XWiki Platform Wiki UI Main Wiki is software for managing subwikis on XWiki Platform, a generic wiki platform. Starting with version 5.3-milestone-2 and prior to versions 13.10.6 and 14.4, its possible to inject arbitrary wiki syntax including Groovy, Python and Velocity script macros via the request (URL parameter) using the XWikiServerClassSheet if the user has view access to this sheet and another page that has been saved with programming rights, a standard condition on a public read-only XWiki installation or a private XWiki installation where the user has an account. This allows arbitrary Groovy/Python/Velocity code execution which allows bypassing all rights checks and thus both modification and disclosure of all content stored in the XWiki installation. Also, this could be used to impact the availability of the wiki. This has been patched in versions 13.10.6 and 14.4. As a workaround, edit the affected document XWiki.XWikiServerClassSheet or WikiManager.XWikiServerClassSheet and manually perform the changes from the patch fixing the issue. On XWiki versions 12.0 and later, it is also possible to import the document XWiki.XWikiServerClassSheet from the xwiki-platform-wiki-ui-mainwiki package version 14.4 using the import feature of the administration application as there have been no other changes to this document since XWiki 12.0.
Weakness
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.
Affected Software
| Name |
Vendor |
Start Version |
End Version |
| Xwiki |
Xwiki |
5.4 (including) |
13.10.6 (excluding) |
| Xwiki |
Xwiki |
14.0 (including) |
14.4 (excluding) |
| Xwiki |
Xwiki |
5.3-milestone2 (including) |
5.3-milestone2 (including) |
Potential Mitigations
- 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.
- 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().
- 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