GHSA-3gm7-v7vw-866c GHSA-3gm7-v7vw-866c

XML External Entity (XXE) Injection in Apache Solr

High Known exploited Maven

Severity: 7.2 / 10; High · CVSS 3.1
Exploit chance (EPSS): 83.55%; Chance of exploitation in the next 30 days. Higher than 99.69% of scored vulnerabilities.
Known exploited: Yes; In CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: 8.2.0; Upgrade org.apache.solr:solr-core to this version or later

Description

In Apache Solr, the DataImportHandler, an optional but popular module to pull in data from databases and other sources, has a feature in which the whole DIH configuration can come from a request's "dataConfig" parameter. The debug mode of the DIH admin screen uses this to allow convenient debugging / development of a DIH config. Since a DIH config can contain scripts, this parameter is a security risk. Starting with version 8.2.0 of Solr, use of this parameter requires setting the Java System property "enable.dih.dataConfigParam" to true.

Affected versions

Maven

Package	Vulnerable	Fixed in
org.apache.solr:solr-core	`< 8.2.0`	`8.2.0`
org.apache.solr:solr-core	`< 8.2.0`	`8.2.0`

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.

org.apache.solr:solr-core Maven → `8.2.0`

maven

<dependency><groupId>org.apache.solr</groupId><artifactId>solr-core</artifactId><version>8.2.0</version></dependency>

gradle

implementation("org.apache.solr:solr-core:8.2.0")

Severity breakdown

CVSS 3.1 7.2

CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H/E:H

Attack vector: Network
Attack complexity: Low
Privileges required: High
User interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: High
Availability: High
Exploit maturity: H

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

Fixes and patches

github.com/apache/lucene-solr/commit/02c693f3713add1b4891cbaa87127de3a55c10f7

Packages

github.com/apache/lucene-solr

Timeline

Aug 1, 2019 Published
Jun 17, 2026 Last updated by the source

Advisory	Severity	Why it's related
CVE-2019-17558 Improper Input Validation in Apache Solr	High Known exploited	Also affects org.apache.solr:solr-core
GHSA-qhr7-h655-pw6r Apache Solr has hardcoded credentials in the Basic Authentication setup tool	Critical	Also affects org.apache.solr:solr-core
GHSA-vc2w-4v3p-2mqw Apache Solr: Insufficient file-access checking in standalone core-creation requests	High	Also affects org.apache.solr:solr-core
GHSA-qr3p-2xj2-q7hq Apache Solr: Unauthorized bypass of certain "predefined permission" rules in the RuleBasedAuthorizationPlugin	High	Also affects org.apache.solr:solr-core
GHSA-68r2-fwcg-qpm8 Apache Solr vulnerable to Execution with Unnecessary Privileges	Medium	Also affects org.apache.solr:solr-core
GHSA-4p5m-gvpf-f3x5 Apache Solr Relative Path Traversal vulnerability	Medium	Also affects org.apache.solr:solr-core
GHSA-37vr-vmg4-jwpw Apache Solr: Backup/Restore APIs allow for deployment of executables in malicious ConfigSets	High	Also affects org.apache.solr:solr-core
GHSA-4wxw-42wx-2wfx Apache Solr Schema Designer blindly "trusts" all configsets	High	Also affects org.apache.solr:solr-core

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