GHSA-rr6p-3pfg-562j CVE-2025-24893
XWiki Platform allows remote code execution as guest via SolrSearchMacros request
Critical
Known exploited
Maven
- Severity
- 9.8 / 10
- Critical · CVSS 3.1
- Exploit chance (EPSS)
- 99.97%
- Chance of exploitation in the next 30 days. Higher than 99.98% of scored vulnerabilities.
- Known exploited
- Yes
- In CISA's catalog since Oct 30, 2025
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
- Fix
15.10.11- Upgrade org.xwiki.platform:xwiki-platform-search-solr-ui to this version or later
Exploited in the wild. XWiki Platform Eval Injection Vulnerability (XWiki Platform). Required action: Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable. Due for U.S. federal agencies by Nov 20, 2025.
Description
Impact
Any guest can perform arbitrary remote code execution through a request to SolrSearch. This impacts the confidentiality, integrity and availability of the whole XWiki installation.
To reproduce on an instance, without being logged in, go to <host>/xwiki/bin/get/Main/SolrSearch?media=rss&text=%7D%7D%7D%7B%7Basync%20async%3Dfalse%7D%7D%7B%7Bgroovy%7D%7Dprintln%28"Hello%20from"%20%2B%20"%20search%20text%3A"%20%2B%20%2823%20%2B%2019%29%29%7B%7B%2Fgroovy%7D%7D%7B%7B%2Fasync%7D%7D%20. If there is an output, and the title of the RSS feed contains Hello from search text:42, then the instance is vulnerable.
Patches
This vulnerability has been patched in XWiki 15.10.11, 16.4.1 and 16.5.0RC1.
Workarounds
This line in Main.SolrSearchMacros can be edited to match the rawResponse macro defined here with a content type of application/xml, instead of simply outputting the content of the feed.
References
- https://jira.xwiki.org/browse/XWIKI-22149
- https://github.com/xwiki/xwiki-platform/commit/67021db9b8ed26c2236a653269302a86bf01ef40
Attribution
This vulnerability has been reported by John Kwak for Trend Micro's Zero Day Initiative.
Affected versions
Maven
| Package | Vulnerable | Fixed in |
|---|---|---|
| org.xwiki.platform:xwiki-platform-search-solr-ui | >= 5.3-milestone-2, < 15.10.11 |
15.10.11 |
| org.xwiki.platform:xwiki-platform-search-solr-ui | >= 16.0.0-rc-1, < 16.4.1 |
16.4.1 |
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.
org.xwiki.platform:xwiki-platform-search-solr-ui Maven → 15.10.11
- maven
<dependency><groupId>org.xwiki.platform</groupId><artifactId>xwiki-platform-search-solr-ui</artifactId><version>15.10.11</version></dependency>- gradle
implementation("org.xwiki.platform:xwiki-platform-search-solr-ui:15.10.11")
Severity breakdown
CVSS 3.1 9.8
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- User interaction
- None
- Scope
- Unchanged
- 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].
CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. "eval").
How to prevent it
- Architecture and Design, Implementation: If possible, refactor your code so that it does not need to use eval() at all.
- 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.
- Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
- 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
Fixes and patches
Packages
Other links
Timeline
- Published
- Added to CISA's Known Exploited Vulnerabilities catalog
- Remediation due for U.S. federal agencies
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-26p8-2jq9-3vq9 MLflow vulnerable to remote code execution on unauthenticated servers via third-party scorer deserialization importing caller-placed artifact modules | Critical | Same weakness, CWE-94 |
| GHSA-h3rc-6mm3-gc2m vLLM: Request-controlled mm_processor_kwargs.code_revision allows remote code execution | Critical | Same weakness, CWE-94 |
| GHSA-qxg6-89mg-rqv8 Vitest: Code injection via unauthenticated manual mock registration | Medium | Same weakness, CWE-94 |
| CVE-2026-0322 A remote code execution (RCE) vulnerability in the management interface of Palo Alto Networks PAN-OS® software enables an authenticated attacker with network… | High Known exploited | Same weakness, CWE-94 |
| GHSA-p393-cf76-4jmr Nginx UI: Authenticated Remote Code Execution via Backup Restore App Config Overwrite | High | Same weakness, CWE-94 |
| GHSA-8r5x-fm3f-whwj Handlebars: JavaScript Injection via AST Type Confusion in compile (bypass of CVE-2026-33937) | Critical | Same weakness, CWE-94 |
| GHSA-97rh-rhh2-7vjv Payload: Remote Code Execution through first-register | High | Same weakness, CWE-94 |
| GHSA-27vj-qcqg-25rc fsspec: Server-Side Template Injection in ReferenceFileSystem leads to Remote Code Execution | High | Same weakness, CWE-94 |
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