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Futurwise — agentic threat model

7.2AIVSS 7.2 · High

Futurwise presents a low-to-moderate agentic risk profile, primarily acting as an ingestion and summarization tool. Its main security vectors involve indirect prompt injection via untrusted user-provided content (PDFs, URLs) and the potential exposure of its 'private library' data store.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.5AARS uplift 0.74Factor sum 2.1/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.20
Goal-Driven Planning
0.10
Self-Modification
0.00
Dynamic Tool Use
0.30
Persistent Memory
0.40
Contextual Awareness
0.30
Dynamic Identity
0.00
Multi-Agent Interactions
0.00
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — likely utilizes commercial LLMs for multi-lingual summarization. The primary threat is indirect prompt injection embedded within user-submitted PDFs, YouTube transcripts, or web articles, which could hijack the model's instructions.

L2 · Data Operations✓ mapped

Ingests diverse external data formats (PDFs, URLs, YouTube, podcasts) and stores them in a 'private library'. This introduces risks of data poisoning, malicious document parsing exploits, and unauthorized access to the user's stored knowledge base.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — likely uses a basic orchestration framework to handle document chunking and summarization pipelines. Threats include insecure tool integration, specifically SSRF (Server-Side Request Forgery) when fetching external URLs or YouTube metadata.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — likely hosted on standard cloud infrastructure. Threats include container compromise or privilege escalation if the PDF/podcast parsing microservices are not properly sandboxed from the main application database.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — no mention of content filtering or guardrails. The system may have blind spots regarding malicious payloads or exfiltration attempts hidden in summarized text.

L6 · Security & Compliance (cross-cutting)✓ mapped

Claims to offer a 'securely stored' private library, but lacks explicit compliance certifications (e.g., SOC2, GDPR). The primary threat is Broken Object Level Authorization (BOLA) allowing users to access other users' private libraries.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — the agent operates as a standalone productivity tool with no described multi-agent or marketplace ecosystem interactions.

MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).