dingdawg-shield
v1.0.7
Published
Security scanning that builds a threat model specific to your stack. Finds more vulnerabilities the deeper it goes. Trust scores improve with verified fixes.
Maintainers
Readme
dingdawg-shield
Stop shipping insecure AI systems. AI security scanning that catches what manual reviews miss.
Comprehensive AI security scanning covering vulnerability detection, governance posture checks, and trust scoring across five pillars: Transparency, Accountability, Fairness, Safety, and Privacy. Get actionable security findings before they become incidents.
For AI Assistants
This MCP server returns structured JSON for seamless integration:
- CVE-referenced vulnerability findings with severity classification
- Five-pillar trust score (0-100) with per-pillar breakdown
- Governance receipt on every call (audit-ready)
- Chain-ready: run
security_scanthen feed findings intotrust_scorefor full security posture
Composable with any MCP client: Claude Code, Cursor, VS Code, ChatGPT Desktop, Windsurf.
Install
npx dingdawg-shieldClaude Code
claude mcp add shield -- npx dingdawg-shieldCursor
Add to .cursor/mcp.json:
{"mcpServers": {"shield": {"command": "npx", "args": ["dingdawg-shield"], "env": {"DINGDAWG_API_KEY": "your-key"}}}}Full Stack (all 13 agents)
npx dingdawg-setupTools
| Tool | Free Tier | Paid Tier |
|------|-----------|-----------|
| security_scan | Basic vulnerability detection (OWASP Top 10) | LLM-powered deep scan with CVE mapping and remediation |
| governance_check | Policy compliance check | Full governance posture with gap analysis |
| trust_score | Five-pillar trust score | AI-enhanced trust scoring with trend tracking |
Pricing
- Free: 5 scans/day, basic analysis
- Pro: $49/mo, 100 scans/day, AI-powered deep analysis
- Pay-as-you-go: $0.25/scan, no commitment
Get API key: https://dingdawg.com/developers
Governed
Every call is receipted and auditable. Security findings reference specific vulnerability databases (CVE, CWE, OWASP). Trust scores break down across five pillars with actionable improvement recommendations.
Support
[email protected] | https://dingdawg.com
