Weakness (CWE)
CWE-94: Improper Control of Generation of Code
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 (7 mitigations)
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Architecture and Design
Refactor your program so that you do not have to dynamically generate code.
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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.
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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().
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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.
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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).
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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).
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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].
Source: MITRE CWE-94
1,737 results · Clear filters · Feed
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High 7.3
Fosowl agenticSeek query Endpoint PyInterpreter.py PyInterpreter.execute code injection
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High 7.3
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High 8.7CVE-2026-28797 Public exploit
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High 8.3
dbgate-web: Stored XSS in applicationIcon leads to potential RCE in Electron due to unsafe renderer configuration
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High 7.2
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High 8.8
Libinput: libinput: unauthorized code execution and information disclosure through lua bytecode plugins
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High 7.2
XenForo Remote Code Execution via Authenticated Admin
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High 8.8
XenForo Template Method Call Restriction Bypass
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High 8.6
SiYuan: Stored XSS in imported .sy.zip content leads to arbitrary command execution
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High 8.1CVE-2026-4800 Public exploit
lodash vulnerable to Code Injection via `_.template` imports key names
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High 7.1
Ruby LSP has arbitrary code execution through branch setting
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High 7.5
Tautulli: RCE via eval() sandbox bypass using lambda nested scope to escape co_names whitelist check
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High 7.3
Sinaptik AI PandasAI Chat Message code_executor.py CodeExecutor.execute code injection
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High 8.6
Notesnook vulnerable to RCE via stored XSS in Note History diff viewer
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High 8.8
Happy DOM ECMAScriptModuleCompiler: unsanitized export names are interpolated as executable code
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High 8.3
Handlebars.js has JavaScript Injection in CLI Precompiler via Unescaped Names and Options
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High 8.1
Handlebars.js has JavaScript Injection via AST Type Confusion when passing an object as dynamic partial
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High 8.1
Handlebars.js has JavaScript Injection via AST Type Confusion by tampering @partial-block
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High 7.3
Windmill: Rogue Workspace Admins can inject code via unescaped workspace environment variable interpolation in NativeTS executor
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High 8.9
Zero-Click Indirect Prompt Injection and Authentication Bypass via Email Polling
nanobot is a personal AI assistant. Prior to version 0.1.6, an indirect prompt injection vulnerability exists in the email channel processing module (`nanobot/channels/email.py`), allowing a remote, unauthenticated attacker to execute arbitrary LLM instructio…
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High 7.3
letta-ai letta Incomplete Fix CVE-2025-6101 ast_parsers.py resolve_type eval injection
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High 8.8
Code injection vulnerability exists in BUFFALO Wi-Fi router products. If this vulnerability is exploited, an arbitrary …
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High 7.8
BentoML has Dockerfile Command Injection via system_packages in bentofile.yaml
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High 8.5
WordPress Post Snippets plugin <= 4.0.12 - Remote Code Execution (RCE) vulnerability
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High 8.8CVE-2026-33310 Public exploit
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High 8.8
Connect-CMS has Arbitrary Code Execution by an Authenticated User in its Code Study Plugin
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High 8.8
AVideo has PHP Code Injection via eval() in Gallery saveSort.json.php Exploitable Through CSRF Against Admin
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High 7.3
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High 8.8CVE-2026-24516 Public exploit
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High 7.5CVE-2026-33154 Public exploit
dynaconf Affected by Remote Code Execution (RCE) via Insecure Template Evaluation in @jinja Resolver
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High 7.2
SuiteCRM has Authenticated RCE in Modules
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High 8.1
jsPDF has a PDF Object Injection via FreeText color
jsPDF is a library to generate PDFs in JavaScript. Prior to version 4.2.1, user control of arguments of the `createAnnotation` method allows users to inject arbitrary PDF objects, such as JavaScript actions. If given the possibility to pass unsanitized input …
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High 8.6
This High severity RCE (Remote Code Execution) vulnerability was introduced in versions 9.6.0, 10.0.0, 10.1.0, 10.2.0,…
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High 8.8
Chamilo LMS: Authenticated RCE via H5P Import
Chamilo LMS is a learning management system. Prior to version 1.11.36, an arbitrary file upload vulnerability in the H5P Import feature allows authenticated users with Teacher role to achieve Remote Code Execution (RCE). The H5P package validation only checks…
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High 8.6
Authenticated RCE in Raytha CMS
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High 7.8
Code Injection vulnerability affecting SOLIDWORKS Desktop from Release 2025 through Release 2026
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High 8.8CVE-2025-50881 Public exploit
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High 7.5
Command Injection in mlflow/mlflow
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High 8.7
(SimpleEval) Objects (including modules) can leak dangerous modules through to direct access inside the sandbox.
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High 7.2
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