listing — No details are provided regarding identity management, access control, or compliance standards (such as SOC2) for the platform or the deployed applications.

The listing highlights collaboration with 'AI agents' (plural). This introduces multi-agent trust abuse, where a compromised sub-agent could inject backdoors into the generated application without the primary agent or user detecting it.