CVE-2022-43769
Hitachi Vantara Pentaho Business Analytics Server prior to versions 9.4.0.1 and 9.3.0.2, including 8.3.x allow certain web services to set property values…
High
Known exploited
- Severity
- 7.2 / 10
- High · CVSS 3.1
- Exploit chance (EPSS)
- 97.67%
- Chance of exploitation in the next 30 days. Higher than 99.90% of scored vulnerabilities.
- Known exploited
- Yes
- In CISA's catalog since Mar 3, 2025
- Weakness
- CWE-74 Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- Fix
- None yet
- No fixed version published
Exploited in the wild. Hitachi Vantara Pentaho BA Server Special Element Injection Vulnerability (Hitachi Vantara Pentaho Business Analytics (BA) Server). 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 Mar 24, 2025.
Description
Hitachi Vantara Pentaho Business Analytics Server prior to versions 9.4.0.1 and 9.3.0.2, including 8.3.x allow certain web services to set property values which contain Spring templates that are interpreted downstream.
How to fix
No fixed version has been published yet. Until one is, remove or replace the package, or turn off or isolate the affected feature. This page updates when a fix ships.
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
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- High
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
Weakness
CWE-74: Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component.
How to prevent it
- Requirements: Programming languages and supporting technologies might be chosen which are not subject to these issues.
- Implementation: Utilize an appropriate mix of allowlist and denylist parsing to filter control-plane syntax from all input.
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
Exploits and evidence
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-j8rh-479h-cp32 Axios: Header Injection via Inherited headers After Minimal Interceptor | Medium | Same weakness, CWE-74 |
| GHSA-4hqw-qxg8-jxx2 Axios: Fetch Adapter Header Injection via Inherited FormData getHeaders | Medium | Same weakness, CWE-74 |
| GHSA-h58c-xccx-75m3 Coder: Stored HTML injection via unescaped ApplicationName and LogoURL appearance settings | Medium | Same weakness, CWE-74 |
| GHSA-5rw4-4665-cvwf Froxlor DomainZones.add allows DNS zone-file RR injection via record/type fields | Medium | Same weakness, CWE-74 |
| GHSA-jm78-9fvv-mhgr GitPython: git-config OPTION-name injection via =/#/whitespace bypasses name validator, enabling forged core.sshCommand/hooksPath (RCE) | High | Same weakness, CWE-74 |
| EEF-CVE-2026-68747 CSS sanitizer allowlist bypass in html_sanitize_ex via non-declaration input | Medium | Same weakness, CWE-74 |
| GHSA-9473-5f9j-94wq Nuxt: Server-Side Remote Code Execution via Runtime Template Injection in Nuxt Server Island Props | High | Same weakness, CWE-74 |
| GHSA-v3r7-h72x-cjcm undici vulnerable to cookie attribute injection via unsanitized domain and unparsed setCookie fields | Medium | Same weakness, CWE-74 |
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