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NEWSJACK — OCT 22, 2026 · SEKOIA ON EXPOSED FLOWISE WORKFLOWS

What Is Prompt Injection? Paste Your Exported AI Workflow — What's Leaking?

Published · by Ingenious AI · interactive explainer and checker

Ask what is prompt injection in the abstract and you get a definition. Ask it about your own workflow and you get a list: the API key typed into a node and stored in every export, the code node sitting one hop downstream of the model, the credentials Sekoia reported being harvested from exposed Flowise servers on October 22, 2026. Paste your Flowise, Langflow or n8n export below — the analysis runs entirely in your browser, and you will see exactly what your workflow is leaking.

Privacy: everything on this page runs locally in your browser. Your export is never uploaded, stored or sent anywhere — the analysis is JavaScript on this page, and secrets in the results are shown redacted to their first 4 characters only.

Short answer: your exported workflow file itself. A Flowise, Langflow or n8n export stores node parameters verbatim, so a key typed into a node is a key written into every export, backup and shared copy — 100% of the credentials stored in an exposed workflow travel with the file, per Sekoia's October 22, 2026 reporting on harvested Flowise credentials. Add a privileged node downstream of the model, and the same file also documents the attack path.

Prompt injection news: what happened on October 22, 2026?

On October 22, 2026, Sekoia reported that attackers were harvesting credentials from exposed Flowise servers — instances of the popular visual agent builder left reachable on the internet, their saved workflows holding the API keys, database passwords and service tokens the builders had typed into nodes. The attackers did not need an exploit in the traditional sense: the workflows were the vault, and the vault was open.

Flowise is one name for a general shape. Langflow and n8n exports have the same property: a workflow file is a complete description of an automation, including its parameters, and parameters are where credentials live. Teams export workflows to share them, back them up, post them in forums for help, and commit them to repositories — each copy carrying the keys in plaintext, readable by anyone who opens the file.

The second leak in the same file is structural. The export also records which nodes connect to which, so it shows whether a language-model node — the component that reads untrusted text — can reach a code, HTTP or database node. That path is the prompt-injection kill chain, drawn by the workflow's own author: hostile content reaches the model, the model steers the flow, and the privileged node at the end executes. The analyzer below checks both leaks, in your browser, on the file you paste.

What is prompt injection?

Prompt injection is an attack where hostile instructions hidden in content a language model reads — a web page, an email, a document, a tool result — override the instructions the model's owner gave it. The model cannot reliably separate data from commands, so text that arrives as input can act as orders. On its own, a hijacked answer is embarrassing. Wired into a workflow where the model's output flows to code execution, HTTP requests or database queries, it becomes remote control — which is why the reachability walk in the analyzer matters as much as the credential scan.

Paste your export — the leak analysis runs locally

Paste a Flowise, Langflow or n8n export (the full JSON). The analyzer parses it here in your browser, scans for credential patterns and plaintext credential fields, detects privileged nodes (code, shell, HTTP, SQL), and walks the workflow's own edges from LLM nodes to see which privileged nodes they can reach. Findings show secrets redacted to the first 4 characters only. Nothing is uploaded — there is no server behind this page. Or load a sample first: the leaking sample carries a demo key and an LLM-to-code-to-HTTP chain; the clean sample uses an environment reference and no privileged nodes.

Honest scope note: this analyzer reads the single export you paste: it cannot see credentials stored server-side in the builder's own database, other workflows on the same instance, or whether your Flowise server is internet-exposed right now — check exposure separately. Pattern matching also cannot catch keys in unusual formats. The full tool is the open-source agent-workflow-leak-auditor repo by Ingenious AI, which audits whole instances and export folders.

What the analyzer hunts — and why each finding matters

FindingSeverityWhy it matters
Credential pattern in the export (sk-, AKIA, ghp_, xox, private keys, JWTs)CRITICALThe key is in the file, in plaintext. Every copy of the export is a copy of the key. Rotate first; deleting the line does not un-leak it.
Plaintext credential field on a nodeHIGHThe builder stored the secret as a parameter instead of a reference. Exports, backups and shared templates all inherit it.
Privileged node reachable from an LLM nodeHIGHThe prompt-injection kill chain in one hop: untrusted text steers the model, the model's flow reaches code, HTTP or SQL execution.
Privileged node present (not LLM-reachable)MEDIUMLegitimate in many designs, but it defines the blast radius if any upstream path changes — re-check after every workflow edit.

Leaking export? Do this now

If the analyzer flags your export, the file has already been a credential copy everywhere it has been — work this list in order:

  1. Rotate every flagged credential. Revoke the old values, do not just add new ones. A key found in an export must be assumed copied — by the exposure Sekoia documented, by shared drives, by chat logs where the file was pasted for help.
  2. Re-issue the export clean. Replace typed-in secrets with environment or credential-store references, re-export, and verify with the analyzer above before the new file goes anywhere.
  3. Hunt the old copies. Repositories, backups, forum posts, tickets, shared drives: find every copy of the leaking export and delete or replace it. Git history counts — a deleted line in a commit is still in the commit.
  4. Break the kill chain. Put a human approval or a strict allowlist between LLM nodes and code, HTTP or SQL nodes, or remove the privileged node from any flow whose inputs include untrusted content.
  5. Check the instance, not just the file. If your Flowise, Langflow or n8n instance is reachable from the internet without authentication, the exports on it are exposed whether or not you ever downloaded them. Close that first.
  6. Make the check a habit. Run every export through this analyzer before sharing it. Exports feel like documentation; they are closer to a keyring.
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Frequently asked questions

What is prompt injection in an AI workflow?

Prompt injection is hostile instruction text hidden in content a language model reads, which the model then obeys as if its owner wrote it. In a workflow the stakes are higher than a wrong answer: if the model's flow reaches a code, HTTP or database node, the injected text can steer real actions. The reachability walk on this page maps exactly that path in your export.

Why do exported workflows leak credentials?

Because builders store what you type. A key entered into a node becomes a parameter, parameters are written verbatim into export JSON, and exports get shared, backed up and committed. Sekoia's October 22, 2026 report documented attackers harvesting exactly these stored credentials from exposed Flowise servers — no exploit needed, just access to the files.

Is it safe to paste my workflow export into this page?

Yes — the analysis runs entirely in your browser with JavaScript on this page. Nothing is uploaded, stored or transmitted, and any secret the analyzer finds is displayed redacted to its first 4 characters. That said, good practice is to rotate any real key you find in an export regardless of where you found it, because the export itself is the leak.

What should I do with a key I find in an old export?

Rotate it: revoke the old value and issue a new one, then replace the typed-in value in the workflow with an environment or credential-store reference and re-export. Deleting the key from the file is not enough — every existing copy of that export, including git history and backups, still holds a working credential until it is revoked.

What does the full auditor check that this page cannot?

This page analyzes the one export you paste. The full agent-workflow-leak-auditor scans entire export folders and builder instances, inventories every workflow's credentials and privileged paths at once, and tracks findings across versions — use it for a complete audit rather than a single-file check.

Built by Ingenious AI — reporting on third-party security research. The Flowise credential-harvesting account is summarized from Sekoia's reporting of October 22, 2026.
This page is general information, not legal advice. This page never asks for, stores, or transmits your credentials or pasted text. Full tool: agent-workflow-leak-auditor on GitLab — part of the Ingenious AI toolkit.