SpinachAI — agentic threat model

Not certain from the listing — The specific foundation models used are undisclosed. The primary threat is indirect prompt injection, where an adversary speaks or writes malicious instructions during a meeting to manipulate the model into generating unauthorized tickets or exfiltrating data.

Not certain from the listing — Details on transcript storage, vector databases, and RAG pipelines are omitted. The agent processes highly sensitive corporate meeting audio and text, making data exfiltration and unauthorized access to meeting history major risks.

The agent framework orchestrates meeting joining, note-taking, and ticket generation. A key threat is insecure tool integration, where parsed action items are translated into Jira tickets or Slack messages without sufficient validation, potentially executing unauthorized API actions.

Not certain from the listing — The hosting infrastructure and sandboxing of the meeting bot are not detailed. Threats include bot hijacking, interception of live audio streams, and compromise of the hosting environment to pivot into corporate networks.

Not certain from the listing — There is no mention of real-time guardrails or observability tools. Gaps in monitoring could allow adversarial prompt injections or hallucinated action items to go unnoticed until they are committed to external tools.

Not certain from the listing — Compliance certifications (e.g., SOC2, GDPR) and OAuth token management policies are not specified. Risks include over-privileged OAuth tokens granting the agent excessive write access to Jira, Slack, or code repositories.

Not certain from the listing — The extent of multi-agent coordination is unclear. However, interacting within collaborative ecosystems like Slack introduces risks of cascading failures or trust abuse if another compromised bot triggers SpinachAI.