ImageLayered — agentic threat model
ImageLayered is a low-risk, single-shot image processing and generation utility with minimal agentic capabilities, posing primary risks around image upload vulnerabilities and potential NSFW generation.
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 | 0.40 |
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.
Uses Qwen-Image-Layered and hosted GPT Image 2.5. Primary threats include adversarial image inputs designed to break decomposition logic, and prompt injection in the text-to-image generator.
Processes user-uploaded images up to 5MB. No persistent vector store or RAG. Threat of data poisoning is low, but malicious image payloads could exploit the preprocessing pipeline.
No complex agent framework, planning, or memory is utilized. The tool operates as a single-shot pipeline, minimizing framework-level threats.
Not certain from the listing — standard web hosting is assumed. Potential threats include container compromise or denial of service via image processing library vulnerabilities during upload handling.
Not certain from the listing — no mention of guardrails or output filtering. Threats include generation of inappropriate or policy-violating content via the text-to-image generator.
Implements standard account authentication and credit-based billing. No user data is used for training beyond the active request, reducing privacy compliance risks.
No multi-agent or marketplace interactions are supported, eliminating ecosystem-level threats.
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 — every score is re-derived by the same automated method as an agent's public evidence changes.