MADS — agentic threat model

Not certain from the listing — likely relies on external LLMs (e.g., OpenAI or local models via Autogen) which are susceptible to prompt injection, adversarial inputs in the CSV, or model reprogramming.

Processes user-provided CSV files and generates trained ML models. Vulnerable to data poisoning via malicious CSV inputs, data exfiltration if the CSV contains sensitive data, and lack of lineage tracking for generated models.

Uses the Autogen framework to orchestrate a multi-agent system executing a data science pipeline. Vulnerable to tool misuse (e.g., executing arbitrary Python code to train models or process data), insecure tool integration, and framework-level vulnerabilities in Autogen.

Not certain from the listing — as an open-source project, deployment infrastructure is user-managed. If run without sandboxing, the code execution environment (required for training ML models and running data pipelines) is highly vulnerable to container escape or host compromise.

Not certain from the listing — no built-in evaluation, logging, or guardrails are mentioned. Gaps in observability could lead to undetected drift, malicious code execution, or silent failures in the generated ML models.

Not certain from the listing — lacks explicit security controls, identity management, or compliance certifications. Users must implement their own access controls and data governance policies.

Orchestrates multiple agents to perform data science tasks. Vulnerable to agent-to-agent trust abuse, cascading failures if one agent generates faulty code/data, and rogue agent behavior within the Autogen orchestration.