youtube-downloader — agentic threat model
The overall risk posture is high due to the agent's capability to execute external CLI binaries (yt-dlp, ffmpeg) and write files directly to the host system, creating a significant attack surface for command injection and host compromise if input URLs or auth headers are manipulated.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.20 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — The listing does not specify which foundation model orchestrates this skill. Standard LLM risks like prompt injection could be used to manipulate CLI arguments.
Not certain from the listing — No vector store or RAG is mentioned. The primary data operations involve downloading and writing media files to the host, risking path traversal or disk exhaustion.
The agent orchestrates a pipeline using yt-dlp and ffmpeg. Insecure tool integration is a major threat, as malicious URLs or crafted auth headers could lead to command injection.
The agent invokes external CLI binaries and writes media files directly on the host. Without strict containerization or sandboxing, this poses a severe risk of host compromise.
Not certain from the listing — There is no mention of logging, guardrails, or monitoring for malicious inputs (e.g., sanitizing URLs before passing them to yt-dlp).
The agent handles authenticated streams using auth headers. If these credentials/headers are not securely stored or masked, they could be leaked in logs or exfiltrated.
Not certain from the listing — The agent is described as a standalone community skill; multi-agent interactions or marketplace trust boundaries are not defined.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).