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Cross-Site Scripting and HTML Injection Testing — agentic threat model

9.2AIVSS 9.2 · Critical

This agent skill possesses high-risk offensive capabilities (XSS exploitation, session hijacking) without built-in safety guardrails or scoping controls, making it a potent vector for unauthorized web attacks or reverse-compromise if the agent processes malicious target payloads.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 8.5AARS uplift 0.74Factor sum 4.7/10Threat ×1.05Mitigation ×1.0
Autonomy of Action
0.60
Goal-Driven Planning
0.70
Self-Modification
0.10
Dynamic Tool Use
0.80
Persistent Memory
0.20
Contextual Awareness
0.70
Dynamic Identity
0.40
Multi-Agent Interactions
0.10
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — likely relies on a general-purpose LLM for payload generation and reasoning, which is susceptible to prompt injection allowing attackers to redirect the offensive testing tools against unauthorized targets.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — the skill likely does not use a dedicated vector database or RAG, but rather processes live HTTP responses and DOM structures in context, presenting risks of data exfiltration if target pages contain sensitive data.

L3 · Agent Frameworks✓ mapped

The skill orchestrates offensive tools for XSS exploitation, cookie theft, and CSP bypass; insecure tool integration or lack of input validation on target responses could allow a malicious target website to execute prompt injection or exploit the agent's execution environment.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — as an open-source skill, deployment infrastructure is host-dependent, but running offensive web-testing tools requires strict sandboxing to prevent local network scanning or SSRF if the agent is compromised.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — there is no mention of built-in logging, guardrails, or safety filters to prevent the skill from being used against unauthorized domains or non-scope targets.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — lacks explicit authorization, scoping controls, or policy enforcement mechanisms, posing significant legal and compliance risks (e.g., unauthorized penetration testing).

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — while designed as an individual skill, if integrated into a multi-agent system, a compromise of this skill could allow it to be used by other rogue agents to conduct internal scanning or session hijacking.

MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).