Inboundr — agentic threat model
Inboundr presents a moderate-to-high security risk primarily due to its ingestion of sensitive internal communications (Slack, meetings, podcasts) and external research capabilities, which could be targeted for data exfiltration or prompt injection.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| 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 — likely relies on third-party foundation models (e.g., OpenAI, Anthropic) for content generation and summarization, exposing it to prompt injection and model-based data leakage.
Ingests highly sensitive raw data from Slack, meetings, and podcasts. This creates a high-value target for data exfiltration, unauthorized access, and knowledge-base poisoning if raw transcripts are stored insecurely.
Orchestrates research tools and content drafting. Insecure tool integration could allow an attacker to manipulate the research tool (e.g., via SSRF or prompt injection from ingested Slack messages) to retrieve malicious content.
Not certain from the listing — closed-source SaaS hosting. Risks include insecure storage of OAuth tokens for Slack and LinkedIn, and potential lack of robust tenant isolation for stored conversational data.
Not certain from the listing — no details on output guardrails or observability. Lack of monitoring could allow hallucinated or brand-damaging content to be drafted without detection.
Not certain from the listing — handling of corporate communications (Slack, meetings) requires strict compliance (GDPR, SOC2) and access controls, which are not detailed in the public directory.
Not certain from the listing — operates primarily as a standalone assistant interacting with APIs (Slack, LinkedIn) rather than a multi-agent ecosystem.
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.