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.
8317 vulnerabilities reference this CWE, most recent first.
GHSA-V886-52JC-G2J8
Vulnerability from github – Published: 2022-05-01 23:27 – Updated: 2022-05-01 23:27Unspecified vulnerability in Microsoft Excel 2000 SP3 through 2003 SP2, Viewer 2003, and Office for Mac 2004 allows user-assisted remote attackers to execute arbitrary code via crafted Style records that trigger memory corruption.
{
"affected": [],
"aliases": [
"CVE-2008-0114"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2008-03-11T23:44:00Z",
"severity": "HIGH"
},
"details": "Unspecified vulnerability in Microsoft Excel 2000 SP3 through 2003 SP2, Viewer 2003, and Office for Mac 2004 allows user-assisted remote attackers to execute arbitrary code via crafted Style records that trigger memory corruption.",
"id": "GHSA-v886-52jc-g2j8",
"modified": "2022-05-01T23:27:40Z",
"published": "2022-05-01T23:27:40Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2008-0114"
},
{
"type": "WEB",
"url": "https://docs.microsoft.com/en-us/security-updates/securitybulletins/2008/ms08-014"
},
{
"type": "WEB",
"url": "https://oval.cisecurity.org/repository/search/definition/oval%3Aorg.mitre.oval%3Adef%3A5456"
},
{
"type": "WEB",
"url": "http://marc.info/?l=bugtraq\u0026m=120585858807305\u0026w=2"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/28166"
},
{
"type": "WEB",
"url": "http://www.securitytracker.com/id?1019584"
},
{
"type": "WEB",
"url": "http://www.us-cert.gov/cas/techalerts/TA08-071A.html"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2008/0846/references"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-V8HW-MH8C-JXFC
Vulnerability from github – Published: 2026-03-26 18:31 – Updated: 2026-06-06 00:56Description
1. Summary
The Agentic Assistant feature in Langflow executes LLM-generated Python code during its validation phase. Although this phase appears intended to validate generated component code, the implementation reaches dynamic execution sinks and instantiates the generated class server-side.
In deployments where an attacker can access the Agentic Assistant feature and influence the model output, this can result in arbitrary server-side Python execution.
2. Description
2.1 Intended Functionality
The Agentic Assistant endpoints are designed to help users generate and validate components for a flow. Users can submit requests to the assistant, which returns candidate component code for further processing.
A reasonable security expectation is that validation should treat model output as untrusted text and perform only static or side-effect-free checks.
The externally reachable endpoints are:
The request model accepts attacker-influenceable fields such as input_value, flow_id, provider, model_name, session_id, and max_retries:
2.2 Root Cause
In the affected code path, Langflow processes model output through the following chain:
/assist
→ execute_flow_with_validation()
→ execute_flow_file()
→ LLM returns component code
→ extract_component_code()
→ validate_component_code()
→ create_class()
→ generated class is instantiated
The assistant service reaches the validation path here:
The code extraction step occurs here:
The validation entry point is here:
The issue is that this validation path is not purely static. It ultimately invokes create_class() in lfx.custom.validate, where Python code is dynamically executed via exec(...), including both global-scope preparation and class construction.
As a result, LLM-generated code is treated as executable Python rather than inert data. This means the “validation” step crosses a trust boundary and becomes an execution sink.
The streaming path can also reach this sink when the request is classified into the component-generation branch:
3. Proof of Concept (PoC)
- Send a request to the Agentic Assistant endpoint.
- Provide input that causes the model to return malicious component code.
- The returned code reaches the validation path.
- During validation, the server dynamically executes the generated Python.
- Arbitrary server-side code execution occurs.
4. Impact
- Attackers who can access the Agentic Assistant feature and influence model output may execute arbitrary Python code on the server.
-
This can lead to:
-
OS command execution
- file read/write
- credential or secret disclosure
- full compromise of the Langflow process
5. Exploitability Notes
This issue is most accurately described as an authenticated or feature-reachable code execution vulnerability, rather than an unconditional unauthenticated remote attack.
Severity depends on deployment model:
- In local-only, single-user development setups, the issue may be limited to self-exposure by the operator.
- In shared, team, or internet-exposed deployments, it may be exploitable by other users or attackers who can reach the assistant feature.
The assistant feature depends on an active user context:
Authentication sources include bearer token, cookie, or API key:
Default deployment settings may widen exposure, including AUTO_LOGIN=true and the /api/v1/auto_login endpoint:
6. Patch Recommendation
- Remove all dynamic execution from the validation path.
- Ensure validation is strictly static and side-effect-free.
- Treat all LLM output as untrusted input.
- If code generation must be supported, require explicit approval and run it in a hardened sandbox isolated from the main server process.
Discovered by: @kexinoh (https://github.com/kexinoh, works at Tencent Zhuque Lab)
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 1.8.1"
},
"package": {
"ecosystem": "PyPI",
"name": "langflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.9.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-33873"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2026-03-26T18:31:36Z",
"nvd_published_at": "2026-03-27T21:17:23Z",
"severity": "CRITICAL"
},
"details": "## Description\n\n### 1. Summary\n\nThe Agentic Assistant feature in Langflow executes LLM-generated Python code during its **validation** phase. Although this phase appears intended to validate generated component code, the implementation reaches dynamic execution sinks and instantiates the generated class server-side.\n\nIn deployments where an attacker can access the Agentic Assistant feature and influence the model output, this can result in arbitrary server-side Python execution.\n\n### 2. Description\n\n#### 2.1 Intended Functionality\n\nThe Agentic Assistant endpoints are designed to help users generate and validate components for a flow. Users can submit requests to the assistant, which returns candidate component code for further processing.\n\nA reasonable security expectation is that validation should treat model output as **untrusted text** and perform only static or side-effect-free checks.\n\nThe externally reachable endpoints are:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/router.py#L252-L297](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/router.py#L252-L297)\n\nThe request model accepts attacker-influenceable fields such as `input_value`, `flow_id`, `provider`, `model_name`, `session_id`, and `max_retries`:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/schemas.py#L20-L31](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/schemas.py#L20-L31)\n\n#### 2.2 Root Cause\n\nIn the affected code path, Langflow processes model output through the following chain:\n\n`/assist`\n\u2192 `execute_flow_with_validation()`\n\u2192 `execute_flow_file()`\n\u2192 LLM returns component code\n\u2192 `extract_component_code()`\n\u2192 `validate_component_code()`\n\u2192 `create_class()`\n\u2192 generated class is instantiated\n\nThe assistant service reaches the validation path here:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79)\n\nThe code extraction step occurs here:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53)\n\nThe validation entry point is here:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47)\n\nThe issue is that this validation path is not purely static. It ultimately invokes `create_class()` in `lfx.custom.validate`, where Python code is dynamically executed via `exec(...)`, including both global-scope preparation and class construction.\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L241-L272](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L241-L272)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L394-L399](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L394-L399)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443)\n\nAs a result, LLM-generated code is treated as executable Python rather than inert data. This means the \u201cvalidation\u201d step crosses a trust boundary and becomes an execution sink.\n\nThe streaming path can also reach this sink when the request is classified into the component-generation branch:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L142-L156](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L142-L156)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300)\n\n### 3. Proof of Concept (PoC)\n\n1. Send a request to the Agentic Assistant endpoint.\n2. Provide input that causes the model to return malicious component code.\n3. The returned code reaches the validation path.\n4. During validation, the server dynamically executes the generated Python.\n5. Arbitrary server-side code execution occurs.\n\n### 4. Impact\n\n* Attackers who can access the Agentic Assistant feature and influence model output may execute arbitrary Python code on the server.\n* This can lead to:\n\n * OS command execution\n * file read/write\n * credential or secret disclosure\n * full compromise of the Langflow process\n\n### 5. Exploitability Notes\n\nThis issue is most accurately described as an **authenticated or feature-reachable code execution vulnerability**, rather than an unconditional unauthenticated remote attack.\n\nSeverity depends on deployment model:\n\n* In **local-only, single-user development setups**, the issue may be limited to self-exposure by the operator.\n* In **shared, team, or internet-exposed deployments**, it may be exploitable by other users or attackers who can reach the assistant feature.\n\nThe assistant feature depends on an active user context:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/utils/core.py#L38](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/utils/core.py#L38)\n\nAuthentication sources include bearer token, cookie, or API key:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L39-L53](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L39-L53)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L156-L163](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L156-L163)\n\nDefault deployment settings may widen exposure, including `AUTO_LOGIN=true` and the `/api/v1/auto_login` endpoint:\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/services/settings/auth.py#L71-L87](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/services/settings/auth.py#L71-L87)\n\n[https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135](https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135)\n\n### 6. Patch Recommendation\n\n* Remove all dynamic execution from the validation path.\n* Ensure validation is strictly static and side-effect-free.\n* Treat all LLM output as untrusted input.\n* If code generation must be supported, require explicit approval and run it in a hardened sandbox isolated from the main server process.\n\nDiscovered by: @kexinoh ([https://github.com/kexinoh](https://github.com/kexinoh), works at Tencent Zhuque Lab)",
"id": "GHSA-v8hw-mh8c-jxfc",
"modified": "2026-06-06T00:56:57Z",
"published": "2026-03-26T18:31:36Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/security/advisories/GHSA-v8hw-mh8c-jxfc"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-33873"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/langflow/PYSEC-2026-82.yaml"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/services/settings/auth.py#L71-L87"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L441-L443"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L394-L399"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/lfx/src/lfx/custom/validate.py#L241-L272"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L39-L53"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/services/auth/utils.py#L156-L163"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/v1/login.py#L96-L135"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/api/utils/core.py#L38"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L58-L79"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L259-L300"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/services/assistant_service.py#L142-L156"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/validation.py#L27-L47"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/helpers/code_extraction.py#L11-L53"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/schemas.py#L20-L31"
},
{
"type": "WEB",
"url": "https://github.com/langflow-ai/langflow/blob/f7f4d1e70ba5eecd18162ec96f3571c2cfbcd1fc/src/backend/base/langflow/agentic/api/router.py#L252-L297"
},
{
"type": "PACKAGE",
"url": "https://github.com/langflow-ai/langflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:H/SI:H/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Langflow has Authenticated Code Execution in Agentic Assistant Validation"
}
GHSA-V8QW-P8WF-QX5H
Vulnerability from github – Published: 2022-07-13 00:00 – Updated: 2022-07-13 00:00Windows Fax Service Remote Code Execution Vulnerability. This CVE ID is unique from CVE-2022-22024.
{
"affected": [],
"aliases": [
"CVE-2022-22027"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-07-12T23:15:00Z",
"severity": "HIGH"
},
"details": "Windows Fax Service Remote Code Execution Vulnerability. This CVE ID is unique from CVE-2022-22024.",
"id": "GHSA-v8qw-p8wf-qx5h",
"modified": "2022-07-13T00:00:40Z",
"published": "2022-07-13T00:00:40Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-22027"
},
{
"type": "WEB",
"url": "https://msrc.microsoft.com/update-guide/vulnerability/CVE-2022-22027"
},
{
"type": "WEB",
"url": "https://portal.msrc.microsoft.com/en-US/security-guidance/advisory/CVE-2022-22027"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-V8V8-6859-QXM4
Vulnerability from github – Published: 2020-06-05 14:47 – Updated: 2023-09-08 20:47Lack of output sanitization allowed an attack to execute arbitrary shell commands via the logkitty npm package before version 0.7.1.
{
"affected": [
{
"package": {
"ecosystem": "npm",
"name": "logkitty"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.7.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2020-8149"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2020-06-04T19:33:24Z",
"nvd_published_at": null,
"severity": "CRITICAL"
},
"details": "Lack of output sanitization allowed an attack to execute arbitrary shell commands via the logkitty npm package before version 0.7.1.",
"id": "GHSA-v8v8-6859-qxm4",
"modified": "2023-09-08T20:47:27Z",
"published": "2020-06-05T14:47:02Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2020-8149"
},
{
"type": "WEB",
"url": "https://github.com/zamotany/logkitty/pull/18"
},
{
"type": "WEB",
"url": "https://github.com/zamotany/logkitty/commit/ef2f673e25c629544dd3de6429999318447dd6bf"
},
{
"type": "WEB",
"url": "https://hackerone.com/reports/825729"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "Arbitrary shell command execution in logkitty"
}
GHSA-V8VM-8H6V-G2GC
Vulnerability from github – Published: 2025-04-17 18:31 – Updated: 2025-04-22 15:30Hazelcast Management Center through 6.0 allows remote code execution via a JndiLoginModule user.provider.url in a hazelcast-client XML document (aka a client configuration file), which can be uploaded at the /cluster-connections URI.
{
"affected": [],
"aliases": [
"CVE-2024-56518"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-04-17T16:15:27Z",
"severity": "CRITICAL"
},
"details": "Hazelcast Management Center through 6.0 allows remote code execution via a JndiLoginModule user.provider.url in a hazelcast-client XML document (aka a client configuration file), which can be uploaded at the /cluster-connections URI.",
"id": "GHSA-v8vm-8h6v-g2gc",
"modified": "2025-04-22T15:30:50Z",
"published": "2025-04-17T18:31:12Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-56518"
},
{
"type": "WEB",
"url": "https://docs.hazelcast.com/management-center/6.0-snapshot/getting-started/install"
},
{
"type": "WEB",
"url": "https://gist.github.com/azraelxuemo/c3d42739aa3306a41111ef603dc65b4c"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-V8X2-MJPX-FX33
Vulnerability from github – Published: 2025-12-18 09:30 – Updated: 2026-01-20 15:32Improper Control of Generation of Code ('Code Injection') vulnerability in jetmonsters Hotel Booking Lite motopress-hotel-booking-lite allows Remote Code Inclusion.This issue affects Hotel Booking Lite: from n/a through <= 5.2.3.
{
"affected": [],
"aliases": [
"CVE-2025-66078"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-12-18T08:16:15Z",
"severity": "CRITICAL"
},
"details": "Improper Control of Generation of Code (\u0027Code Injection\u0027) vulnerability in jetmonsters Hotel Booking Lite motopress-hotel-booking-lite allows Remote Code Inclusion.This issue affects Hotel Booking Lite: from n/a through \u003c= 5.2.3.",
"id": "GHSA-v8x2-mjpx-fx33",
"modified": "2026-01-20T15:32:31Z",
"published": "2025-12-18T09:30:30Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-66078"
},
{
"type": "WEB",
"url": "https://patchstack.com/database/Wordpress/Plugin/motopress-hotel-booking-lite/vulnerability/wordpress-hotel-booking-lite-plugin-5-2-3-remote-code-execution-rce-vulnerability?_s_id=cve"
},
{
"type": "WEB",
"url": "https://vdp.patchstack.com/database/Wordpress/Plugin/motopress-hotel-booking-lite/vulnerability/wordpress-hotel-booking-lite-plugin-5-2-3-remote-code-execution-rce-vulnerability?_s_id=cve"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N",
"type": "CVSS_V3"
}
]
}
GHSA-V8X2-PMHM-GQCJ
Vulnerability from github – Published: 2023-12-14 09:30 – Updated: 2025-05-22 18:31Nagios XI before version 5.11.3 was discovered to contain a remote code execution (RCE) vulnerability via the component command_test.php.
{
"affected": [],
"aliases": [
"CVE-2023-48085"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2023-12-14T07:15:09Z",
"severity": "CRITICAL"
},
"details": "Nagios XI before version 5.11.3 was discovered to contain a remote code execution (RCE) vulnerability via the component command_test.php.",
"id": "GHSA-v8x2-pmhm-gqcj",
"modified": "2025-05-22T18:31:10Z",
"published": "2023-12-14T09:30:19Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-48085"
},
{
"type": "WEB",
"url": "https://www.nagios.com/products/security"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-V92F-JX6P-73RX
Vulnerability from github – Published: 2023-09-19 20:35 – Updated: 2025-10-22 19:17Impact
Programs using jt-jiffle, and allowing Jiffle script to be provided via network request, are susceptible to a Remote Code Execution as the Jiffle script is compiled into Java code via Janino, and executed. In particular, this affects the downstream GeoServer project.
Patches
Version 1.2.22 will contain a patch that disables the ability to inject malicious code into the resulting script.
Workarounds
Negate the ability to compile Jiffle scripts from the final application, by removing janino-x.y.z.jar from the classpath.
References
None.
{
"affected": [
{
"package": {
"ecosystem": "Maven",
"name": "it.geosolutions.jaiext.jiffle:jt-jiffle"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.1.22"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "Maven",
"name": "it.geosolutions.jaiext.jiffle:jt-jiffle-language"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.1.22"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2022-24816"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2023-09-19T20:35:16Z",
"nvd_published_at": "2022-04-13T21:15:00Z",
"severity": "CRITICAL"
},
"details": "### Impact\nPrograms using jt-jiffle, and allowing Jiffle script to be provided via network request, are susceptible to a Remote Code Execution as the Jiffle script is compiled into Java code via Janino, and executed. In particular, this affects the downstream GeoServer project.\n\n### Patches\nVersion 1.2.22 will contain a patch that disables the ability to inject malicious code into the resulting script.\n\n### Workarounds\nNegate the ability to compile Jiffle scripts from the final application, by removing janino-x.y.z.jar from the classpath.\n\n### References\nNone.",
"id": "GHSA-v92f-jx6p-73rx",
"modified": "2025-10-22T19:17:43Z",
"published": "2023-09-19T20:35:16Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/geosolutions-it/jai-ext/security/advisories/GHSA-v92f-jx6p-73rx"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-24816"
},
{
"type": "WEB",
"url": "https://github.com/geosolutions-it/jai-ext/commit/cb1d6565d38954676b0a366da4f965fef38da1cb"
},
{
"type": "PACKAGE",
"url": "https://github.com/geosolutions-it/jai-ext"
},
{
"type": "WEB",
"url": "https://www.cisa.gov/known-exploited-vulnerabilities-catalog?field_cve=CVE-2022-24816"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H/E:H",
"type": "CVSS_V3"
}
],
"summary": "Improper Control of Generation of Code (\u0027Code Injection\u0027) in jai-ext"
}
GHSA-V92M-HHHW-VV9V
Vulnerability from github – Published: 2021-09-01 18:27 – Updated: 2021-08-30 21:59Codiad Web IDE through 2.8.4 allows PHP Code injection.
{
"affected": [
{
"package": {
"ecosystem": "Packagist",
"name": "codiad/codiad"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"last_affected": "2.8.4"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2019-19208"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2021-08-30T21:59:12Z",
"nvd_published_at": "2020-03-16T15:15:00Z",
"severity": "CRITICAL"
},
"details": "Codiad Web IDE through 2.8.4 allows PHP Code injection.",
"id": "GHSA-v92m-hhhw-vv9v",
"modified": "2021-08-30T21:59:12Z",
"published": "2021-09-01T18:27:12Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2019-19208"
},
{
"type": "PACKAGE",
"url": "https://github.com/Codiad/Codiad"
},
{
"type": "WEB",
"url": "https://github.com/Codiad/Codiad/commits/master"
},
{
"type": "WEB",
"url": "https://github.com/Hacker5preme/Exploits/tree/main/CVE-2019-19208-Exploit"
},
{
"type": "WEB",
"url": "https://herolab.usd.de/en/security-advisories"
},
{
"type": "WEB",
"url": "https://herolab.usd.de/security-advisories/usd-2019-0049"
},
{
"type": "WEB",
"url": "https://www.exploit-db.com/exploits/49902"
},
{
"type": "WEB",
"url": "http://packetstormsecurity.com/files/162753/Codiad-2.8.4-Remote-Code-Execution.html"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "Code injection in codiad"
}
GHSA-V93M-WW8M-RRFF
Vulnerability from github – Published: 2022-05-01 23:43 – Updated: 2022-05-01 23:43CRLF injection vulnerability in Akamai Download Manager ActiveX control before 2.2.3.6 allows remote attackers to force the download and execution of arbitrary files via a URL parameter containing an encoded LF followed by a malicious target line.
{
"affected": [],
"aliases": [
"CVE-2008-1770"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2008-06-04T21:32:00Z",
"severity": "HIGH"
},
"details": "CRLF injection vulnerability in Akamai Download Manager ActiveX control before 2.2.3.6 allows remote attackers to force the download and execution of arbitrary files via a URL parameter containing an encoded LF followed by a malicious target line.",
"id": "GHSA-v93m-ww8m-rrff",
"modified": "2022-05-01T23:43:24Z",
"published": "2022-05-01T23:43:24Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2008-1770"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/42879"
},
{
"type": "WEB",
"url": "https://www.exploit-db.com/exploits/5741"
},
{
"type": "WEB",
"url": "http://lists.grok.org.uk/pipermail/full-disclosure/2008-June/062672.html"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/30537"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/archive/1/493077/100/0/threaded"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/archive/1/493142/100/0/threaded"
},
{
"type": "WEB",
"url": "http://www.securitytracker.com/id?1020194"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2008/1746/references"
}
],
"schema_version": "1.4.0",
"severity": []
}
Mitigation
Strategy: Refactoring
Refactor your program so that you do not have to dynamically generate code.
Mitigation
- 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
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
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
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
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
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.