Patronus AI — agentic threat model
Patronus AI acts as an evaluation and analysis platform for AI agents, meaning its primary risk lies in its role as a security gatekeeper where compromised evaluation logic could allow insecure or malicious agents to be deployed.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.60 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.50 |
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 — Patronus AI evaluates other AI agents, but its own underlying foundation models are not specified. General threats include adversarial examples or model stealing if its proprietary evaluation models are targeted.
Not certain from the listing — The data pipelines used for evaluation datasets are unspecified. General threats include evaluation data poisoning or leakage of proprietary test cases.
Not certain from the listing — While it analyzes complex AI agents, its own internal orchestration framework is not detailed. General threats include insecure tool integration if it dynamically executes agent code during evaluation.
Not certain from the listing — Deployment details are absent. General threats include container compromise if evaluating untrusted agent code without strict sandboxing.
As an evaluation and analysis platform, L5 is highly relevant. Key threats include evaluation gaming, blind spots in automated guardrails, and evasion of its detection mechanisms by sophisticated adversarial agents.
Not certain from the listing — No specific compliance standards (like SOC2 or ISO) or access control mechanisms are mentioned in the brief description.
Patronus AI interacts directly with the agent ecosystem by evaluating external AI agents. Threats include cascading failures if a compromised agent under evaluation exploits the evaluation platform itself.
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.