CVE-2026-41265·Python vulnerability
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.
- Severity
- critical
- Software
- Python
- Fixed in
- 3.1.0
- Published
- 2026-04-23
Affected versions
Until: 3.1.0
Fixed in: 3.1.0
How to fix this CVE
Upgrade Flowise to version 3.1.0 or later immediately to patch the prompt injection vulnerability in the Airtable Agent node. This critical flaw allows unauthenticated attackers to inject malicious Python code that executes on your server through LLM-generated scripts without proper sandboxing. After patching, audit your Airtable Agent deployments and review server logs for any suspicious script execution patterns.
sudo dnf update python3Defensia detects this vulnerability
What an exploitation attempt looks like
Sample log line indicative of exploitation attempts:
POST requests to /api/prediction/{chatflowId} or similar LLM endpoint with payloads containing Python code patterns (import os, __import__, exec, eval, subprocess) embedded in user prompt fields; successful exploitation correlates with Python traceback errors or unexpected process spawning in application logsWAF mitigation (if patching is not yet possible)
Add this rule to your WAF to block exploitation attempts while you schedule the patch.
Implement strict input validation on chatflow prompt fields to reject payloads containing Python keywords (exec, eval, __import__, subprocess, os.system). Deploy rate limiting on prediction endpoints to slow brute-force prompt injection attempts. Block requests containing base64-encoded Python bytecode or pickle serialization patterns.How to check if you are affected
- Check Flowise version: grep -i 'version' /path/to/flowise/package.json | head -1 or flowise --version if CLI is available
- Verify if Airtable Agent node is configured: grep -r 'Airtable_Agents' /path/to/flowise/nodes/ to confirm the vulnerable component is deployed
- Search server logs for Python script execution: grep -i 'exec\|eval\|subprocess' /var/log/flowise.log or application logs for patterns indicating code execution
- Confirm remediation: Verify Flowise version is 3.1.0 or higher and restart the service with sudo systemctl restart flowise
FAQ
What is CVE-2026-41265?
This is a critical remote code execution vulnerability in Flowise's Airtable Agent node where attackers can inject malicious prompts that trick the LLM into generating and executing arbitrary Python code on the server due to insufficient sandboxing of evaluated scripts.
Is CVE-2026-41265 being actively exploited?
No, this vulnerability is not currently listed in CISA's Known Exploited Vulnerabilities catalog, and no public exploits are available. However, the critical CVSS score and ease of exploitation via prompt injection make proactive patching essential.
What versions of Flowise are affected by CVE-2026-41265?
All versions of Flowise prior to version 3.1.0 are vulnerable. If you are running any version below 3.1.0 and have the Airtable Agent node enabled, your deployment is at risk.
How do I check if my server is vulnerable to CVE-2026-41265?
Run: grep '"version"' /path/to/flowise/package.json and compare against 3.1.0. If your version is below 3.1.0 and Airtable Agent nodes are active, you are vulnerable. Check: find /path/to/flowise -name '*airtable*' -o -name '*Airtable*' to confirm the component exists.
Does Defensia detect CVE-2026-41265?
Yes — Defensia's CVE advisory scanner compares installed package versions against the NVD database. If Flowise is installed on a monitored server, CVE-2026-41265 will appear in your dashboard with remediation steps.
Related Python CVEs
References
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