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← Claude Notifications Go

Claude Notifications Go — agentic threat model

6.6AIVSS 6.6 · Medium

Claude Notifications Go presents a moderate risk profile primarily centered on data exfiltration, as it analyzes local workspace context and transmits notifications to external webhooks (Slack, Telegram, ntfy) from a local execution environment.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 6.5AARS uplift 0.84Factor sum 2.4/10Threat ×1.0Mitigation ×0.9
Autonomy of Action
0.30
Goal-Driven Planning
0.10
Self-Modification
0.00
Dynamic Tool Use
0.40
Persistent Memory
0.10
Contextual Awareness
0.60
Dynamic Identity
0.20
Multi-Agent Interactions
0.20
Non-Determinism
0.30
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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — The plugin relies on Claude Code's underlying foundation model for context analysis, making it indirectly susceptible to prompt injection or model-level manipulation that could alter notification content.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — While the plugin performs context analysis to generate notifications, it is unclear if it caches, stores, or logs this context locally, posing potential data leakage risks if workspace data is included in webhook payloads.

L3 · Agent Frameworks✓ mapped

The plugin integrates directly with the Claude Code agent framework by declaring 4 commands and hooking into execution events, creating a potential vector for malicious command execution or hook hijacking if the plugin is compromised.

L4 · Deployment & Infrastructure✓ mapped

Runs locally as a cross-platform (Linux, macOS, Windows) zero-dependency plugin, meaning it operates within the user's local host environment and inherits the user's local execution privileges.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — The plugin acts as an observability tool itself by sending notifications, but there is no mention of internal guardrails, input validation, or logging of the webhook payloads it dispatches.

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

Not certain from the listing — It is unclear how webhook credentials (tokens/URLs for Slack, Telegram, ntfy) are securely stored, managed, or encrypted on the local machine.

L7 · Agent Ecosystem✓ mapped

The plugin bridges the local agent framework to external communication ecosystems (Slack, Telegram, ntfy), establishing an outbound data channel that could be abused for unauthorized data exfiltration.

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