Flowise: CSV Agent Prompt Injection Remote Code Execution Vulnerability

CISA KEV is observed exploitation evidence. FIRST EPSS is a dated prediction of exploitation in the next 30 days. Missing scores are unknown. This collector requests predictions only for explicit CVE aliases in the public NuGet archive; Microsoft release views retain their existing prediction scope.

No collected risk observation for these identifiers. This does not establish absence of exploitation or a zero probability.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

Source published

4 Aug 2026 19:29:14 UTC

Source modified

5 Aug 2026 13:51:57 UTC

Observed here

23 Oct 2026 15:48:27 UTC

Source terms

CVE Program Terms of Use

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CVSS 4.0 · 9.5 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

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

Source published

Aug 4, 2026, 7:29:14 PM (timezone not stated)

Source modified

Aug 5, 2026, 1:51:57 PM (timezone not stated)

Observed here

7 Nov 2026 14:22:25 UTC

Source terms

ENISA EUVD · source terms and attribution apply

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This view projects the publisher's source fields for reading; the original JSON retains additional fields and CVE enrichment containers. Attribution: euvd · ENISA EUVD · source terms and attribution apply. Source text is retained; field grouping and change labels are provided by ChangeIntel.

EUVD CVSS 4.0 · 9.5 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

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

Source published

4 Aug 2026 20:16:54 UTC

Source modified

24 Oct 2026 15:10:00 UTC

Observed here

25 Oct 2026 15:18:57 UTC

Source terms

NIST NVD usage terms; underlying source attribution retained

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This view projects the publisher's source fields for reading; the original JSON retains additional fields and CVE enrichment containers. Attribution: nvd · NIST NVD usage terms; underlying source attribution retained. Source text is retained; field grouping and change labels are provided by ChangeIntel.

CVSS 4.0 · [email protected] · Secondary · 9.5 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/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

CVSS 3.1 · [email protected] · Primary · 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

-- ABSTRACT ------------------------------------- Trend Micro's Zero Day Initiative has identified a vulnerability affecting the following products: Flowise - Flowise -- VULNERABILITY DETAILS ------------------------ * Version tested: 3.1.1 * Installer file: https://github.com/FlowiseAI/Flowise (npm install [email protected]) * Platform tested: Ubuntu 25.10 --- A prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed pyodide environment. An attacker can leverage this to execute arbitrary code in the context of the user running the server. ``` This vulnerability allows remote attackers to execute arbitrary code on affected installations of Flowise. Authentication is not required to exploit this vulnerability. The specific flaw exists within the run method of the CSV_Agents class. The issue results from insufficient input sanitization when using untrusted data to construct an LLM prompt. An attacker can leverage this vulnerability to execute code in the context of the service account. ``` ### Analysis When a user makes a query against a chatflow using the CSV Agent node, the `run` method of the `CSV_Agents` class is called. This method reads the CSV file, loads a pyodide environment, and uses pandas to extract column names and data types into a dictionary. It then constructs a system prompt using that dictionary and the user's input, and sends this prompt to a configured LLM. The LLM response is stored in a variable named `pythonCode`. The method then attempts to validate this value using `validatePythonCodeForDataFrame` from `packages/components/src/pythonCodeValidator.ts` before evaluating it in pyodide. The validator relies on a static regex blocklist. It can be bypassed using obfuscation techniques including string concatenation to reconstruct forbidden identifiers, `chr()` encoding, aliasing of dangerous builtins, `__getattribute__` with concatenated attribute names, frame object inspection, MRO traversal, `df.query()` expression evaluation, and decorator syntax to invoke `exec` indirectly. Furthermore, pyodide is not sandboxed from the host operating system, so any Python code that passes the validator is executed with full access to OS interfaces. From `packages/components/nodes/agents/CSVAgent/CSVAgent.ts`: ```ts let pythonCode = '' if (dataframeColDict) { const chain = new LLMChain({ llm: model, prompt: PromptTemplate.fromTemplate(systemPrompt), verbose: process.env.DEBUG === 'true' ? true : false }) const inputs = { dict: dataframeColDict, question: input // user-controlled input substituted into prompt } const res = await chain.call(inputs, [loggerHandler, ...callbacks]) pythonCode = res?.text // LLM response assigned to pythonCode pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '') } let finalResult = '' if (pythonCode) { const validation = validatePythonCodeForDataFrame(pythonCode) // blocklist validation applied if (!validation.valid) { throw new Error( `Generated code was rejected for security reasons (${ validation.reason ?? 'unsafe construct' }). Please rephrase your question to use only pandas DataFrame operations.` ) } try { const code = `import pandas as pd\nimport numpy as np\n${pythonCode}` finalResult = await pyodide.runPythonAsync(code) // executed in unsandboxed pyodide } catch (error) { throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using following code: "${pythonCode}"`) } } ``` An unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may use prompt injection to cause the LLM to respond with a malicious Python script. An authenticated attacker may instead configure a chatflow that points to an attacker-controlled server, which responds to LLM requests with an attacker-controlled Python payload, bypassing the LLM entirely. Eight bypass variants were demonstrated against the validator: | Variant | Technique | Bypasses | |---------|-----------|----------| | 0 | `@exec` decorator with string-concatenated `__import__` | `/\bexec\s*\(/`, `/\b__import__\s*\(/` | | 1 | `eval` aliased to a variable, payload chr()-encoded | `/\beval\s*\(/`, `/\bimport\b/` | | 2 | `df.query()` with chr()-encoded `@__builtins__.__import__` | `/\b__builtins__\b/`, `/\b__import__\s*\(/` | | 3 | MRO traversal + `__getattribute__` + `__subclasses__` -> `BuiltinImporter.load_module` | `/\b__class__\b/`, `/\b__subclasses__\s*\(/`, `/\b__mro__\b/` | | 4 | Generator frame inspection via `gi_frame.f_globals['__loader__']` | `/\b__loader__\b/`, `/\b__globals__\b/` | | 5 | Exception traceback frame walk to `f_builtins['__import__']` | `/\b__globals__\b/`, `/\b__import__\s*\(/` | | 6 | `__build_class__.__self__.__getattribute__('__import__')` | `/\b__import__\s*\(/` | | 7 | `vars` aliased to a variable, `__builtins__` accessed via dict key | `/\bvars\s*\(/`, `/\b__builtins__\b/`, `/\b__import__\s*\(/` | ### Repro The proof of concept (`poc.py`) has three modes of operation: **mode = "server"**: Starts a malicious server that responds to "/api/chat" requests with a JSON object containing an LLM response with the selected attack payload. **mode = "chatflow"**: Authenticates to the Flowise server, creates a chatflow with a CSV Agent node configured to use a ChatOllama model pointed at the malicious server, and triggers a prediction to execute the payload. **mode = "prompt_injection"**: Sends a prompt injection payload directly to an existing chatflow's prediction endpoint. Due to the nature of LLM responses, it may take multiple attempts or require a different injection technique depending on the model used. ``` python3 poc.py --mode [server OR chatflow OR prompt_injection] [--user <USER> --passwd <PASSWORD> --host <HOST> --r_host <R_HOST> --r_port <R_PORT> --l_port <L_PORT> --port <PORT> --cmd <CMD> --attack <ATTACK> --chatflow_id <CHAT_ID>] ``` -- CREDIT --------------------------------------- This vulnerability was discovered by: Dre Cura (@dre_cura) of TrendAI Research

Source published

4 Aug 2026 19:29:26 UTC

Source modified

4 Aug 2026 19:56:08 UTC

Observed here

24 Oct 2026 13:17:17 UTC

Source terms

GitHub Advisory Database · CC-BY-4.0

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This view projects the publisher's source fields for reading; the original JSON retains additional fields and CVE enrichment containers. Attribution: osv · GitHub Advisory Database · CC-BY-4.0. Source text is retained; field grouping and change labels are provided by ChangeIntel.

CVSS_V4 · 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

Publisher rating · CRITICAL