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CVE-2026-70477 – Prompt Injection to Remote Code Execution – Flowise prior to 3.1.3

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Summary

CVE-2026-70477 is a code injection (CWE-94) vulnerability in Flowise, the open-source drag-and-drop platform for building LLM applications and AI agents. A prompt injection sent to a chatflow that uses a CSV Agent node can drive the LLM into returning a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment, resulting in remote code execution. The issue is rated Critical (CVSS 9.5).

Technical details

  • Root cause: The CSV Agent’s run method builds LLM prompts from untrusted user input and then executes the model’s Python response. Validation relies on a static regex blocklist that can be circumvented through obfuscation.
  • Trigger conditions: A chatflow using a CSV Agent node processes an attacker-supplied prompt, causing the LLM to emit Python that passes the validatePythonCodeForDataFrame check.
  • Attack vector: Network. A prompt injection delivered to a chatflow endpoint requires no privileges and no user interaction.
  • Impact: Pyodide is not sandboxed from the host operating system, so any Python code passing the validator runs with full access to OS interfaces — leading to complete compromise of confidentiality, integrity, and availability.

Affected software

  • Flowise (FlowiseAI) versions prior to 3.1.3 (npm flowise and flowise-components ≤ 3.1.2).

Severity

  • CVSS 4.0 base score: 9.5 (Critical)
  • Vector: CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

Mitigation and recommended actions

  • Immediate: Upgrade to Flowise 3.1.3 or later, which contains the fix.
  • If no patch can be applied: Restrict network exposure of Flowise instances, avoid deploying or exposing chatflows that use the CSV Agent node, and place instances behind authenticated, access-controlled network boundaries until upgrading.

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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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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