Superluminal — agentic threat model
Superluminal acts as an embedded data copilot, presenting moderate-to-high risk due to its direct access to sensitive product databases and dashboards, where prompt injection could lead to unauthorized data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — The underlying foundation models are not specified, but they are highly susceptible to prompt injection and indirect prompt injection via the dashboard data they ingest.
As a data dashboard copilot, the agent directly queries and processes structured product data, making it a high-value target for data exfiltration, unauthorized data access, and embedding inversion.
Not certain from the listing — The orchestration framework is closed source, but risks include insecure tool integration if the agent dynamically generates and executes database queries (SQL injection via LLM) or code.
Not certain from the listing — Integration requires 'just a few lines of code', suggesting a client-side or simple API-based deployment where API key exposure and lack of execution sandboxing are primary threats.
Not certain from the listing — No built-in evaluation, guardrails, or observability features are mentioned, creating potential blind spots regarding what data the agent accesses and presents to users.
Not certain from the listing — There is no mention of compliance certifications (e.g., SOC2) or row-level security enforcement, raising the risk of privilege escalation where users access unauthorized data via the copilot.
Not certain from the listing — The agent appears to operate as a single-agent copilot within a dashboard, with no explicit multi-agent or ecosystem interactions described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.