primaBots — agentic threat model
primaBots acts as a multi-model aggregator and custom bot creation platform, presenting moderate risk primarily centered around prompt injection, insecure handling of user-provided data, and potential execution of untrusted generated code.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.70 | |
| 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.
Leverages major foundation models (OpenAI, Claude, Gemini). Primary threats include prompt injection bypassing custom bot instructions, and model-switching vulnerabilities where adversarial inputs exploit weaknesses specific to one model.
Not certain from the listing — The platform allows tailoring bots to user 'data', which implies a RAG or vector database mechanism. This introduces risks of data exfiltration via prompt injection and unauthorized access to uploaded datasets.
Not certain from the listing — The orchestration framework for custom bots is unspecified. If bots can invoke tools or APIs dynamically, there is a risk of insecure tool execution or state manipulation across model switches.
Not certain from the listing — No details are provided regarding the sandboxing of generated code or the secure storage of API keys used to access external LLM providers.
Not certain from the listing — There is no mention of built-in guardrails, output filtering, or logging mechanisms to monitor user interactions and detect malicious bot behavior.
Not certain from the listing — No compliance certifications (such as SOC2 or GDPR alignment) or enterprise access controls are detailed for managing custom bot permissions.
The platform functions as a curated library/ecosystem of specialized bots. This introduces risks of ecosystem contamination if users can publish malicious custom bots, or if the curation process fails to detect compromised tools.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).