# io.github.dingdawg/dingdawg-shield (npm · dingdawg-shield)

AI security scanning and trust scoring. Stack-specific threat models. Free local scan.

- Trust score: 68/100 (medium)
- Change this week: +22
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- npm · `dingdawg-shield`: 68/100 (this document), [markdown](https://verifymcp.io/servers/dingdawg-dingdawg-shield/dingdawg-shield.md), [page](https://verifymcp.io/servers/dingdawg-dingdawg-shield/dingdawg-shield)

## Channel facts

- Registry: `npm`
- Package: `dingdawg-shield`
- Version: `1.0.4`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 86/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 18 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 71/100
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 340 tokens (~113/item across 3 items; 3 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add dingdawg-dingdawg-shield -- npx -y dingdawg-shield
```

### Codex

```bash
codex mcp add dingdawg-dingdawg-shield -- npx -y dingdawg-shield
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "dingdawg-dingdawg-shield": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "dingdawg-shield"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add dingdawg-dingdawg-shield --command npx --arg -y --arg dingdawg-shield
```

### Hermes

```yaml
mcp_servers:
  dingdawg-dingdawg-shield:
    command: "npx"
    args: ["-y", "dingdawg-shield"]
```

### Other

```json
{
  "mcpServers": {
    "dingdawg-dingdawg-shield": {
      "command": "npx",
      "args": [
        "-y",
        "dingdawg-shield"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 68, +4)

- [functional improvement] Stability: unverified → 0.27

### 2026-08-02 (score 64, +38)

- [security regression] Provenance: unverified → fail
- [security improvement] Install scripts: unverified → pass
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet.
- [functional regression] Capabilities: pass → unverified
- [functional improvement] Schema quality: unverified → good
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional improvement] License: unverified → pass
- [functional] Licence: MIT

### 2026-08-01 (score 26, +21)

- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Tool coverage: unverified → 100

### 2026-07-31 (score 5, −23)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 28, −18)

- [security regression] Malware scan: pass → unverified

### 2026-07-27 (score 46)

First indexed and scored.

## MCP tools (3)

### `security_scan` (~119 tokens)

Scan a codebase for AI security vulnerabilities. Free tier: 1 local scan per day. Checks SQL injection, XSS, SSRF, path traversal, prompt injection, hardcoded secrets, insecure deserialization, missing auth, data leakage, and more.

Input parameters:

- `code_snippet` (string, required): Code or system description to scan for AI security vulnerabilities
- `language` (string): Programming language (python, typescript, javascript, etc.)
- `scan_depth` (string): Scan depth — quick (free), standard (API), deep (API)

### `governance_check` (~92 tokens)

Check if an AI system meets governance requirements. Evaluates transparency, accountability, fairness, safety, and privacy controls. Free — runs locally.

Input parameters:

- `deployment_context` (string): Where this AI system is deployed
- `has_human_oversight` (boolean): Whether human oversight exists for AI decisions
- `system_description` (string, required): Describe your AI system: purpose, data sources, decision scope, affected users

### `trust_score` (~129 tokens)

Get a quantified trust score for an AI agent or system. $0.05 per check via API, or free local assessment. Returns a 0-100 score with breakdown across reliability, safety, transparency, fairness, and accountability.

Input parameters:

- `certifications` (array): Existing certifications (SOC2, ISO 27001, etc.)
- `incident_history` (string): Any known incidents, failures, or complaints
- `system_description` (string, required): What the system does, how it makes decisions, what data it uses
- `system_name` (string, required): Name of the AI system or agent to score

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/dingdawg-dingdawg-shield/dingdawg-shield#diagnostics

## Score history

- 2026-08-03: 68
- 2026-08-02: 64
- 2026-08-01: 26
- 2026-07-31: 5
- 2026-07-30: 28
- 2026-07-28: 46
- 2026-07-27: 46

## Links

- npm package: https://www.npmjs.com/package/dingdawg-shield
- Socket report: https://socket.dev/npm/package/dingdawg-shield
- Repository: https://github.com/dingdawg/dingdawg-agent-1
- Changelog RSS feed: https://verifymcp.io/servers/dingdawg-dingdawg-shield/dingdawg-shield/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/dingdawg-dingdawg-shield/dingdawg-shield/changelog.json
- HTML version of this page: https://verifymcp.io/servers/dingdawg-dingdawg-shield/dingdawg-shield