CVE-2026-41265
Flowise is a drag & drop user interface to build a customized large language model flow.
Published Apr 23, 2026 · updated Jun 17, 2026 · flowiseai flowise
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.
Code
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Timeline
Detection, updates, and resolution for this record.
Apr 23, 2026 · NIST NVD
Detected
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 t
Jun 17, 2026 · NIST NVD
Updated
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 t
Affected
- cpe flowiseai/flowiseversion unspecified
Sources
- CVE.orgVulnerability recordCVE-2026-41265
Official CVE identifier
cve.org - NIST NVDVulnerability recordCVE-2026-41265
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 scrip
nvd.nist.gov