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HuggingFace User Access Token

A HuggingFace User Access Token is a credential used to authenticate and authorize access to HuggingFace's platform, which provides machine learning models and datasets. This token allows users to interact with HuggingFace's API, enabling actions such as downloading models, uploading datasets, and managing repositories. Exposure of this token is a significant security concern as it can lead to unauthorized access to sensitive data and resources, potentially compromising the integrity and confidentiality of machine learning projects.

How Does It Look​

User Access Tokens can appear in various contexts, such as:

  • Environment variables:

    export HF_USER_TOKEN="hf_xxxREDACTEDxxx"

  • Configuration files (JSON, YAML, .env):

    {
      "huggingface": {
        "user_token": "hf_xxxREDACTEDxxx"
      }
    }

  • Code snippets:

    import requests

    headers = {"Authorization": "Bearer hf_xxxREDACTEDxxx"}
    response = requests.get("https://api.huggingface.co/models", headers=headers)

  • Connection strings (if applicable): Not typically used in connection strings.

Severity​

  • 🟠 High

This severity level is due to the token's ability to provide access to a user's HuggingFace account, which may include private models, datasets, and other sensitive resources. The blast radius can be extensive if the token has permissions to modify or delete resources, potentially affecting multiple projects and collaborators.

What Can an Attacker Do?​

With immediate access to a HuggingFace User Access Token, an attacker can perform several actions:

An attacker can immediately access the HuggingFace API, potentially leading to unauthorized actions.

Key actions an attacker can perform:

  • Download private models and datasets (if the token has read permissions)
  • Delete or modify models and datasets (if the token has write permissions)
  • Upload malicious models (if the token allows uploads)
  • Access billing information (if the account has billing scope enabled)

Additionally, an attacker could use the token to escalate privileges or move laterally within the HuggingFace ecosystem, potentially compromising other connected services or accounts.

Real-World Impact​

The exposure of a HuggingFace User Access Token poses significant business risks, including:

The primary impact is the unauthorized access and potential misuse of machine learning resources.

Potential consequences include:

  • Data Exposure: Private models and datasets (if the credential has read access to sensitive data)
  • Financial Loss: Increased costs from unauthorized resource usage (if billing/resource creation is permitted)
  • Operational Disruption: Corruption or loss of critical models and datasets (if the attacker has delete/modify permissions)
  • Reputational Damage: Loss of trust from clients and partners due to data breaches

In a worst-case scenario, the exposure could lead to cascading effects, such as the compromise of downstream applications relying on HuggingFace models.

Prerequisites for Exploitation​

To exploit a HuggingFace User Access Token, an attacker needs:

  • Network access: Ability to send requests to the HuggingFace API
  • Additional context: Knowledge of specific endpoints or account details
  • Rate limits: Awareness of any API rate limits that might restrict actions

How to Verify If It's Active​

To verify if a HuggingFace User Access Token is active, use the following command:

curl -H "Authorization: Bearer [TOKEN]" https://api.huggingface.co/me

Valid credential response: A successful response will include user account details, such as username and email.

Invalid/expired credential response: An error message indicating unauthorized access or token expiration.

Detection Patterns​

Common Variable Names:

  • HF_USER_TOKEN
  • HUGGINGFACE_TOKEN
  • HF_ACCESS_TOKEN
  • HUGGINGFACE_API_KEY
  • USER_ACCESS_TOKEN
  • HF_TOKEN

File Locations:

  • .env
  • config.json
  • settings.yaml
  • credentials.txt
  • secrets.env

Regex Pattern:

hf_[a-zA-Z0-9]{20,}

Remediation Steps​

  1. Revoke immediately - Go to HuggingFace's account settings and revoke the compromised token.
  2. Audit access logs - Review HuggingFace API logs for unauthorized access or actions during the exposure window.
  3. Assess blast radius - Identify all systems, applications, and environments that used the exposed credential.
  4. Rotate credential - Generate a new User Access Token in HuggingFace with least-privilege permissions.
  5. Update dependent systems - Deploy the new credential to all applications and update CI/CD pipelines securely.
  6. Harden access controls - Enable IP allowlisting in HuggingFace and require two-factor authentication for account access.
  7. Implement secrets management - Migrate credentials to a secrets manager (HashiCorp Vault, AWS Secrets Manager) to prevent hardcoding.
  8. Add detection controls - Set up pre-commit hooks and repository scanning to catch credential leaks before they reach production.

Credential exposures often go undetected for extended periods, increasing the window for exploitation. As a long-term strategy, plan to establish an internal process or engage an external vendor for continuous external exposure monitoring. This helps identify leaked secrets across public repositories, paste sites, dark web forums, and other external sources before attackers can leverage them. Proactive detection and rapid response are essential to minimizing the impact of credential leaks.

References​