GHSA-wg6p-jmpc-xjmr CVE-2023-35926
Backstage Scaffolder plugin has insecure sandbox
- Severity
- 8.0 / 10
- Critical · CVSS 3.1
- Exploit chance (EPSS)
- 1.89%
- Chance of exploitation in the next 30 days. Higher than 79.53% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- Fix
1.15.0- Upgrade @backstage/plugin-scaffolder-backend to this version or later
Description
The Backstage scaffolder-backend plugin uses a templating library that requires a sandbox, as it by design allows for code injection. The library used for this sandbox so far has been vm2, but in light of several past vulnerabilities and existing vulnerabilities that may not have a fix, the plugin has switched to using a different sandbox library.
Impact
A malicious actor with write access to a registered scaffolder template could manipulate the template in a way that allows for remote code execution on the scaffolder-backend instance. This was only exploitable in the template YAML definition itself and not by user input data.
Patches
This is vulnerability is fixed in version 1.15.0 of @backstage/plugin-scaffolder-backend.
Workarounds
Note that the Backstage Threat Model states that scaffolder templates are considered to be a sensitive area that with the recommendation that you control access and perform manual reviews of changes to the scaffolder templates. The exploit is of a nature where it is easily discoverable in manual review.
Affected versions
npm
| Package | Vulnerable | Fixed in |
|---|---|---|
| @backstage/plugin-scaffolder-backend | < 1.15.0 |
1.15.0 |
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.
@backstage/plugin-scaffolder-backend npm → 1.15.0
- npm
npm install @backstage/plugin-scaffolder-backend@1.15.0- yarn
yarn add @backstage/plugin-scaffolder-backend@1.15.0- pnpm
pnpm add @backstage/plugin-scaffolder-backend@1.15.0
Severity breakdown
CVSS 3.1 8.0
CVSS:3.1/AV:N/AC:H/PR:H/UI:N/S:C/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- High
- Privileges required
- High
- User interaction
- None
- Scope
- Changed
- Confidentiality
- High
- Integrity
- High
- Availability
- High
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
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-vwp5-f99x-x3rq Backstage has improper input validation in scaffolder task list ordering | Medium | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-4xvq-3m68-h444 Backstage has sensitive information exposure in scaffolder task logs | Medium | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-hmp2-4m7g-22cv Backstage: Scaffolder action input authorization bypass | High | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-95p4-vv4g-jxxm Backstage may expose sensitive information in Scaffolder task failure events | Medium | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-g2v8-7jhw-pp8p Backstage: Sensitive information exposure in Scaffolder | Critical | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-xvgh-hmx8-9xxf Backstage: Improper task state validation in Scaffolder backend | High | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-mfvq-x7vr-rgqg Backstage: Secret-derived values may be exposed in scaffolder task logs | Medium | Also affects @backstage/plugin-scaffolder-backend |
| GHSA-29gx-h2m3-xw44 Backstage's scaffolder credential handling may allow unintended GitHub authentication fallback | Medium | Also affects @backstage/plugin-scaffolder-backend |
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