Common Weakness Enumeration
CWE-94
Allowed-with-ReviewImproper Control of Generation of Code ('Code Injection')
Abstraction: Base · Status: Draft
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.
8982 vulnerabilities reference this CWE, most recent first.
CVE-2026-42214 (GCVE-0-2026-42214)
Vulnerability from cvelistv5 – Published: 2026-05-07 18:14 – Updated: 2026-05-09 03:55
VLAI
EPSS
VEX
Title
Improper Control of Generation of Code ('Code Injection') in dail8859/NotepadNext
Summary
Notepad Next is a cross-platform, reimplementation of Notepad++. Prior to version 0.14, NotepadNext's detectLanguageFromExtension() function interpolates a file's extension directly into a Lua script without sanitization. An attacker can craft a filename whose extension contains Lua code, which executes automatically when the victim opens the file in NotepadNext. Because luaL_openlibs() is called unconditionally, the full os, io, and package libraries are available to the injected code, enabling arbitrary command execution. This issue has been patched in version 0.14.
Severity
7.8 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-08 00:00 UTC
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/dail8859/NotepadNext/security/… | x_refsource_CONFIRM |
| https://github.com/dail8859/NotepadNext/commit/f3… | x_refsource_MISC |
| https://github.com/dail8859/NotepadNext/releases/… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| dail8859 | NotepadNext |
Affected:
< 0.14
|
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CVE-2026-42090 (GCVE-0-2026-42090)
Vulnerability from cvelistv5 – Published: 2026-05-04 16:43 – Updated: 2026-05-05 03:56
VLAI
EPSS
VEX
Title
Notesnook: RCE via stored XSS in note export rendering
Summary
Notesnook is a note-taking app focused on user privacy & ease of use. Prior to Notesnook Web/Desktop version 3.3.15 and prior to Notesnook iOS/Android version 3.3.20, a stored XSS vulnerability in the note export flow can be escalated to remote code execution in the desktop app. The root cause is that exported note fields such as title, headline, and content are inserted into the generated HTML template without HTML escaping. When the note is later exported to PDF, Notesnook renders that HTML into a same-origin, unsandboxed iframe using iframe.srcdoc = .... Injected script executes in the Notesnook origin. In the desktop app, this becomes RCE because Electron is configured with nodeIntegration: true and contextIsolation: false. This issue has been patched in Notesnook Web/Desktop version 3.3.15 and Notesnook iOS/Android version 3.3.20.
Severity
9.6 (Critical)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-04 00:00 UTC
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/streetwriters/notesnook/securi… | x_refsource_CONFIRM |
| https://github.com/streetwriters/notesnook/releas… | x_refsource_MISC |
| https://github.com/streetwriters/notesnook/releas… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| streetwriters | notesnook |
Affected:
Notesnook Web/Desktop < 3.3.15
Affected: Notesnook iOS/Android < 3.3.20 |
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CVE-2026-42049 (GCVE-0-2026-42049)
Vulnerability from cvelistv5 – Published: 2026-07-14 21:44 – Updated: 2026-07-15 13:26
VLAI
EPSS
VEX
Title
jadx: RCE Via Groovy Code Injection in Gradle Export
Summary
jadx is a Dex to Java decompiler. Prior to 1.5.6, jadx inserts the android:versionName value from an AndroidManifest into the generated app/build.gradle Groovy template without proper sanitization when exporting a decompiled APK as an Android Gradle project. A malicious APK can break out of the string context so that opening or building the exported Gradle project executes attacker-controlled Groovy code on the victim machine. This issue is fixed in version 1.5.6.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-07-15 13:20 UTC
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/skylot/jadx/security/advisorie… | x_refsource_CONFIRM |
| https://github.com/skylot/jadx/commit/5a6e660b466… | x_refsource_MISC |
| https://github.com/skylot/jadx/releases/tag/v1.5.6 | x_refsource_MISC |
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CVE-2026-41900 (GCVE-0-2026-41900)
Vulnerability from cvelistv5 – Published: 2026-05-08 03:25 – Updated: 2026-05-08 12:54
VLAI
EPSS
VEX
Title
OpenLearnX has Critical Remote Code Execution Through Python Sandbox Escape via Code Execution Environment
Summary
OpenLearnX is an open-source, decentralized learning and assessment platform. Prior to version 2.0.3, a remote code execution (RCE) vulnerability was identified in the OpenLearnX code execution environment, allowing sandbox escape and arbitrary command execution. This issue has been patched in version 2.0.3.
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-08 12:53 UTC
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/th30d4y/OpenLearnX/security/ad… | x_refsource_CONFIRM |
| https://github.com/th30d4y/OpenLearnX/commit/1476… | x_refsource_MISC |
| https://github.com/th30d4y/OpenLearnX/releases/ta… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| th30d4y | OpenLearnX |
Affected:
< 2.0.3
|
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CVE-2026-41870 (GCVE-0-2026-41870)
Vulnerability from cvelistv5 – Published: 2026-09-09 10:43 – Updated: 2026-09-10 18:03
VLAI
EPSS
VEX
Title
Apache Nutch: Unauthenticated remote code execution (RCE) via JEXL injection in Nutch Server (Nutch REST API)
Summary
Missing Authorization, Improper Control of Generation of Code ('Code Injection'), Improper Control of Dynamically-Managed Code Resources, Use of Externally-Controlled Input to Select Classes or Code ('Unsafe Reflection') vulnerability in Apache Nutch Server (Nutch REST API).
This issue affects Apache Nutch: from 1.11 through 1.22.
Users are recommended to upgrade to version 1.23, which removes the Nutch Server.
If an upgrade is not possible, user must restrict access to instances running the Nutch Service to trusted users only.
Please, also visit the Apache Nutch security advisories https://nutch.apache.org/documentation/security/ .
Severity
8.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-10 18:02 UTC
CWE
Assigner
References
2 references
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Apache Software Foundation | Apache Nutch |
Affected:
1.11 , ≤ 1.22
(custom)
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CVE-2026-41692 (GCVE-0-2026-41692)
Vulnerability from cvelistv5 – Published: 2026-05-07 20:01 – Updated: 2026-05-11 18:04
VLAI
EPSS
VEX
Title
i18nextify is vulnerable to DOM XSS via javascript:/data: URL schemes in translated href/src attributes
Summary
i18nextify is a JavaScript library that adds website internationalization via a script tag, without source code changes. Versions prior to 4.0.8 substitute {{key}} interpolation tokens inside src and href attribute values with the raw string returned by i18next.t(). The substitution logic in src/localize.js (the replaceInside handler) only guards against a duplicated http:// origin prefix — it does not validate the URL scheme of the substituted value. A translated value such as javascript:alert(1) or data:text/html,<script>...</script> is applied unchanged to the live DOM attribute when an attacker can influence the content of a translation file or the translation-backend response — for example, via a compromised translation CDN, user-contributed locales, a MITM on a plain-HTTP backend, or write access to the translation JSON. This issue was patched in version 4.0.8.
Severity
4.7 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-11 18:03 UTC
CWE
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/i18next/i18nextify/security/ad… | x_refsource_CONFIRM |
| https://github.com/i18next/i18nextify/commit/16f2… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| i18next | i18nextify |
Affected:
< 4.0.8
|
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CVE-2026-41645 (GCVE-0-2026-41645)
Vulnerability from cvelistv5 – Published: 2026-05-08 03:17 – Updated: 2026-05-11 18:16
VLAI
EPSS
VEX
Title
Nuclei: Environment variable disclosure via Response-Derived DSL Expressions
Summary
Nuclei is a vulnerability scanner built on a simple YAML-based DSL. From version 3.0.0 to before version 3.8.0, a vulnerability in Nuclei's expression evaluation engine makes it possible for a malicious target server to inject and execute supported DSL expressions. This happens when HTTP response data containing helper/function syntax gets reused by multi-step templates. If the -env-vars / -ev option is explicitly enabled, this can expose host environment variables. That option is off by default, so standard configurations are not affected by the information disclosure risk. This issue has been patched in version 3.8.0.
Severity
5.3 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-11 18:15 UTC
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://github.com/projectdiscovery/nuclei/securi… | x_refsource_CONFIRM |
| https://github.com/projectdiscovery/nuclei/pull/7221 | x_refsource_MISC |
| https://github.com/projectdiscovery/nuclei/pull/7321 | x_refsource_MISC |
| https://github.com/projectdiscovery/nuclei/commit… | x_refsource_MISC |
| https://github.com/projectdiscovery/nuclei/commit… | x_refsource_MISC |
| https://github.com/projectdiscovery/nuclei/releas… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| projectdiscovery | nuclei |
Affected:
>= 3.0.0, < 3.8.0
|
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CVE-2026-41523 (GCVE-0-2026-41523)
Vulnerability from cvelistv5 – Published: 2026-06-22 22:18 – Updated: 2026-09-04 12:04
VLAI
EPSS
VEX
Title
vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (python -O or PYTHONOPTIMIZE=1). This vulnerability is fixed in 0.22.0.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-23 12:23 UTC
CWE
Assigner
References
20 references
Impacted products
24 products
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
< 0.22.0
|
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951012 , < *
(rpm)
Unaffected: 1787860580 , < * (rpm) cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951244 , < *
(rpm)
Unaffected: 1787884873 , < * (rpm) cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1787161382 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1787161803 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151769 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151840 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151771 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151774 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.3 |
Unaffected:
1788290314 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.3 |
Unaffected:
1788273908 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.3 |
Unaffected:
1788273909 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.3 |
Unaffected:
1788260684 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.3 |
Unaffected:
1788273977 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.3 |
Unaffected:
1788260620 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787310717 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253912 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253989 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787254059 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787243961 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253774 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787244006 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
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"value": "RHSA-2026:62336: Red Hat Enterprise Linux AI 3.3"
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CVE-2026-41512 (GCVE-0-2026-41512)
Vulnerability from cvelistv5 – Published: 2026-05-08 13:52 – Updated: 2026-05-08 21:27
VLAI
EPSS
VEX
Title
Remote code execution via JavaScript injection in `BrowserAutomation::PlaywrightService`
Summary
ai-scanner is an AI model safety scanner built on NVIDIA garak. From version 1.0.0 to before version 1.4.1, there is a remote code execution vulnerability via JavaScript injection in `BrowserAutomation::PlaywrightService`. This issue has been patched in version 1.4.1.
Severity
9.9 (Critical)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-08 17:04 UTC
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/0din-ai/ai-scanner/security/ad… | x_refsource_CONFIRM |
| https://github.com/0din-ai/ai-scanner/releases/ta… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| 0din-ai | ai-scanner |
Affected:
>= 1.0.0, < 1.4.1
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CVE-2026-41507 (GCVE-0-2026-41507)
Vulnerability from cvelistv5 – Published: 2026-05-08 13:49 – Updated: 2026-05-08 14:20
VLAI
EPSS
VEX
Title
Remote Code Execution (RCE) via String Literal Injection into math-codegen
Summary
math-codegen generates code from mathematical expressions. Prior to version 0.4.3, string literal content passed to cg.parse() is injected verbatim into a new Function() body without sanitization. This allows an attacker to execute arbitrary system commands when user-controlled input reaches the parser. Any application exposing a math evaluation endpoint where user input flows into cg.parse() is vulnerable to full RCE. This issue has been patched in version 0.4.3.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-08 14:20 UTC
CWE
- CWE-94 - Improper Control of Generation of Code ('Code Injection')
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/mauriciopoppe/math-codegen/sec… | x_refsource_CONFIRM |
| https://github.com/mauriciopoppe/math-codegen/pull/11 | x_refsource_MISC |
| https://github.com/mauriciopoppe/math-codegen/com… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| mauriciopoppe | math-codegen |
Affected:
< 0.4.3
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Mitigation
Architecture and Design
Strategy: Refactoring
Refactor your program so that you do not have to dynamically generate code.
Mitigation
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.
Mitigation MIT-5
Implementation
Strategy: Input Validation
- 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().
Mitigation
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.
Mitigation MIT-32
Operation
Strategy: Compilation or Build Hardening
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).
Mitigation MIT-32
Operation
Strategy: Environment Hardening
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).
Mitigation
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].
CAPEC-242: Code Injection
An adversary exploits a weakness in input validation on the target to inject new code into that which is currently executing. This differs from code inclusion in that code inclusion involves the addition or replacement of a reference to a code file, which is subsequently loaded by the target and used as part of the code of some application.
CAPEC-35: Leverage Executable Code in Non-Executable Files
An attack of this type exploits a system's trust in configuration and resource files. When the executable loads the resource (such as an image file or configuration file) the attacker has modified the file to either execute malicious code directly or manipulate the target process (e.g. application server) to execute based on the malicious configuration parameters. Since systems are increasingly interrelated mashing up resources from local and remote sources the possibility of this attack occurring is high.
CAPEC-77: Manipulating User-Controlled Variables
This attack targets user controlled variables (DEBUG=1, PHP Globals, and So Forth). An adversary can override variables leveraging user-supplied, untrusted query variables directly used on the application server without any data sanitization. In extreme cases, the adversary can change variables controlling the business logic of the application. For instance, in languages like PHP, a number of poorly set default configurations may allow the user to override variables.