@nanomind/daemon

@nanomind/daemon

Persistent NanoMind inference server. Runs on localhost:47200 with HTTP and Unix socket interfaces. Lazy-loads the model on first request, unloads after idle timeout.

Install

npm install @nanomind/daemon

Quick Start

# Start the daemon
nanomind-daemon start

# Check status
nanomind-daemon status

# Stop
nanomind-daemon stop

HTTP API

POST /v1/infer

Classify input text using the loaded NanoMind model.

curl -X POST http://127.0.0.1:47200/v1/infer \
  -H "Content-Type: application/json" \
  -d '{
    "intent": "SCAN_SKILL",
    "input": "This skill forwards tokens to an external endpoint",
    "context": { "artifactType": "skill" },
    "priority": "high"
  }'

Response (malicious classification):

{
  "intent": "SCAN_SKILL",
  "result": "exfiltration",
  "confidence": 0.94,
  "attackClass": "exfiltration_pattern",
  "evidence": "exfiltration",
  "latencyMs": 2,
  "modelVersion": "nanomind-tme-v0.5.0"
}

Response (benign):

{
  "intent": "INTENT_CHECK",
  "result": "benign",
  "confidence": 0.91,
  "attackClass": "",
  "evidence": "benign",
  "latencyMs": 1,
  "modelVersion": "nanomind-tme-v0.5.0"
}

Response schema

Field	Type	Required	Description
intent	string	yes	Echoes the request intent (or routed intent if not provided).
result	string	yes	Raw model label (e.g. "injection", "benign") — convenience for human-readable logs.
confidence	number	yes	Softmax probability of the predicted class, in [0, 1].
attackClass	string	yes	Canonical attack-class label, or empty string. See enum + mapping below.
evidence	string	no	Raw 10-way model label. Carries audit-trail granularity beyond the canonical bucket.
remediation	string	no	Suggested remediation text (reserved for future use).
latencyMs	number	yes	End-to-end inference latency in milliseconds.
modelVersion	string	yes	Loaded model identifier.

attackClass enum

The field is always emitted. An empty string means "no malicious intent detected"; non-empty values are produced by the v0.5.0 production classifier:

Value	Meaning
""	No malicious intent detected (model classified as benign).
"exfiltration_pattern"	Output or tool call appears to forward sensitive data to an external destination.
"prompt_injection"	Input contains instructions that attempt to override the agent's policy.
"tool_misuse"	Capability or tool used outside its declared purpose.
"data_extraction"	Sequence of reads consistent with bulk data extraction.

attackClass mapping

The model emits 10 raw labels (matching the 10-class training corpus). The daemon maps them to the 5-value canonical attackClass enum above for the FGA decision contract, while preserving the raw label in evidence so audit and telemetry retain full granularity.

Raw model label	attackClass
benign	""
injection	prompt_injection
social_engineering	prompt_injection
exfiltration	exfiltration_pattern
steganography	exfiltration_pattern
credential_abuse	data_extraction
privilege_escalation	tool_misuse
persistence	tool_misuse
lateral_movement	tool_misuse
policy_violation	tool_misuse

FGA contract

AIM's FGA Step 5 (fga_engine.go::checkIntentSync) reads this response and blocks when:

attackClass != "" && confidence > 0.8

The wire contract is required (the field is always present); the value is empty when the model classifies the request as benign and non-empty otherwise. Consumers needing 10-way granularity (e.g. dashboards, runtime correlation) read the raw label from evidence.

GET /health

curl http://127.0.0.1:47200/health
# {"running":true,"port":47200,"activeTasks":0,"modelLoaded":false,"startedAt":"2026-04-28T21:16:18.748Z","uptime":2069}

modelLoaded is false until the first /v1/infer call — the model lazy-loads on demand and unloads after idleUnloadSeconds of inactivity.

GET /v1/status

curl http://127.0.0.1:47200/v1/status
# {"running":true,"port":47200,"activeTasks":0,"modelLoaded":false,"startedAt":"2026-04-28T21:16:18.748Z","uptime":2069}

Known model-quality limitations (v0.5.0 classifier)

The v0.5.0 Mamba-TME classifier ships with documented limitations on inputs outside its training corpus distribution. These are model properties, not daemon bugs, and the wire contract (attackClass always emitted, canonical 5-value enum) holds in every case.

• Confidence saturation. The softmax output frequently saturates at exactly 1.0 rather than expressing graduated probability. Threshold-based decisions (e.g. AIM FGA Step 5's confidence > 0.8) become effectively binary in the saturated zone.
• False positives on natural-language benign inputs. Generic operational queries (e.g. "What is the weather today?", "list users") may classify as non-benign with high confidence because the training corpus underweights conversational benign prose.
• False negatives on natural-language attack paraphrases. Exfiltration intents expressed in natural language outside the training corpus's phrasing (e.g. "transfer the contents of <file> to <url> via webhook") may classify as benign.

Recommended consumer mitigations until v0.6.0:

• Set confidence > 0.95 (not 0.8) before treating a non-empty attackClass as actionable.
• Combine the classifier signal with a corroborating signal (rule-based detectors, telemetry, capability checks) before blocking — do not rely on the classifier alone for high-stakes decisions.
• Log every (input, attackClass, confidence) triple. Out-of-distribution behavior is the input source for the v0.6.0 retrain corpus.

A retrain to v0.6.0 with broader corpus coverage is tracked separately. The 5-value canonical attackClass enum will not change between v0.5.0 and v0.6.0; only model accuracy improves.

Configuration

Option	Default	Description
httpPort	47200	HTTP server port
ipcPath	/tmp/nanomind.sock	Unix socket path
maxConcurrent	4	Max concurrent inference requests
idleUnloadSeconds	300	Unload model after N seconds idle

Programmatic Usage

import { NanoMindDaemon } from '@nanomind/daemon';

const daemon = new NanoMindDaemon({ httpPort: 47200 });
await daemon.start();

// Direct inference (bypasses HTTP)
const result = await daemon.infer({
  intent: 'COMPILE_AST',
  input: skillContent,
  priority: 'high',
});

Model files

The daemon loads the v0.5.0 production NanoMind classifier (Mamba-TME, 8 blocks, 6000-token vocab) from ~/.nanomind/models/. Three files are required:

File	Purpose
nanomind-tme.onnx	ONNX graph (architecture only — small).
nanomind-tme.onnx.data	External weights data file (~8MB).
tokenizer.json	Word-level vocabulary (6000 entries).

Download from HuggingFace (opena2a/nanomind-security-classifier):

mkdir -p ~/.nanomind/models
cd ~/.nanomind/models
BASE=https://huggingface.co/opena2a/nanomind-security-classifier/resolve/main
curl -sSL -o nanomind-tme.onnx        "$BASE/nanomind-tme.onnx"
curl -sSL -o nanomind-tme.onnx.data   "$BASE/nanomind-tme.onnx.data"
curl -sSL -o tokenizer.json           "$BASE/tokenizer.json"

Security

• Binds to 127.0.0.1 only (no external access).
• Model files SHA-256 verified on load against canonical hashes recorded in nanomind-models.json (v0.5.0). Mismatch fails the daemon hard rather than silently running a tampered or stale model.
• Request size capped at 1MB.
• Rate limited to 100 requests/second.
• No credentials in memory after model load.

License

MIT