GHSA-75g8-rv7v-32f7 CVE-2026-27493
n8n has Unauthenticated Expression Evaluation via Form Node
- Severity
- 0.0 / 10
- Critical · CVSS 4.0
- Exploit chance (EPSS)
- 1.39%
- Chance of exploitation in the next 30 days. Higher than 72.23% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
- Fix
1.123.22- Upgrade n8n to this version or later
Description
Impact
A second-order expression injection vulnerability existed in n8n's Form nodes that could allow an unauthenticated attacker to inject and evaluate arbitrary n8n expressions by submitting crafted form data. When chained with an expression sandbox escape, this could escalate to remote code execution on the n8n host.
The vulnerability requires a specific workflow configuration to be exploitable:
- A form node with a field interpolating a value provided by an unauthenticated user, e.g. a form submitted value.
- The field value must begin with an
=character, which caused n8n to treat it as an expression and triggered a double-evaluation of the field content. For example, a workflow uses a multi-step Form where a downstream Form node renders user-provided input back in an HTML field and precedes it with an=sign:=<h2>Thank you, {{ $input.first().json[\"Name\"] }}!</h2>
There is no practical reason for a workflow designer to prefix a field with = intentionally — the character is not rendered in the output, so the result would not match the designer's expectations. If added accidentally, it would be noticeable and very unlikely to persist. An unauthenticated attacker would need to either know about this specific circumstance on a target instance or discover a matching form by chance.
Even when the preconditions are met, the expression injection alone is limited to data accessible within the n8n expression context. Escalation to remote code execution requires chaining with a separate sandbox escape vulnerability.
Due to these real-world constraints — the unlikely workflow configuration, the need for an additional sandbox escape, and the difficulty of discovery — we have assessed the severity as High rather than Critical, diverging from the base CVSS score to better reflect actual exploitability.
Patches
The issue has been fixed in n8n versions 2.10.1, 2.9.3, and 1.123.22. Users should upgrade to one of these versions or later to remediate the vulnerability.
Workarounds
If upgrading is not immediately possible, administrators should consider the following temporary mitigations:
- Review usage of form nodes manually for above mentioned preconditions.
- Disable the Form node by adding
n8n-nodes-base.formto theNODES_EXCLUDEenvironment variable. - Disable the Form Trigger node by adding
n8n-nodes-base.formTriggerto theNODES_EXCLUDEenvironment variable.
These workarounds do not fully remediate the risk and should only be used as short-term mitigation measures.
Affected versions
npm
| Package | Vulnerable | Fixed in |
|---|---|---|
| n8n | < 1.123.22 |
1.123.22 |
| n8n | >= 2.0.0, < 2.9.3 |
2.9.3 |
| n8n | >= 2.10.0, < 2.10.1 |
2.10.1 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
n8n npm → 1.123.22
- npm
npm install n8n@1.123.22- yarn
yarn add n8n@1.123.22- pnpm
pnpm add n8n@1.123.22
Severity breakdown
CVSS 4.0 0.0
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H
- Attack vector
- Network
- Attack complexity
- Low
- Attack requirements
- Present
- Privileges required
- None
- User interaction
- None
- Confidentiality (vulnerable system)
- High
- Integrity (vulnerable system)
- High
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- High
- Integrity (subsequent systems)
- High
- Availability (subsequent systems)
- High
CVSS 3.1 9.0
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- High
- Privileges required
- None
- User interaction
- None
- Scope
- Changed
- Confidentiality
- High
- Integrity
- High
- Availability
- High
Weakness
CWE-94: Improper Control of Generation of Code ('Code Injection')
The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.
How to prevent it
- Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
- Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
- Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
- Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
- Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. "eval").
How to prevent it
- Architecture and Design, Implementation: If possible, refactor your code so that it does not need to use eval() at all.
- 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.
- Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
- Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-v98v-ff95-f3cp n8n Vulnerable to Remote Code Execution via Expression Injection | High Known exploited | Also affects n8n |
| GHSA-qgpw-8g46-w95v n8n: Git Node branch.<name>.remote Config Key Bypasses Sandbox Path Restriction, Enabling Local Git Repository Read | Medium | Also affects n8n |
| GHSA-65xw-2v52-jhxc n8n: Cross-User Active Workflow ID and Lifecycle Event Disclosure via Missing userId Filter | Medium | Also affects n8n |
| GHSA-pq6c-vh67-xpm3 n8n: Log Streaming Event Destinations Decrypt Generic-Auth Credentials Without Ownership Check | Medium | Also affects n8n |
| GHSA-cqr2-h44g-v75v n8n: Cross-Tenant Project-Member PII Disclosure via Missing Per-Project Scope Check on Role Assignment Endpoints | Medium | Also affects n8n |
| GHSA-pf83-w3f9-8m37 n8n: Disabled OIDC SSO Endpoints Remain Active and Issue Valid Sessions | Medium | Also affects n8n |
| GHSA-6xcw-7xm6-48c6 n8n: Expression Sandbox Escape via Shared Builtin Tampering and Code-Printer Injection Leads to Code Execution | High | Also affects n8n |
| GHSA-34ff-336r-5q23 n8n: Domain-Restriction Bypass via Unguarded Model-Search Endpoint in OpenAI Chat Model Node | Medium | Also affects n8n |
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