Fixie — agentic threat model
Fixie is an open-source framework for building LLM-powered agents, presenting a moderate-to-high risk profile due to its enablement of dynamic tool execution and autonomous planning without built-in, visible security guardrails in the basic listing.
OWASP AIVSS score rationale
| 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 | 0.50 | |
| 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 — as a framework, Fixie allows developers to plug in various foundation models, making it susceptible to standard LLM threats like prompt injection or adversarial examples depending on the chosen model.
Not certain from the listing — the platform likely supports RAG and data integration, but specific vector databases or data lineage controls are not detailed in the brief description.
As an agent framework, Fixie orchestrates planning, memory, and tool calling, exposing it to risks like insecure tool integration, framework-level vulnerabilities, and prompt injection leading to unauthorized tool execution.
Not certain from the listing — deployment details, sandboxing of agent code, and secrets management are not specified, leaving potential risks of container escape or privilege escalation if self-hosted insecurely.
Not certain from the listing — there is no mention of built-in evaluation, guardrails, or observability tools to detect drift, anomalies, or malicious agent behavior.
Not certain from the listing — compliance certifications, identity management, and access control policies are not described in the public listing.
Not certain from the listing — while designed to build agents, the listing does not detail multi-agent coordination protocols or marketplace risks, though cascading failures are possible in custom multi-agent deployments.
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.