Firecrawl MCP Server — agentic threat model
The Firecrawl MCP Server acts as a high-risk vector for indirect prompt injection by feeding untrusted web content directly to LLMs. Its primary risks include tool abuse, such as SSRF or credit exhaustion, and the lack of built-in sanitization for scraped data.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.40 | |
| 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 — Firecrawl MCP Server is an integration tool rather than a foundation model itself, but the LLMs calling it are highly vulnerable to indirect prompt injection and adversarial reprogramming via the scraped web content it returns.
Fetches untrusted web content which acts as a primary vector for indirect prompt injection and data poisoning. Structured extraction could be manipulated by malicious web page layouts or hidden text to exfiltrate data or poison downstream vector stores.
Integrates via Model Context Protocol (MCP) to provide scraping and crawling tools. Vulnerable to tool misuse where an orchestrating agent is tricked into crawling malicious sites, exhausting metered credits, or executing SSRF-like actions via the crawl tool.
Not certain from the listing — The deployment environment of the MCP server itself (local vs cloud) and how API keys for Firecrawl are stored are not detailed, posing risks of credential theft or SSRF if the server is not properly sandboxed.
Not certain from the listing — No built-in guardrails, content filtering, or logging mechanisms are mentioned to detect indirect prompt injections or anomalous crawl patterns before they reach the LLM.
Not certain from the listing — Access control and rate limiting policies for the MCP server are unspecified, though metered credits imply some financial/usage control.
Designed to be used by other agents via MCP. A compromised or rogue agent could abuse this tool to perform distributed scraping, scan internal networks (if SSRF is possible), or cascade prompt injections to other connected agents in the ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).