LlamaIndex — agentic threat model
LlamaIndex acts as a highly autonomous data and multi-agent orchestration framework, presenting significant risks of data poisoning, tool misuse, and cascading multi-agent failures if deployed without strict external sandboxing and access controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.90 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.80 | |
| 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 — LlamaIndex is model-agnostic and connects to external LLMs; foundation model threats like adversarial prompt injection or model reprogramming depend entirely on the specific LLM selected by the developer.
As a data framework supporting read/write functions and dynamic data ingestion, LlamaIndex is highly exposed to data/knowledge-base poisoning, embedding inversion, and unauthorized data exfiltration from connected custom data sources.
The framework's core reasoning loops and tool abstractions are vulnerable to tool misuse, insecure tool integration, and framework-level orchestration bypasses if malicious inputs manipulate the agentic flow.
Not certain from the listing — although it mentions a 'distributed service-oriented architecture' and 'ease of deployment', specific infrastructure security controls, sandboxing of tool execution, and secrets management are deployment-dependent.
Not certain from the listing — the description highlights iteration and orchestration but does not detail built-in evaluation, guardrails, or logging mechanisms to detect drift or anomalous agent behavior.
Not certain from the listing — there is no mention of built-in security compliance, access control policies (RBAC), or identity management to govern how the framework interacts with sensitive enterprise data sources.
By simplifying the deployment of multi-agent AI systems, LlamaIndex introduces risks of agent-to-agent trust abuse, cascading failures across distributed agents, and horizontal privilege escalation within the agent ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).