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CVE-2026-69255 – Remote Code Execution (Code Injection) – Flowise < 3.1.3

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Summary

CVE-2026-69255 is a code injection (CWE-94) vulnerability in the CSV Agent component of Flowise, the open-source low-code platform for building LLM applications and AI agents. Attacker-controlled CSV data is unsafely interpolated into Python code executed via Pyodide, allowing arbitrary operating system command execution. It affects Flowise versions prior to 3.1.3 and is rated Critical (CVSS 9.2).

Technical details

  • Root cause: The CSV Agent extracts base64-encoded CSV data from a data URI and interpolates it directly into a generated Python code block as base64_string = "${base64String}" before Pyodide execution.
  • Trigger conditions: An attacker crafts a base64 payload containing a closing quote followed by arbitrary Python, breaking out of the string context. The existing validatePythonCodeForDataFrame() denylist only validates LLM-generated code, not this initial interpolated block, so the injected code is not filtered.
  • Attack vector: Network. The vulnerable code path is reached by submitting malicious CSV input to the CSV Agent.
  • Impact: Execution of arbitrary OS commands, enabling full remote code execution, credential/environment-variable theft, unauthorized file access, and denial of service.

Affected software

  • Flowise versions before 3.1.3 (i.e., ≤ 3.1.2).

Severity

  • CVSS 9.2 (Critical).
  • Vector: CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N.

Mitigation and recommended actions

  • Immediate: Upgrade to Flowise 3.1.3 or later, which fixes the unsafe interpolation.
  • If no patch can be applied: Restrict network access to the Flowise instance, avoid exposing it directly to the internet, enforce authentication and access controls, and avoid using the CSV Agent with untrusted input until upgraded.

How IONIX identifies potentially affected assets

IONIX matches the following signals against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Page title: Flowise - Build AI Agents, Visually, Flowise - Low-code LLM apps builder
  • Raw response body: data-rewardful="9a3a26", og:site_name … flowiseai.com
  • author meta tag content: FlowiseAI
  • description meta tag content: Open source generative AI development platform for building AI agents

References

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How IONIX’s External Exposure Management Platform Detects and Validates
Zero-Days to Shrink MTTR

1

Map your entire attack surface (continously)

IONIX uses multi-factor discovery methods, including DNS analysis, certificate mapping, metadata inspection, and more, to automatically map every internet-facing asset across your environment. This includes cloud instances, third-party platforms, shadow IT, and even forgotten infrastructure that traditional tools miss.

2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

5

Execute Exploit Validations

By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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