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Holiwise — agentic threat model

8.8AIVSS 8.8 · High

Holiwise is a collaborative travel planning agent with moderate risk, primarily driven by its integration with booking APIs, handling of user PII, and multi-user trip coordination which could be targeted for unauthorized data access or booking manipulation.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 7.5AARS uplift 1.25Factor sum 5.0/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.60
Goal-Driven Planning
0.80
Self-Modification
0.10
Dynamic Tool Use
0.60
Persistent Memory
0.70
Contextual Awareness
0.70
Dynamic Identity
0.20
Multi-Agent Interactions
0.20
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — likely utilizes commercial LLMs for generating tailored itineraries and recommendations. Threats include prompt injection that could manipulate travel recommendations or redirect users to malicious booking links.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — processes user preferences, trip details, and real-time travel data (flights, hotels). Threats include exfiltration of sensitive travel itineraries and PII, or poisoning of the recommendation data store.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — orchestrates multi-step planning and booking tool execution. Threats include insecure tool integration with external travel APIs and manipulation of the planning logic via indirect prompt injection.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — deployed as a closed-source web application. Threats include standard web application vulnerabilities, insecure API endpoints, and potential exposure of API keys used to query travel partners.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — no public details on guardrails or monitoring. Threats include a lack of observability into LLM outputs, allowing hallucinated or malicious travel recommendations to reach the end user undetected.

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

Not certain from the listing — manages collaborative planning among friends and family. Threats include Broken Object Level Authorization (BOLA) allowing unauthorized users to view or modify shared trip itineraries, and potential PCI-DSS compliance gaps if handling booking payments.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — operates primarily as a single-agent platform interacting with external APIs. Threats include cascading failures or trust abuse if third-party flight/hotel booking systems are compromised.

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.