EveryAnswer — agentic threat model
EveryAnswer presents a low-to-moderate agentic risk posture due to its lack of autonomous action or tool execution capabilities. Its primary security risks center on data confidentiality, specifically the potential for prompt injection to leak sensitive ingested documents or bypass access controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.40 |
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 LLMs are not specified. Threats include prompt injection bypassing the 'anti-hallucination' guardrails, leading to mis-aligned or toxic outputs.
Highly relevant. Users upload documents, websites, and Q&As. Threats include data poisoning of the knowledge base (e.g., uploading malicious PDFs or compromised websites) and unauthorized data exfiltration via prompt extraction.
Not certain from the listing — the orchestration framework (e.g., LangChain, custom) is undisclosed. Threats include insecure integration of the document parser or RAG pipeline leading to denial of service or prompt injection.
Not certain from the listing — hosting details are omitted. Threats include container compromise or unauthorized access to the multi-user SaaS infrastructure hosting the 'Experts'.
Partially visible. Features 'anti-hallucination technology' and 'citation for transparency'. Threats include blind spots in detecting sophisticated prompt injections that bypass these citation checks.
Features a 'secure, multi-user interface' with 'access-controlled interactions'. Threats include broken object-level authorization (BOLA) allowing unauthorized users to access private 'Experts' or their underlying training data.
Not certain from the listing — no multi-agent collaboration or external marketplace is described. Threats are limited to isolated 'Expert' interactions rather than cascading A2A failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).