dnnsec
# dnnsec
[](https://www.npmjs.com/package/dnnsec)
[](https://opensource.org/licenses/MIT)
**Deep Neural Network Security (DNNSEC)** MCP Server. Designed for enterprise AI systems to verify neural model weight integrity, audit adversarial robustness, and safeguard autonomous agent infrastructure.
## Features
- **Model Integrity Verification**: Cryptographic validation of neural weights and checkpoint artifacts.
- **Adversarial Robustness Screening**: Automated resilience check against FGSM/PGD perturbations.
- **Model Context Protocol (MCP)**: Native integration with Claude Desktop, Cursor, and autonomous agent frameworks.
## Quick Start
### Running directly via npx
```bash
npx dnnsec
```
### Claude Desktop Integration
Add to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"dnnsec": {
"command": "npx",
"args": ["-y", "dnnsec"]
}
}
}
```
## Tools Included
1. `verify_model_integrity`: Audits weights checksums and model authenticity.
2. `check_adversarial_robustness`: Evaluates resilience to input perturbation attacks.
## License
MIT © [tudadada](https://github.com/tudadada)
TDQS
Scored across 2 tools
The two tools have overlapping purposes: both verify model integrity and check adversarial robustness, making it unclear when to use one versus the other. An agent would struggle to decide which tool handles a given security assessment request.
Both tool names follow a consistent verb_noun pattern (verify_model_integrity, check_adversarial_robustness), though the verbs 'verify' and 'check' are near-synonyms, which slightly reduces clarity.
With only two tools, the server feels thin for a domain as broad as neural network security. The count is at the extreme low end, offering minimal coverage for the stated purpose.
The tool surface is severely limited: it covers verification and robustness screening but lacks common operations like model hardening, attack generation, report generation, or remediation. The domain is not fully covered.