Logical — agentic threat model
Logical presents a high-confidentiality risk profile due to its deep integration with desktop window context and cross-app data, though its local-first architecture and human-in-the-loop execution model significantly limit remote exfiltration and autonomous action risks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.90 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.60 |
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 local models or local API wrappers to maintain its 'privacy-first' local data promise. Primary threats include model tampering if local files are compromised, or prompt injection via on-screen text.
Builds a personal knowledge base and reads cross-app/window context. Threats include local data/knowledge-base poisoning (e.g., via malicious emails or documents read by the agent) and unauthorized local data exfiltration if another local process accesses its database.
Orchestrates context transfer across apps, detects to-dos, and drafts actions. Threats include indirect prompt injection where a malicious website or document in an open tab injects instructions to draft malicious emails or exfiltrate data via tool calling.
Runs as a desktop-native application locally on the user's OS. Threats include privilege escalation if the desktop client runs with high privileges, local sandbox escape, or exposure of local IPC/APIs used to capture window/tab context.
Not certain from the listing — no details on local logging, guardrails, or evaluation frameworks are provided. Gaps in local observability could allow silent prompt injection or data harvesting to go unnoticed.
Not certain from the listing — claims 'privacy-first' and 'local data' with no data sent to Logical servers, but lacks explicit details on compliance certifications (e.g., SOC2), local encryption standards, or enterprise access controls.
Not certain from the listing — designed as a single-user desktop copilot with no explicit multi-agent or marketplace interactions mentioned.
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.