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Qwen Chat — agentic threat model

7.1AIVSS 7.1 · High

Qwen Chat is a low-autonomy, web-based assistant whose primary security risks stem from processing untrusted user documents/images and rendering dynamic HTML/code artifacts, which could expose users to prompt injection or cross-site scripting (XSS) within the preview environment.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.1AARS uplift 1.01Factor sum 2.6/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.10
Goal-Driven Planning
0.20
Self-Modification
0.00
Dynamic Tool Use
0.30
Persistent Memory
0.20
Contextual Awareness
0.40
Dynamic Identity
0.00
Multi-Agent Interactions
0.10
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✓ mapped

Uses proprietary Qwen foundation models (e.g., Qwen2.5-Plus, Qwen2-VL-Max). Key threats include prompt injection, jailbreaks, and adversarial vision-language inputs via the image upload feature.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — processes uploaded documents and images, but the underlying data retention, vector storage, and privacy policies regarding user-submitted data are not specified.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — orchestrates model switching and artifact generation (HTML/SVG previews), but the specific orchestration framework and its vulnerability to tool-misuse or state-manipulation are undisclosed.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — hosted on Alibaba's web infrastructure. The sandboxing capabilities of the HTML preview environment are critical to prevent client-side execution attacks, but technical details are omitted.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — proprietary guardrails and input/output filtering are likely active to prevent toxic or misaligned outputs, but no specific observability or logging mechanisms are detailed.

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

Not certain from the listing — as a closed-source, free horizontal assistant, its compliance with international standards (like SOC2 or ISO 27001) or regional AI regulations is not documented in the listing.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — operates primarily as a single-user conversational interface with no explicit multi-agent collaboration or third-party agent marketplace integrations described.

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