AI Clothes Changer — agentic threat model
The AI Clothes Changer is a low-risk, single-purpose generative AI tool with minimal agentic autonomy, primarily posing privacy and content-generation risks rather than systemic agentic threats.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.60 | |
| 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.
Not certain from the listing — likely uses diffusion models (e.g., Stable Diffusion) for image-to-image and text-to-image generation. Threats include adversarial inputs to bypass safety filters (NSFW generation) and model stealing.
Not certain from the listing — processes user-uploaded photos and garment images. Threats include unauthorized access to user photos, data leakage, and poisoning of any downstream fine-tuning datasets.
Not certain from the listing — likely does not use a complex agent framework, operating instead as a simple pipeline. Threats include insecure handling of user prompts and image processing parameters.
Not certain from the listing — hosted as a closed-source web application. Threats include server-side request forgery (SSRF) via image URLs, denial of service via resource-intensive GPU rendering, and insecure storage of uploaded images.
Not certain from the listing — no mention of monitoring or guardrails. Threats include lack of detection for adversarial prompt injections or generation of inappropriate/NSFW content.
Not certain from the listing — closed-source freemium model with no explicit compliance certifications (e.g., GDPR, SOC2) mentioned. Threats include lack of user data deletion controls and privacy policy violations regarding uploaded biometric/facial data.
No multi-agent or ecosystem interactions are described; this is a standalone vertical application, making ecosystem threats like cascading agent failures or rogue agent interactions highly unlikely.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).