LangGraph — agentic threat model
LangGraph is a highly flexible agentic framework enabling stateful, multi-agent workflows with cyclic graphs. Its primary risk lies in the complexity of orchestrating multi-agent interactions, state persistence, and tool execution, which can lead to cascading failures or unauthorized actions if not properly sandboxed.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.90 | |
| Self-Modification | 0.50 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.90 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| 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 — LangGraph is model-agnostic and does not provide its own foundation models, meaning model-level threats (adversarial prompt injection, poisoning) depend entirely on the external LLMs integrated by the developer.
Not certain from the listing — While LangGraph manages state and memory, the underlying data operations, vector databases, and RAG pipelines are configured externally by the developer, leaving data poisoning and exfiltration risks dependent on implementation.
As an orchestration framework, LangGraph is highly susceptible to framework-level vulnerabilities, including state manipulation, infinite loops in cyclic graphs, insecure tool integration, and state-poisoning across multi-turn conversations.
Not certain from the listing — LangGraph can be deployed locally, self-hosted, or via managed cloud infrastructure; thus, sandboxing, secret management, and network isolation risks depend on the deployment environment.
Not certain from the listing — The listing mentions an 'intuitive interface' but does not detail built-in guardrails or evaluation mechanisms, though the broader ecosystem (e.g., LangSmith) typically provides observability.
Not certain from the listing — There is no mention of built-in enterprise security controls, role-based access control (RBAC), or compliance certifications (such as SOC2 or GDPR) in the public directory listing.
LangGraph is explicitly designed for multi-agent architectures. This introduces significant ecosystem risks, including agent-to-agent trust abuse, cascading failures across collaborative agents, and unauthorized delegation of tasks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).