Presentations.AI — agentic threat model
Presentations.AI exhibits low agentic risk due to its human-in-the-loop design focused on slide generation, though it presents moderate data confidentiality risks if users upload sensitive corporate data for visualization.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.20 |
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 utilizes commercial LLMs for text generation and layout planning. Primary threats include prompt injection leading to inappropriate content generation or model-based data leakage.
Not certain from the listing — processes user-uploaded data and metrics for data visualization. Threats include unauthorized access to sensitive corporate presentation data and potential data leakage if inputs are used for model fine-tuning.
Not certain from the listing — uses orchestration to map text prompts to slide structures and design templates. Threats include insecure tool integration where the layout engine could be manipulated via prompt injection.
Not certain from the listing — hosted as a closed-source SaaS platform. Threats include standard web application vulnerabilities, container escape, or unauthorized access to presentation databases.
Not certain from the listing — likely relies on standard application logging. Gaps in LLM-specific observability could allow prompt injection or policy violations to go undetected.
Not certain from the listing — closed-source freemium model. Lacks explicit mention of enterprise compliance standards (e.g., SOC2, GDPR), posing risks for sensitive corporate data.
Not certain from the listing — primarily a single-agent/user-facing tool with real-time human collaboration. No active multi-agent marketplace or autonomous A2A interactions described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).