← Gemma 4 Local Hardware Matcher
Gemma 4 Local Hardware Matcher — agentic threat model
The Gemma 4 Local Hardware Matcher presents low inherent agentic risk due to its limited autonomy, but poses a moderate social engineering risk if an attacker manipulates the tool to generate malicious local execution commands that users run blindly.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.20 | |
| Opacity & Reflexivity | 0.20 |
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 tool's underlying model is unspecified, but if it uses an LLM to generate run commands, it is vulnerable to prompt injection that could alter the output commands to include malicious payloads.
Not certain from the listing — it is unclear how hardware specifications are ingested, processed, or if any telemetry/system data is exfiltrated or cached locally.
Not certain from the listing — the orchestration framework is not detailed. Insecure tool integration could allow system profiling utilities to be abused or hijacked.
Not certain from the listing — whether this runs as a local CLI tool, a web page utilizing WebGL/WebGPU APIs, or a packaged application is unspecified, which heavily impacts sandboxing and host compromise risks.
Not certain from the listing — there is no mention of output validation, guardrails, or logging to ensure generated commands are safe before being presented to the user.
Not certain from the listing — no security controls, identity management, or compliance frameworks are described for validating the integrity of the diagnostic tool.
The agent operates as a standalone local diagnostic utility with no multi-agent coordination or ecosystem marketplace integrations described.
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.