UndressHer — agentic threat model
UndressHer presents minimal agentic risk due to its static, single-purpose image-processing nature, but poses extreme privacy, ethical, and reputational risks due to the generation of non-consensual synthetic nude imagery and the potential exposure of highly sensitive user-uploaded photos.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.10 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.50 | |
| 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.
Uses advanced generative image models (likely diffusion-based or GANs) to perform image-to-image translation. Primary threats include adversarial inputs designed to bypass safety filters, model stealing of proprietary fine-tuned weights, and output misalignment generating illegal or highly harmful content.
Not certain from the listing — The listing does not specify how user-uploaded photos are stored, processed, or deleted. There is a high risk of data exfiltration of sensitive user uploads, lack of data lineage, and potential privacy violations if uploaded images are used to train or fine-tune models without explicit consent.
Not certain from the listing — The application appears to function as a standard web wrapper around an image generation pipeline rather than utilizing an autonomous agent framework. There is no evidence of tool-calling, planning, or memory orchestration.
Not certain from the listing — No details are provided regarding hosting, sandboxing, or API security. Given the GPU-intensive nature of image generation, infrastructure compromise could lead to high resource theft (GPU mining) or unauthorized access to the image generation backend.
Not certain from the listing — There is no mention of input validation, content moderation guardrails (to prevent processing images of minors), or logging mechanisms to detect abusive patterns or policy violations.
Critical compliance and ethical exposure. The tool lacks visible consent verification mechanisms for uploaded subjects, creating severe legal risks regarding non-consensual deepfakes, copyright infringement, and violation of regional privacy regulations (e.g., GDPR, EU AI Act provisions on synthetic media).
Not certain from the listing — The tool operates as a standalone horizontal application with no described multi-agent interactions, marketplace integrations, or agent-to-agent communication protocols.
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.