Diagnostics AI (PCR AI) — agentic threat model
Diagnostics AI presents a high-consequence risk profile due to its integration with clinical LIMS and diagnostic workflows, where data manipulation could lead to incorrect patient diagnoses, though this is heavily mitigated by strict regulatory compliance (CE-IVDR/MHRA) and deterministic QC rules.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.20 | |
| 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 — The specific foundation or machine learning models used to analyze qPCR amplification curves are not disclosed. Threats include adversarial manipulation of curve data to bypass detection or trigger false positives/negatives.
Processes highly sensitive clinical qPCR data and integrates directly with Laboratory Information Management Systems (LIMS). Risks include data poisoning of reference curves or unauthorized exfiltration of patient diagnostic data.
Orchestrates clinical workflows by applying Westgard and Levey-Jennings rules to automate quality control. Vulnerabilities here involve logic bypasses in QC enforcement or insecure tool execution when writing reports back to LIMS.
Not certain from the listing — The deployment architecture (cloud SaaS vs. on-premise laboratory network) is unspecified. Compromise of the hosting infrastructure could allow lateral movement into sensitive hospital or laboratory networks.
Enforces strict clinical validation and quality control standards with a stated >99.9% accuracy. However, blind spots in drift detection could lead to silent failures in curve analysis over time if reagent or instrument baselines shift.
Demonstrates strong regulatory alignment with CE-IVDR and MHRA compliance, indicating robust audit trails, traceability, and validation protocols required for clinical diagnostic environments.
Not certain from the listing — There is no indication of multi-agent collaboration or third-party agent marketplace integration; the system operates as a specialized, single-purpose automation pipeline.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).