mcp-trove — agentic threat model
mcp-trove acts as a high-value target storing encrypted secrets and snippets; its primary risk is prompt injection on consuming agents leading to unauthorized credential extraction.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.30 | |
| Non-Determinism | 0.10 | |
| 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 — mcp-trove is an MCP tool/server rather than a foundation model itself, but it is highly vulnerable to adversarial prompt injections targeting the LLMs that call it.
Manages a git-backed vault for plaintext snippets and encrypted secrets. Threats include unauthorized data exfiltration of secrets and Git repository poisoning.
Exposes MCP tools for secret retrieval. Threats include insecure tool integration and indirect prompt injection forcing the host agent to call retrieval tools maliciously.
Relies on local or remote Git storage and file-system key paths. Threats include local privilege escalation to read the encryption key path or compromise the Git host.
Not certain from the listing — there is no mention of built-in logging, access auditing, or anomaly detection for secret retrieval attempts.
Uses key-path based encryption and access controls. Threats include weak key management, lack of robust authentication between the calling agent and the MCP server, and compliance gaps in credential handling.
Operates within the MCP ecosystem where other agents can request secrets. Threats include rogue or compromised agents abusing trust to dump credentials horizontally.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).