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← Chyo

Chyo — agentic threat model

7.7AIVSS 7.7 · High

Chyo is a customer-facing AI agent platform with moderate risk, primarily driven by its 'automatic learning' capabilities from multiple sources which expose it to data poisoning, and its multi-agent creation features which increase the internal attack surface.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.5AARS uplift 1.58Factor sum 4.3/10Threat ×1.05Mitigation ×0.95
Autonomy of Action
0.50
Goal-Driven Planning
0.30
Self-Modification
0.20
Dynamic Tool Use
0.40
Persistent Memory
0.40
Contextual Awareness
0.50
Dynamic Identity
0.10
Multi-Agent Interactions
0.60
Non-Determinism
0.60
Opacity & Reflexivity
0.70

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 underlying foundation models are not specified. As a closed-source customer service tool, it likely relies on commercial LLMs, making it susceptible to prompt injection, model alignment bypasses, and indirect prompt injection via customer inputs.

L2 · Data Operations✓ mapped

The platform features 'Automatic Learning from multiple sources' and 'Fine tuning of FAQ'. This introduces a high risk of data and knowledge-base poisoning if malicious or untrusted external sources are ingested, potentially leading to the generation of toxic, inaccurate, or malicious customer responses.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — The specific orchestration framework is proprietary. However, the system manages state for product recommendations, multi-currency, and human handover, indicating potential risks around insecure tool integration and state manipulation during active sessions.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — No deployment, hosting, or sandboxing details are provided. As a closed-source SaaS, standard web application vulnerabilities and tenant isolation risks apply.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — While 'Handover to human' is supported as a fallback mechanism, the listing does not detail active guardrails, real-time anomaly detection, or automated evaluation metrics to prevent drift or offensive outputs.

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

Not certain from the listing — No compliance certifications (e.g., SOC2, ISO 27001) or specific data privacy controls (e.g., GDPR/CCPA masking for customer chats) are mentioned in the public directory listing.

L7 · Agent Ecosystem✓ mapped

The platform allows users to 'Create multiple agents'. This introduces multi-agent ecosystem risks, such as cross-agent trust abuse, cascading failures if one agent is compromised, and authorization boundary confusion between different configured agents.

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