Multi-GPT — agentic threat model

Uses GPT-4 for text generation, exposing the system to standard LLM vulnerabilities such as prompt injection, adversarial manipulation, and misaligned outputs that could disrupt agent coordination.

Features file storage and long/short-term memory management. This introduces risks of memory poisoning, unauthorized data exfiltration via internet access, and data integrity issues within the shared file store.

Orchestrates multiple 'expertGPTs' with internet access and file capabilities. Insecure tool integration or prompt injection could lead to tool misuse, SSRF via internet search, or arbitrary file manipulation.

Not certain from the listing — deployment infrastructure depends entirely on how the user hosts this open-source framework; risks include exposed API keys, lack of container sandboxing for file operations, and insecure local execution environments.

Not certain from the listing — no built-in evaluation, logging, or guardrail mechanisms are described, which may result in complete operational blindness regarding inter-agent communication and decision-making paths.

Not certain from the listing — lacks explicit security controls, authentication, authorization policies, or compliance frameworks in the public description, shifting all security responsibility to the deployer.

Designed specifically for collaborative multi-agent environments ('expertGPTs' communicating and sharing info). This creates a high risk of agent-to-agent trust abuse, cascading failures, and emergent rogue behaviors if one agent is compromised.