nuclide-research/aimap
GitHub: nuclide-research/aimap
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# aimap
Fingerprint AI/ML infrastructure at population scale and enumerate what is exposed inside.
aimap is a single Go binary that TCP-connects to a target's open ports, matches
each response against a 196-fingerprint database of AI and ML services, then
runs up to 62 dedicated deep enumerators that pull collections, model lists,
experiment data, credentials in HTTP responses, claimable admin states, and PII
fields from whatever service answers. Three output phases: port discovery,
fingerprint matching, deep enumeration. The result is a JSON report shaped to
feed directly into `visorlog ingest`, `winnow`, and SIEM pipelines.
We built aimap because generic scanners (nmap, nuclei) see an open port and stop.
They do not know that the Ollama on port 11434 exposes every model it holds, that
the Flowise on port 3000 stores OpenAI keys in its credentials panel, or that the
Jupyter on port 8888 has no token. aimap knows all of those things and surfaces
them in a single pass.
## Install
go install github.com/nuclide-research/aimap@latest
Or build from source:
git clone https://github.com/nuclide-research/aimap
cd aimap
go build -o aimap .
Pre-built Linux amd64 and arm64 binaries are on the releases page.
Go 1.21+, zero external dependencies.
## Usage
aimap -target 192.0.2.10
aimap -target 10.0.0.0/24 -threads 50 -o audit.json
aimap -list ips.txt -ports-class llm-gateway -threads 30 -o out.json
aimap -version
| Flag | Default | Effect |
|------|---------|--------|
| `-target` | (required) | single IP, hostname, or CIDR |
| `-list` | | file of targets, one per line; `#` comments supported |
| `-ports` | 42-port default set | comma-separated port list |
| `-ports-class` | | named port profile (see below); overrides `-ports` |
| `-timeout` | `5s` | per-connection timeout |
| `-threads` | `20` | concurrent scan threads |
| `-o` | | JSON report output file |
| `-v` | off | verbose output |
| `-scan-all-fingerprints` | off | probe every fingerprint on every open port, bypassing the DefaultPorts filter |
| `-exclude-compromised` | off | drop extortion-wiped hosts (Meow-class) from the report |
| `-version` | | print version and exit |
Default 42-port list: `80,443,1984,2379,3000,3001,4000,4040,4200,5000,5001,5678,6333,7575,7576,7860,8000,8001,8080,8081,8088,8123,8233,8265,8443,8501,8787,8888,8889,9000,9090,9091,9200,10000,11434,15500,18080,18789,19530,30000,51000,55000`
## Port profiles
`-ports-class ` narrows the port list to a hand-curated set for a specific
service class. On a typical population survey this gives a 5-10x wall-time
reduction compared to the 42-port default.
| Profile | Ports | Best for |
|---------|-------|----------|
| `llm-gateway` | 12 ports | Ollama, vLLM, TGI, Open WebUI, LiteLLM, sub2api |
| `vector-db` | 11 ports | Qdrant, Weaviate, ChromaDB, Milvus |
| `observability` | 10 ports | Langfuse, Helicone, MLflow, Phoenix, Prometheus |
| `registry` | 11 ports | Docker, Harbor, Quay |
| `network-mesh` | 19 ports | Envoy admin, Istio, Linkerd, Kiali, Cilium |
| `workflow-orch` | 10 ports | Prefect, Dagster, Temporal, Argo |
| `browser-control` | 9 ports | CDP, Selenium Grid, Playwright MCP |
| `sub2api` | 6 ports | sub2api-class pooled-account proxies |
| `jetson` | 11 ports | Jetson edge AI, Triton, Frigate |
| `healthcare` | 10 ports | DICOM / PACS / dcm4chee / Orthanc |
| `finance` | 10 ports | QuantConnect, OpenBB, JESSE |
| `mcp` | 9 ports | Model Context Protocol servers |
| `wide` | 42 ports | the default catch-all, explicit selection |
| `minimal` | 4 ports | quick host-alive HTTP probe |
Define new profiles in `port_classes.go`: one map entry, no other files touched.
## What aimap fingerprints (196 services, 62 deep enumerators)
| Category | Services |
|----------|----------|
| Vector databases | Weaviate, ChromaDB, Qdrant, Milvus, Apache Solr, Meilisearch, Typesense, Vespa |
| LLM runtimes | Ollama, llama.cpp server, vLLM, SGLang, LocalAI, text-generation-webui |
| RAG frameworks | AnythingLLM, LightRAG, PrivateGPT, txtai, Cognita, R2R, Kotaemon, Quivr, Danswer/Onyx, Verba, DocsGPT, Ragapp, Perplexica, RAGFlow |
| Image generation | ComfyUI, AUTOMATIC1111 / SD WebUI, InvokeAI, Fooocus, SwarmUI |
| Embedding servers | HuggingFace TEI, infinity-embedding, Embedding API |
| Model serving | TensorFlow Serving, Triton Inference Server, NVIDIA NIM |
| ML platforms | MLflow, Weights & Biases, WandB Service, ClearML, Aim |
| Orchestration / UI | LangServe, Flowise, Dify, Open WebUI, SillyTavern, LiteLLM, One API, NewAPI, BentoML, sub2api |
| AI agent platforms | OpenHands, AutoGen Studio, Anti-detect CDP server, Mem0, Coolify, OpenClaw |
| MCP servers | MCP Server |
| Code assistants | Sourcegraph, Sourcebot, Sweep AI, Tabnine Context Engine, Dyad, bolt.diy, Refact |
| Agent memory | Mem0, Argilla, Zep, Letta |
| Data labeling | Label Studio, CVAT, Doccano, Prodigy |
| Compute orchestration | Ray Serve, Ray Dashboard, Kubeflow, Apache Spark UI, Apache Airflow, Dask Dashboard, Prefect, Temporal Web |
| Container / infra | etcd, Vault, Docker daemon, Kubernetes API, Consul, Portainer, Kubelet |
| Service mesh | Kiali, Hubble UI, Linkerd Viz, Linkerd Proxy Admin, Cilium Metrics, Istio Envoy Admin, Istiod Debug, Pomerium |
| Auth / policy | Open Policy Agent |
| BI / Dashboard | Metabase, Apache Superset, Redash, Grafana |
| Observability | Langfuse, Arize Phoenix, Helicone Self-Hosted, Lunary, OpenLIT, Pezzo, Prometheus |
| Workflow automation | n8n |
| Object storage | MinIO |
| Analytical datastores | ClickHouse, Elasticsearch, Apache Pinot, ScyllaDB REST |
| AI safety / eval | Promptfoo, NeMo Guardrails, DeepEval, LangSmith Self-Hosted, Inspect AI, Garak REST, Lakera Guard Self-Hosted, LLM Guard API |
| Voice / Audio AI | Whisper ASR, Coqui XTTS, Piper TTS, RVC Voice Cloning, OpenVoice, ChatTTS, F5-TTS, Pipecat, Vocode, LiveKit Agents, AI TTS Server |
| Medical AI / PACS | MONAI Label Server, Orthanc DICOM Server, dcm4che / dcm4chee-arc, DICOMweb (QIDO-RS) |
| Notebooks / dev | Jupyter Notebook, Open Directory, Docker Registry |
| Cross-cutting | Exposed API credentials (Langfuse, Helicone, Stripe, Anthropic, LangSmith, OpenRouter, Slack) |
62 of the 196 services have dedicated deep enumerators. They surface:
- PII fields in vector DB collections
- Unauthenticated model execution surfaces
- Exposed credentials in HTTP responses
- Claimable admin states (unconfigured Metabase, Flowise credential panels)
- Data counts, schema names, and experiment metadata
## JSON output shape
The `-o` flag writes a `ScanReport`:
tool string
version string
target string
timestamp string
ports_scanned int
open_ports []{host, port, open, tls, status_code, server, content_type}
services []{host, port, service, version, severity, base_url, match_path}
adjacencies []{...}
enum_results []{service, host, port, base_url, version, auth_status,
risk_level, details, findings[]{category, title, detail,
severity, data}, raw_data}
summary {total_targets, open_ports, services_found, unauthenticated,
total_findings, critical, high, medium, low, info,
scan_duration}
Risk levels: `critical`, `high`, `medium`, `low`, `info`. The escalation rule:
`auth == none` + `high` finding = `critical`.
## Example
█████╗ ██╗███╗ ███╗ █████╗ ██████╗
██╔══██╗██║████╗ ████║██╔══██╗██╔══██╗
███████║██║██╔████╔██║███████║██████╔╝
██╔══██║██║██║╚██╔╝██║██╔══██║██╔═══╝
██║ ██║██║██║ ╚═╝ ██║██║ ██║██║
╚═╝ ╚═╝╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═╝
AI Infrastructure Mapper v1.9.46
by NuClide
PHASE 1: PORT DISCOVERY
──────────────────────────────────────────────────────────
Scanning 192.0.2.0/24 (256 hosts)
Ports: 80,443,3000,...
Threads: 20
PHASE 2: AI SERVICE FINGERPRINTING
PHASE 3: DEEP ENUMERATION
The terminal output is colorized. JSON is stable across releases.
## Adding a fingerprint
1. Add a `Fingerprint` struct to `fingerprints.go`. Every probe must carry at
least `status_code` + `json_field` or `body_contains` conjuncts. A naked
single-word `body_contains` alone is unsound at population scale: false
positives fire on blog posts and marketing pages that mention the product name.
2. Optionally add an `enum` function to `enumerators.go` and wire it
in `runEnumerators`.
## Companion tool: aimap-profile
`aimap-profile/` is a single-file Python tool. Where aimap fingerprints services,
aimap-profile profiles the target: identity, WHOIS, ASN, TLS, category
(personal / institutional / commercial / research / honeypot), ethics flags
(HIPAA? CFAA exposure? safe harbor?), PTR neighborhood, and disclosure channels
(security.txt, bounty programs, abuse contacts). Emits structured JSON for
pipeline or LLM consumption.
See `aimap-profile/README.md` for details.
## What aimap is not
aimap does not authenticate to services, submit forms, POST data, execute
exploits, or modify anything on a target. All probes are read-only HTTP GETs and
TCP connects. It is active: it makes real connections. Only scan systems you own
or have explicit written authorization to test.
## License
MIT. Part of the NuClide toolchain. Contact: [nuclide-research.com](https://nuclide-research.com)
标签：EVTX分析