social-dive — agentic threat model
social-dive acts as a high-exposure data ingestion vector, exposing consuming agents to severe indirect prompt injection risks by pulling untrusted content from over 20 external sources without built-in sanitization.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.30 |
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 is used, but the retrieved untrusted text from 20+ sources poses a severe risk of indirect prompt injection and model reprogramming to any model consuming this MCP server.
High risk of data poisoning and context contamination. The tool reads directly from untrusted third-party sources (GitHub, Wikipedia, YouTube) which can easily host malicious payloads designed to hijack the agent's context.
The tool integrates as an MCP (Model Context Protocol) server. Insecure tool integration or lack of output sanitization could allow malicious search results to trigger unintended tool calls in the parent agent framework.
Remote-capable hosting is supported. If deployed insecurely without proper network sandboxing, an attacker could exploit the server to perform Server-Side Request Forgery (SSRF) or scan internal networks.
Not certain from the listing — There is no mention of built-in guardrails, input/output filtering, or observability tools to detect and block malicious payloads in the retrieved content.
Not certain from the listing — No authentication, authorization, or compliance controls are described for securing the remote-capable hosting feature.
High risk of cascading failures in agent ecosystems. As an open-source MCP tool, it can be integrated into various multi-agent workflows, serving as a vector for cross-agent prompt injection and trust abuse.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).