ScrapeGraphAI — agentic threat model
ScrapeGraphAI presents a moderate-to-high risk profile primarily driven by the threat of indirect prompt injection, where untrusted web content can hijack the LLM-driven extraction pipeline to exfiltrate data or trigger SSRF.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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.
Integrates with external LLMs (GPT, Gemini, Groq, Azure, Ollama). The primary threat is indirect prompt injection, where malicious instructions embedded in scraped web pages manipulate the underlying model's behavior.
Processes HTML, XML, and JSON data sources. Risks include data exfiltration of sensitive scraped documents and potential XML External Entity (XXE) or injection attacks if local files are parsed insecurely.
Uses graph-based scraping pipelines to orchestrate extraction. Vulnerabilities in the pipeline execution logic could lead to insecure tool integration, such as Server-Side Request Forgery (SSRF) when fetching untrusted URLs.
Not certain from the listing — deployment is flexible (on-premises or cloud) as a Python library, meaning infrastructure security, sandboxing of the scraping environment, and secret management (API keys for LLMs) depend entirely on the user's host environment.
Not certain from the listing — there is no mention of built-in evaluation, logging, or guardrails to detect drift, anomalous scraping behavior, or prompt injection attempts.
Not certain from the listing — as an open-source Python library, it lacks built-in compliance frameworks, access controls, or audit logging, leaving these to be implemented by the deploying organization.
Not certain from the listing — the library operates standalone and does not natively feature multi-agent coordination or marketplace integrations, minimizing direct ecosystem risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.