Sciloop — agentic threat model

Not certain from the listing — Sciloop likely relies on advanced LLMs for code generation and hypothesis formulation. Threats include prompt injection leading to malicious code generation and model reprogramming.

Not certain from the listing — As an ML research tool, it must ingest and manipulate large datasets. Threats include training data poisoning and unauthorized exfiltration of proprietary research data.

Not certain from the listing — The agent framework must orchestrate complex ML workflows. Insecure tool integration is a major threat, particularly if the agent can execute arbitrary Python code or shell commands during experiments.

Not certain from the listing — Running ML experiments requires significant compute resources. If the execution environment is not strictly sandboxed, attackers could achieve container escape, lateral movement, or resource hijacking (e.g., GPU mining).

Not certain from the listing — Monitoring is critical to detect anomalous code execution or resource consumption. Gaps in observability could allow malicious agent behaviors to go unnoticed during long-running training loops.

Not certain from the listing — Intellectual property protection and access controls are vital for research tools. There is no mention of compliance frameworks (like SOC2) or data governance policies to protect sensitive research.

Not certain from the listing — It is unclear if Sciloop interacts with external model registries, code repositories, or other agents, which could introduce supply chain risks and cascading failures.