Revoke the suspected Hugging Face token immediately, then investigate what it could access. Review account and organization activity, rotate any other exposed credentials, and contact Hugging Face if you suspect misuse. If you are locked out because of lost two-factor authentication (2FA), use one unused recovery code or request identity-verified help from support.
First, contain the compromise
Stop using the account, machine, notebook, CI job, or agent that may have handled the secret. Do not allow the suspected agent to make another API request while you investigate. If the token is stored in an environment variable, secret manager, workflow file, shell profile, notebook, prompt, or log, treat every copy as exposed until you have replaced it.
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1. Revoke the affected token
In Hugging Face, open your account settings, go to Access Tokens, identify the token used by the agent, and delete or refresh it. Revocation prevents that token from continuing to authenticate. If you cannot identify the token, revoke each token that the agent could have read rather than waiting for more evidence.
Hugging Face also documents a leaked-credential revocation endpoint. It invalidates a matching raw token immediately everywhere, including a token belonging to another user. The endpoint returns 202 Accepted whether the supplied value existed or not, so the response must not be treated as a validity check. Never paste a live token into shell history, an issue, a chat transcript, or a public report. Pass it through a protected environment variable or file as described in Hugging Face’s token documentation.
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2. Determine which identity the local CLI uses
On every machine where the agent ran, execute:
hf auth whoamiThis reports the active Hugging Face identity for the local CLI session. Check the environment, credential store, notebook secrets, CI variables, and container layers as well. Remove the old credential from places the agent can still read, then replace it with a newly generated token only after the investigation has been contained.
Understand what the token could do
The consequences depend on both the token type and the account or organization permissions behind it. A stolen read token may expose private repositories that your user or organization membership allows you to read; a write token may also permit repository changes. A fine-grained token can be limited to selected resources and is the safer choice for production automation.
| Token type | What it generally permits | Response priority |
|---|---|---|
| Read | Reading repositories and resources available to the user or organization | Revoke promptly; inspect private data access and downloads |
| Write | Read access plus writes available to that user, such as repository updates | Revoke immediately; inspect commits, files, settings, and automation |
| Fine-grained | Access restricted to selected resources and permissions | Revoke immediately, then verify the allowed resource list |
Permission labels are not a complete incident report. A read token can still expose sensitive private material, and a fine-grained token may cover a critical production repository. Record the token name, creation or last-use information shown in settings, scope, associated application, and the time window in which the agent could access it.
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Personal account checks
Look for password, email, username, 2FA, token, session, and profile changes that you did not make. Review new or modified repositories, unexpected commits, changed visibility, altered collaborators, unfamiliar webhooks, and unusual downloads or API activity. Preserve timestamps, repository names, commit identifiers, and screenshots or exported records before changing evidence.
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Organization checks
Ask an organization administrator to inspect token usage, membership changes, permission changes, repository settings, and available audit logs. Audit logs can include security and access-management events such as token approvals and organization token rotations. Your plan and role determine which records are visible, so note both what you checked and what was unavailable.
Pay attention to the difference between organization denial and revocation. Denial belongs to an approval workflow and may later be reversed. Enterprise organization revocation permanently blocks that token from the organization, but neither action necessarily stops the same token from accessing unrelated resources. A leaked credential that needs global invalidation requires the global revocation process or deletion by its owner.
Rotate every credential that may have been exposed
Hugging Face recommended rotating access tokens and reviewing recent activity after its July 2026 incident. Rotate any other Hugging Face token the agent could read, even if you have not observed suspicious use. If the same secret was reused on another service, rotate it there independently: revoking a Hugging Face token does not revoke a separate credential with the same value or an equivalent privilege.
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- Update CI variables, secret stores, notebooks, containers, deployment manifests, and local credential files.
- Invalidate cached sessions or credentials on machines that were accessible to the agent.
- Search private repositories and artifacts for hard-coded tokens, then remove the secret and rewrite affected history where appropriate.
- Record the old token’s retirement time and the new token’s intended use.
Do not assume that rebuilding a container alone removes a secret. A token can remain in an image layer, a copied notebook, a workflow log, or an artifact cache.
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Recover the account if 2FA or the password is unavailable
Using a recovery code
Use one unused 2FA recovery code at the normal sign-in challenge. Each recovery code works once. If you regenerate the recovery-code set, all earlier codes become invalid, so store the new set securely before signing out again.
When both the password and authenticator are lost
Contact [email protected] and request identity-verified recovery. Hugging Face says an SSH key or personal access token can serve as a recovery factor. Do not send a live token in the message; provide only the account or organization identifier and the information support requests for verification.
When you suspect active misuse
Contact [email protected] if you believe the account was affected or need to report a security concern. Include your account or organization identifier, approximate time window, affected repositories, and what the agent could access. Keep secrets out of the report. Support or security staff can correlate your information with records that are not visible in the normal account interface.
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Hugging Face published its “Security incident disclosure — July 2026” on July 16, 2026. The company said an autonomous AI-agent system entered part of its production infrastructure through code execution in dataset processing. Its description involved a malicious dataset using a remote-code dataset loader and a template-injection path in a dataset configuration; the actor then escalated access and harvested cloud and cluster credentials.
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Hugging Face reported unauthorized access to a limited set of internal datasets and several service credentials. It said assessment of possible partner or customer impact was continuing and that affected parties would be contacted as required. The disclosure says its analysis agents processed more than 17,000 recorded events and describes a swarm of tens of thousands of automated actions; the latter phrase is not a precise event count.
The company also reported no evidence of tampering with public user-facing models, datasets, or Spaces, and said container images and published packages were verified clean. Its response included closing the code-execution paths, removing the foothold, rebuilding affected nodes, revoking and rotating credentials, and expanding precautionary secret rotation.
Those are company-reported infrastructure findings. They do not prove that a particular Hugging Face account was accessed, nor do they prove that an individual account was unaffected. Only your activity records and direct communication with Hugging Face can resolve that question.
How to investigate an AI agent specifically
Map the agent’s access
Write down the model or agent name, host machine, container or virtual environment, tools enabled, prompts or files supplied, and every secret source available to it. Include inherited environment variables, mounted directories, browser sessions, CLI credential stores, cloud metadata, and organization credentials. The aim is to define the maximum possible access, not merely the actions visible in the model transcript.
Preserve evidence before cleanup
Save agent transcripts, tool-call logs, shell history, CI logs, notebook outputs, network or proxy records, repository audit entries, and timestamps in a restricted location. Redact tokens before sharing the material. If logs contain a secret, treat that secret as compromised and rotate it; do not circulate the unredacted copy.
Check for indirect changes
An agent may not need to edit a repository directly to cause harm. Look for newly created access grants, modified workflow files, changed release artifacts, altered model cards, hidden files, webhooks, scheduled jobs, and copied data in temporary storage. Compare the affected repository with a trusted revision and review changes made during the agent’s access window.
Design safer Hugging Face automation afterward
Use the narrowest permission
Use a read token for retrieval-only work. For production workflows that need writes, use a fine-grained token restricted to the exact repositories and operations required. Hugging Face states: “We also recommend using only fine-grained tokens for production usage.” Treat that as a risk-reduction measure, not as a guarantee against prompt injection or malicious tool use.
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Create a distinct token for each agent, application, environment, and major workflow. A separate token lets you disable one integration without interrupting unrelated jobs and makes activity attribution clearer. Never give a development agent the same credential used by a production deployment.
Keep secrets out of prompts and source code
Do not embed tokens in system prompts, source files, notebooks, command arguments, issue text, or configuration committed to a repository. Command-line arguments can appear in process listings or shell history, and verbose agent logs can capture environment values. Prefer a secret manager or protected runtime injection with the minimum lifetime and scope practical.
Use organization-owned automation identities
Where available, organization service accounts separate automation from individual members. Administrators can scope and rotate their fine-grained tokens independently, reducing the disruption caused by an employee account change and making ownership clearer when an agent runs unattended.
Protect 2FA recovery
Enable 2FA on the account, store recovery codes in a protected location, and test that you can retrieve them without placing them in an agent-readable directory. Remember that every code is single-use and that generating a new set invalidates the old set.
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Quick Recap
A practical incident checklist
- Stop the suspected agent and isolate the machine, workspace, or workflow that supplied its credentials.
- Run
hf auth whoamion relevant hosts to identify active CLI identities. - Delete or refresh the affected token in Access Tokens settings.
- Use the documented global revocation method when a raw token must be invalidated everywhere; never expose the raw value in logs or reports.
- Review personal account, repository, organization, and audit-log activity for the access window.
- Rotate every other token or reused secret the agent could have read.
- Use a recovery code or identity-verified support if 2FA or password recovery is blocked.
- Report suspected impact to
[email protected]without including live credentials. - Rebuild the agent workflow with separate, fine-grained credentials and read-only access where possible.
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