Lila Sciences — agentic threat model
Lila Sciences presents an exceptionally high-risk profile due to its integration of generative AI with physical lab robotics and autonomous experimentation in life sciences and chemistry. A compromise could lead to severe physical safety hazards, including the unauthorized synthesis of hazardous materials or biological agents, alongside massive intellectual property theft.
OWASP AIVSS score rationale
| Autonomy of Action | 0.90 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.80 |
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 — uses generative AI for scientific discovery, but the specific foundation models are not disclosed. Threats include adversarial prompt injection causing unsafe chemical/biological designs, and model stealing of highly valuable proprietary scientific models.
Not certain from the listing — likely utilizes proprietary scientific databases, RAG, and experimental results. Threats include data poisoning of chemical/biological properties and IP theft of novel therapeutics or materials.
The agent designs, runs, observes, and refines experiments autonomously. Threats include tool misuse where the agent executes dangerous physical/chemical protocols or bypasses safety constraints in experiment design.
Integrates directly with physical lab robotics and automation infrastructure. Threats include compromise of physical lab equipment, unauthorized remote control of robotics, and lateral movement from the digital platform to physical lab networks.
Not certain from the listing — while it 'observes and refines' experiments, the specific security logging, guardrails, and anomaly detection for physical/chemical safety are not detailed.
Not certain from the listing — no specific compliance certifications (e.g., ISO, SOC2, or biosecurity compliance) are mentioned, though operating in life sciences and robotics demands strict regulatory alignment.
Not certain from the listing — it is unclear if multiple specialized agents coordinate or if there is an external marketplace, though the scale suggests a complex multi-agent or multi-system orchestration.
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.