GHSA-363w-hvwh-w7m6 CVE-2026-45719
Budibase: CouchDB Reduce Injection via Unsanitized Calculation Parameter in V1 Views API
- Severity
- 6.5 / 10
- Medium · CVSS 3.1
- Exploit chance (EPSS)
- 0.26%
- Chance of exploitation in the next 30 days. Higher than 18.15% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- Fix
3.38.1- Upgrade @budibase/server to this version or later
Description
Security Advisory: CouchDB Reduce Injection via Unsanitized Calculation Parameter in V1 Views API
Affected Software: Budibase
Affected Component: packages/server/src/api/controllers/view/viewBuilder.ts, packages/server/src/api/routes/view.ts
CWE: CWE-94 (Improper Control of Generation of Code)
Discovery Date: 2026-03-24
Summary
The V1 Views API (POST /api/views) accepts a calculation parameter from the request body that is interpolated directly into a CouchDB reduce function definition without validation. Although an internal SCHEMA_MAP object defines the valid calculation types (sum, count, stats), no actual validation is performed against this map before the value is used in string interpolation.
A user with Builder permissions can inject arbitrary JavaScript code that will be executed within the CouchDB JavaScript engine when the view is queried.
Affected Component
Route: POST /api/views (V1 legacy views endpoint)
File: packages/server/src/api/routes/view.ts, line 45
.post("/api/views", viewController.v1.save)
Note: This route has no Joi request body validator, unlike the V2 views endpoint which uses viewValidator().
Vulnerable code: packages/server/src/api/controllers/view/viewBuilder.ts, line 213
const reduction = field && calculation ? { reduce: `_${calculation}` } : {}
return {
meta: { field, tableId, groupBy, filters, schema, calculation, ... },
map: `function (doc) { ... }`,
...reduction, // <-- unvalidated calculation string becomes CouchDB reduce
}
Vulnerability Detail
The viewBuilder function constructs a CouchDB design document view definition. It correctly sanitizes all inputs that flow into the map function string (using JSON.stringify for field names and a strict TOKEN_MAP allowlist for filter operators).
However, the calculation parameter follows a different path:
- User submits
calculationviaPOST /api/viewsrequest body - No Joi validator is present on this V1 route
viewBuilderreceivescalculationas a raw string- It is interpolated as:
reduce: `_${calculation}` - This reduce definition is saved to a CouchDB design document
- When the view is queried, CouchDB evaluates the reduce value
CouchDB's behavior for reduce functions:
- Values starting with
_followed by a known built-in (_sum,_count,_stats) are executed as native reducers - Any other value is treated as a JavaScript function string and executed in CouchDB's SpiderMonkey JS engine
The SCHEMA_MAP object in the same file defines sum, count, and stats as valid keys, but this map is only used for schema construction — it is never used as an input validator for the calculation parameter.
Steps to Reproduce
Prerequisites: Authenticated session with Builder role permissions.
1. Send a crafted view creation request:
curl -X POST https://<budibase-instance>/api/views \
-H "Content-Type: application/json" \
-H "Cookie: <builder-session-cookie>" \
-d '{
"name": "test_view",
"tableId": "<valid-table-id>",
"field": "amount",
"calculation": "stats\"); } function(keys,values,rereduce){ var data = \"\"; for(var i in this) { data += i + \"=\" + this[i] + \",\"; } return data; } //"
}'
2. Query the created view:
curl https://<budibase-instance>/api/views/test_view?group=true \
-H "Cookie: <builder-session-cookie>"
3. Expected result: The injected JavaScript function executes in CouchDB's JS context during reduce evaluation. The function can:
- Enumerate objects available in the CouchDB sandbox
- Access document data from the reduce
valuesparameter - Return arbitrary data in the view response
Simplified test: To verify the injection point without complex payloads:
{
"name": "calc_test",
"tableId": "<valid-table-id>",
"field": "amount",
"calculation": "INVALID_NOT_A_BUILTIN"
}
This produces reduce: "_INVALID_NOT_A_BUILTIN". CouchDB will reject this as neither a valid built-in nor a valid function, confirming that arbitrary strings reach the reduce evaluator.
Impact
- Code execution: Arbitrary JavaScript runs in CouchDB's SpiderMonkey sandbox
- Data access: The reduce function receives all matching document values, allowing data exfiltration across the database
- Scope limitation: CouchDB's JS sandbox prevents filesystem or network access — this is not OS-level RCE
- Authentication required: Attacker must have Builder role, which already grants significant application access
- Persistence: The injected reduce function persists in the design document and executes on every view query
Recommended Fix
Add an allowlist validation in viewBuilder before the reduce interpolation:
const VALID_CALCULATIONS = ["sum", "count", "stats"];
if (calculation && !VALID_CALCULATIONS.includes(calculation)) {
throw new Error(`Invalid calculation type: ${calculation}`);
}
const reduction = field && calculation ? { reduce: `_${calculation}` } : {};
Additionally, add a Joi validator to the V1 views route to match the V2 endpoint:
// In packages/server/src/api/routes/view.ts
.post("/api/views", v1ViewValidator(), viewController.v1.save)
Additional Context
The V2 views API (POST /api/v2/views) uses viewValidator() with Joi schema validation and a separate calculation handling path. This finding is specific to the V1 legacy endpoint which lacks equivalent input validation.
The map function string in the same code is properly protected — all user inputs reaching it are escaped via JSON.stringify() or validated against a strict TOKEN_MAP allowlist. Only the reduce path is affected.
Affected versions
npm
| Package | Vulnerable | Fixed in |
|---|---|---|
| @budibase/server | < 3.38.1 |
3.38.1 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
@budibase/server npm → 3.38.1
- npm
npm install @budibase/server@3.38.1- yarn
yarn add @budibase/server@3.38.1- pnpm
pnpm add @budibase/server@3.38.1
Severity breakdown
CVSS 3.1 6.5
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- High
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- None
Weakness
CWE-94: Improper Control of Generation of Code ('Code Injection')
The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.
How to prevent it
- Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
- Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
- Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
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