Project Alice — agentic threat model

Supports a wide variety of local (LM Studio, Llama, Mistral) and cloud-based (OpenAI, Anthropic, Gemini, Cohere) foundation models. Threats include adversarial prompt injection bypassing chain-of-thought reasoning, and model misalignment or tampering in locally hosted environments.

Utilizes RAG and embeddings to ingest data. The primary threat is data poisoning of the knowledge base, especially when ingesting untrusted real-time data from integrated search tools (Google, Reddit, Arxiv).

Orchestrates complex workflows using chain-of-thought, tool calls, and direct code execution. This creates a severe threat of tool misuse, where malicious inputs from external searches could exploit the code execution engine to run arbitrary commands.

Not certain from the listing — No details are provided regarding the hosting environment, secrets management, or whether the code execution tool runs within a secure, isolated sandbox. Untrusted code execution poses a direct threat of host compromise.

Not certain from the listing — There is no mention of built-in evaluation, logging, or guardrails to monitor tool outputs, code execution results, or model drift.

Not certain from the listing — The framework does not specify any identity management, access control policies, or compliance auditing mechanisms for restricting sensitive tool usage.

Not certain from the listing — While described as a complete framework, there are no explicit details regarding multi-agent orchestration, agent-to-agent trust boundaries, or marketplace integrations.