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Modassembly — agentic threat model

8.9AIVSS 8.9 · High

Modassembly presents a high-risk profile due to its deep integration into critical business communication channels (Slack, WhatsApp, Email) and backoffice/revenue operations. A compromise of these agents could lead to automated financial fraud, data exfiltration, or highly convincing social engineering attacks.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.9Factor sum 5.7/10Threat ×1.05Mitigation ×0.95
Autonomy of Action
0.80
Goal-Driven Planning
0.70
Self-Modification
0.10
Dynamic Tool Use
0.80
Persistent Memory
0.50
Contextual Awareness
0.70
Dynamic Identity
0.60
Multi-Agent Interactions
0.30
Non-Determinism
0.60
Opacity & Reflexivity
0.60

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 — The specific foundation models (e.g., GPT-4, Claude) used by Modassembly are not disclosed. Standard threats like prompt injection, model reprogramming, or adversarial inputs could compromise the downstream backoffice and communication workflows.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — While Modassembly integrates with existing company tools and data, the exact data operations, vector databases, or RAG architectures are unspecified. This leaves potential risks of data exfiltration or knowledge-base poisoning unverified.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — The underlying agent orchestration framework is proprietary. However, because it automates backoffice workflows and integrates with communication tools (Slack, WhatsApp, Email), insecure tool integration and unauthorized tool execution are high-priority risks.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — The hosting environment, sandboxing mechanisms for tool execution, and secrets management for third-party integrations (Slack, WhatsApp, etc.) are not detailed, though the platform claims to be secure and scalable.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — Although Modassembly emphasizes a 'tested' approach and consultation-led onboarding, specific runtime guardrails, drift detection, or continuous evaluation mechanisms are not explicitly described.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — The platform claims a 'secure' approach, but specific compliance standards (e.g., SOC 2, GDPR, HIPAA) or identity and access management (IAM) controls for agent operations are not detailed.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — The platform builds 'custom AI agents' to automate workflows, but it is unclear if these agents interact in a multi-agent ecosystem or if there is a shared marketplace, which could introduce cascading trust-abuse risks.

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.