PlexeAI — agentic threat model

Not certain from the listing — The specific foundation models used to interpret natural language and generate code are undisclosed. Threats include prompt injection manipulating the model generation logic, and model stealing of the generated custom architectures.

Plexe ingests user data to train, evaluate, and continuously improve custom models. This exposes the platform to data poisoning (corrupting the generated model's behavior) and data exfiltration of proprietary business datasets.

Not certain from the listing — The orchestration framework managing the transition from natural language definition to a trained model is proprietary. Threats include insecure tool integration, particularly if the orchestrator executes generated code or training scripts in an unconstrained manner.

Not certain from the listing — The hosting and sandboxing environment for model training and execution is not detailed. A key threat is container escape or privilege escalation during the resource-heavy model generation and training phases.

Plexe emphasizes 'interpretable results' and 'continuous model improvement', requiring robust evaluation and drift monitoring. Gaps here could lead to evaluation gaming, undetected model drift, or insufficient logging of malicious training inputs.

Not certain from the listing — No specific compliance certifications (e.g., SOC2, ISO) or identity governance controls are mentioned, raising concerns about unauthorized access to proprietary generated models and training data.

Not certain from the listing — While Plexe focuses on model generation, it is unclear if these models interact in a multi-agent ecosystem. If they do, threats include cascading failures and trust abuse between generated models.