pskill9/web-search — agentic threat model
This agentic tool presents a moderate risk profile primarily driven by its role as an unauthenticated gateway to external web content, making it a highly viable vector for indirect prompt injection attacks via scraped search snippets.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.70 | |
| 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 MCP server itself does not host or define a foundation model, but the downstream LLM consuming its scraped outputs is highly vulnerable to indirect prompt injection and adversarial data poisoning.
The tool dynamically ingests external data by scraping Google search results. This introduces a significant data provenance gap and exposes the consuming system to untrusted, potentially poisoned web content acting as an injection vector.
As an MCP tool, it integrates directly into agent frameworks. Insecure tool integration could allow a calling agent to be hijacked if it executes instructions embedded within the scraped search snippets.
Not certain from the listing — The deployment environment of this lightweight MCP server is unspecified, though local execution without sandboxing could expose the host if the server itself is compromised or manipulated.
Not certain from the listing — There are no mentioned guardrails, content filtering, or observability mechanisms to detect or sanitize malicious payloads within the scraped search results before they reach the agent.
The tool operates without API keys or authentication, bypassing standard access controls. This lack of identity management and reliance on scraping also presents compliance and terms-of-service (ToS) risks.
Designed for the MCP ecosystem, this tool facilitates agent-to-tool interactions where upstream agents may implicitly trust the returned snippets, leading to cascading failures or compromised decision-making across an agentic workflow.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).