Arp Spoofing in Huggingface

ARP spoofing is a layer‑2 attack where an adversary on the same LAN sends forged ARP replies, tricking hosts into sending traffic intended for a legitimate IP address to the attacker’s MAC address. In the context of Huggingface services, this can expose any unencrypted HTTP traffic that flows between a client (e.g., a CI runner, a local notebook, or an internal micro‑service) and Huggingface endpoints such as the Inference API, the Model Hub, or private inference endpoints.

When a developer uses the Huggingface Hub or Inference APIs over plain HTTP (or when an internal service mistakenly resolves api-inference.huggingface.co to an internal IP without TLS), an attacker who has successfully poisoned the ARP cache can intercept those requests. The intercepted traffic may contain:

• Model weights or configuration files pulled from huggingface_hub.
• Prompts, completion results, or sensitive data sent to an inference endpoint.
• Bearer tokens used for authentication (if they are transmitted in headers over HTTP).

Consider the following insecure snippet that explicitly forces an HTTP request to the Huggingface inference service:

import os
import requests

# ❌ Insecure: uses HTTP instead of HTTPS
def query_model_http(prompt):
    url = 'http://api-inference.huggingface.co/models/gpt2'
    headers = {
        'Authorization': f'Bearer {os.getenv("HF_TOKEN")}'
    }
    payload = {"inputs": prompt}
    resp = requests.post(url, json=payload, headers=headers)
    return resp.json()

print(query_model_http('Hello world'))

If an attacker on the same network performs ARP spoofing against the victim’s machine, the HTTP request (including the Authorization header) will be sent to the attacker instead of the genuine Huggingface endpoint, leading to token theft and potential model exfiltration.

middleBrick performs unauthenticated, black‑box scanning of any API endpoint you submit. When scanning a Huggingface‑related URL, it checks for:

• Exposed HTTP endpoints (no TLS) that could be sniffed via ARP spoofing.
• Missing or weak authentication on inference or model‑hub routes.
• Responses that leak tokens, API keys, or PII (which would be valuable to an attacker who has intercepted traffic).
• Whether the service advertises strict transport security (HSTS) or forces HTTPS redirects.

For example, running the middleBrick CLI against a suspected insecure inference endpoint will produce a finding if the service answers over plain HTTP:

middlebrick scan http://api-inference.huggingface.co/models/distilbert-base-uncased-finetuned-sst-2-english

The resulting report will list a “Data Exposure” finding with severity “High”, note that the endpoint is accessible without TLS, and provide remediation guidance such as “Enforce HTTPS and redirect all HTTP traffic to HTTPS”.

Similarly, scanning a private Huggingface Inference Endpoint that is reachable only via an internal IP will reveal whether the endpoint is exposed to the local network without proper network segmentation—a condition that makes ARP spoofing feasible.

The primary defense against ARP spoofing is to ensure that all traffic to Huggingface services is encrypted and authenticated, making intercepted packets useless to an attacker. Below are concrete, Huggingface‑native ways to achieve this.

1. Enforce HTTPS and Verify Certificates

Always use the default HTTPS endpoints provided by the Huggingface libraries. If you must construct requests manually, verify the TLS certificate (the default in requests is to verify).

import os
import requests

# ✅ Secure: uses HTTPS and validates the certificate
def query_model_https(prompt):
    url = 'https://api-inference.huggingface.co/models/gpt2'
    headers = {
        'Authorization': f'Bearer {os.getenv("HF_TOKEN")}'
    }
    payload = {"inputs": prompt}
    resp = requests.post(url, json=payload, headers=headers, timeout=10)
    resp.raise_for_status()  # will raise on HTTP errors or TLS failures
    return resp.json()

print(query_model_https('Hello world'))

2. Use Huggingface Hub with Token‑Based Auth

The huggingface_hub library automatically adds the Authorization bearer token when you log in, and it always communicates over HTTPS.

from huggingface_hub import HfApi, login
import os

# Login once (token from environment)
login(token=os.getenv('HF_TOKEN'), add_to_git_credential=False)

api = HfApi()  # defaults to https://huggingface.co
# ✅ All subsequent calls (upload, download, repo info) are HTTPS‑protected
model_url = api.model_info('gpt2').sha
print(f'Model SHA: {model_url}')

3. Prefer Private Inference Endpoints with Network Isolation

For enterprise workloads, deploy Huggingface Inference Endpoints inside a VPC and enable AWS PrivateLink, Azure Private Link, or GCP Private Service Connect. This removes the endpoint from the public internet and confines it to a trusted network where ARP spoofing is only possible if an attacker already has a foothold inside the VPC—something that should be mitigated by network segmentation, MAC‑based port security, and dynamic ARP inspection (DAI) on switches.

When creating a private endpoint via the Huggingface UI or API, specify private=true and provide the VPC subnet IDs. The resulting endpoint URL will resolve only within the VPC, and traffic never traverses the local LAN where an ARP spoofing attempt could succeed.

4. Monitor and Rotate Tokens Frequently

Even with encryption, limit the blast radius of a leaked token by using fine‑grained scoped tokens (read‑only for model download, write‑only for specific repos) and rotating them on a regular schedule (e.g., every 30 days). The Huggingface settings page lets you generate new tokens and revoke old ones.