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OpenAI Agents SDK — agentic threat model

9.1AIVSS 9.1 · Critical

As an open-source framework, the OpenAI Agents SDK provides the scaffolding for highly autonomous, multi-agent systems, shifting the primary security responsibility (sandboxing, input validation, and secure tool execution) entirely to the implementing developer.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.1AARS uplift 0.95Factor sum 5.0/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.50
Goal-Driven Planning
0.60
Self-Modification
0.20
Dynamic Tool Use
0.70
Persistent Memory
0.50
Contextual Awareness
0.60
Dynamic Identity
0.30
Multi-Agent Interactions
0.70
Non-Determinism
0.50
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — While it is an OpenAI SDK likely designed for OpenAI's foundation models (e.g., GPT-4o), the specific model, its alignment, and vulnerability to adversarial prompt injection or data poisoning depend entirely on the developer's choice of model and configuration.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — The SDK provides orchestration but the data storage, vector databases, and RAG pipelines are defined by the developer, leaving potential risks of data poisoning or exfiltration dependent on implementation.

L3 · Agent Frameworks✓ mapped

As an open-source agent framework, this layer is highly relevant. The SDK defines how planning, memory, and tool calling are structured. Threats include insecure tool integration, framework-level vulnerabilities, and memory poisoning if state management is not secured.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — As an open-source SDK, deployment infrastructure (e.g., containerization, sandboxing of tool execution, secrets management) is entirely up to the developer deploying the framework.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — The SDK's built-in telemetry, logging, and guardrail capabilities are not detailed in the brief description, meaning developers must implement their own observability stack to detect drift or anomalies.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — Compliance with standards (like NIST or SOC2) and identity/authorization controls are not specified and must be managed at the application layer by the implementing organization.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — While the SDK may support multi-agent orchestration patterns, the specific ecosystem, agent-to-agent trust boundaries, and cascading failure risks depend on the multi-agent architecture designed by the developer.

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.