Teammately — agentic threat model
Teammately presents a high agentic risk profile due to its autonomous capability to write, evaluate, and iterate on AI models and code, which could lead to supply chain vulnerabilities or unauthorized code execution if compromised.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.70 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.60 | |
| 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 — the specific foundation models used are not disclosed, but the agent's reliance on LLMs for autonomous iteration exposes it to prompt injection, adversarial manipulation, and mis-aligned outputs during code generation.
Not certain from the listing — while RAG and ML data operations are mentioned, the specific vector stores or data pipelines are not detailed. Threats include knowledge-base poisoning and exfiltration of proprietary training data.
The agent framework autonomously plans, iterates, and executes code/prompts to build AI products. This introduces severe threats of tool misuse, framework vulnerabilities, and insecure tool integration if the agent executes generated code in an unconstrained environment.
Not certain from the listing — the hosting, sandboxing, and execution environment for running and evaluating user models are not specified. Inadequate sandboxing could lead to container escape or host compromise during automated testing.
The agent emphasizes a 'scientific approach' with 'AI-driven testing and evaluation'. However, this introduces the threat of evaluation gaming, where the agent optimizes prompts or models to pass automated tests without achieving genuine quality, alongside potential logging blind spots.
Not certain from the listing — there are no details regarding authentication, authorization, access controls, or compliance frameworks (such as SOC2 or ISO) governing the agent's operations.
Because the agent is designed to build, evaluate, and refine other 'AI products, models, and agents', it operates within a complex agent ecosystem. This creates risks of cascading failures, rogue child agents, and agent-to-agent trust abuse.
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.