Flowise: Improper Neutralization of Special Elements used in a Command
CVE-2026-41265 is a critical-severity improper neutralization of special elements used in a command vulnerability in Flowise from FlowiseAI, with a CVSS 3.1 base score of 9.8 out of 10. It can be exploited remotely over the network by an unauthenticated attacker, without any action from a user. Affected Flowise versions: < 3.1.0. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.57% chance of exploitation within 30 days, which is low compared with other CVEs.
Critical severity: a successful attack would have severe impact.
What to do
- Check which version of Flowise you run against the affected versions below, and install the vendor's latest release.
- Read the advisory on github.com for workarounds and exact fixed builds.
- Until you can update, restrict network access to the affected service so it is not reachable from untrusted networks.
Affected products and versions
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- NoneNo account or login is needed.
- User interaction
- NoneNo action by a victim is required.
- Scope
- UnchangedImpact stays within the vulnerable component.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- HighData or code can be modified at will.
- Availability
- HighThe component can be taken down completely.
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 4.0 | 9.2 | Critical | CNA (github.com) |
| CVSS 3.1 | 9.8 | Critical | NVD |
Official description
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the Airtable_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the Airtable Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the flowise server. This vulnerability is fixed in 3.1.0.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Improper Neutralization of Special Elements used in a Command
The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.
How developers prevent this class of flaw
- If at all possible, use library calls rather than external processes to recreate the desired functionality.
- If possible, ensure that all external commands called from the program are statically created.
- Run time: Run time policy enforcement may be used in an allowlist fashion to prevent use of any non-sanctioned commands.
More Improper Neutralization of Special Elements used in a Command vulnerabilities (CWE-77)
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: no, technical impact: total).
- Record last changed materially (score, affected products, status or references).
References
Advisories
Other vulnerabilities in Flowise
- 8.5 High severity
- 8.5 High severity
- 7.5 High severity
- 8.3 High severity
- 7.7 High severity
- 7.7 High severity
Recent Improper Neutralization of Special Elements used in a Command vulnerabilities
- 9.8 Critical severity
- 9.8 Critical severity
- 9.8 Critical severity
- 7.3 High severity
- 8.8 High severity
- 7.2 High severity
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