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Flowise CVE-2026-73487

| EUVDEUVD-2026-57803 CRITICAL
Code Injection (CWE-94)
9.0
CVSS 4.0 · Vendor: VulnCheck
View

Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Reach unauthenticated prediction API
  2. Delivery
    Send prompt-injection query to CSV/Airtable agent
  3. Exploit
    LLM emits Python with unblocked pd.read_json()
  4. Execution
    Payload bypasses regex validator
  5. Persist
    Flowise executes pandas call
  6. Impact
    Exfiltrate data / SSRF / code execution

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A05:2025 - Injection
ASVS 5.0
V1 - Encoding and Sanitization Code injection prevention, sandbox exec
MITRE ATT&CK

2 techniques across 2 tactics: Execution, Initial Access

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Exploitation Conditions

Exploitation requires that the target Flowise deployment (before 3.1.3) expose a flow using the CSV Agent or Airtable Agent node reachable through the prediction API, and that this API be network-accessible without authentication (PR:N per the CVSS vector). The concrete prerequisite is prompt-injection control over the agent's input plus a payload that invokes an unblocked pandas function such as pd.read_json() to slip past the regex validator. Limiting factors: the attack is rated high complexity (AC:H) because it hinges on reliably manipulating the LLM and defeating the blocklist; instances that do not use these specific agent nodes, that gate the prediction API behind authentication, or that are not network-exposed are not practically exploitable.

Impact

The CVSS 4.0 base score is 9.0 (AV:N/AC:H/AT:N/PR:N/UI:N/VC:H/VI:L/VA:N/SC:H/SI:L/SA:N): network-reachable and unauthenticated (PR:N) with high impact to confidentiality of both the vulnerable system and downstream/internal systems (SC:H, reflecting SSRF), but rated high attack complexity (AC:H) because the attacker must reliably steer the LLM and craft payloads that survive the regex validator. That AC:H and the multi-step, model-mediated exploitation path temper the raw severity: this is not a single deterministic request but a prompt-injection-dependent chain, so real-world reliability varies by model and node configuration. No EPSS score, KEV entry, or public POC was provided, so exploitation appears theoretical/reported rather than observed in the wild - treat as a high-priority patch for any internet-exposed Flowise instance that uses the CSV or Airtable Agent nodes, but not an emergency on par with a mass-exploited KEV item. Note the exploitation is contingent on those specific agent nodes being deployed and reachable.

Exploit ScenarioDerived

An attacker submits a crafted query to an internet-exposed Flowise instance's unauthenticated prediction API targeting a CSV or Airtable Agent flow, using prompt injection to make the underlying LLM emit Python that calls an unblocked function like pd.read_json('http://169.254.169.254/...') or a local file path. Because the regex validator does not block this call, Flowise executes it, letting the attacker reach internal services (SSRF), read local datasets, or escalate to code execution. No public POC was provided, and success depends on reliably steering the model past the validator (AC:H).

Act Now Prioritize within 24-48h. Why: CRITICAL severity + public POC available.
Patch available 0.4% exploitation probability (EPSS)

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AnalysisAIv1 – Aug 13, 2026

Remote code execution and SSRF in FlowiseAI Flowise before 3.1.3 stems from a bypassable regex-based Python code validator in the CSV and Airtable Agent nodes, letting attackers smuggle malicious pandas calls through the unauthenticated prediction API via prompt injection. Because the validator blocklists rather than sandboxes, unblocked functions such as pd.read_json() can be abused to exfiltrate local datasets, reach internal services (SSRF), or execute arbitrary code. No public exploit identified at time of analysis, and it is not listed in CISA KEV; the reporting vendor VulnCheck and a GitHub Security Advisory (GHSA-w7x8-q2gp-5cgg) document the issue.

RemediationAI

Upgrade to Flowise 3.1.3 or later (Vendor-released patch: 3.1.3), which addresses the validator bypass - see GHSA-w7x8-q2gp-5cgg. If immediate patching is not possible, remove or disable the CSV Agent and Airtable Agent nodes from any exposed flows so the vulnerable Python execution path cannot be invoked, accepting the trade-off that data-analysis capabilities of those nodes are lost. Additionally, require authentication on the Flowise instance and restrict the prediction API to trusted networks (place it behind a reverse proxy/VPN or IP allowlist) to eliminate unauthenticated access, and apply egress network filtering from the Flowise host to blunt SSRF and data-exfiltration attempts (side effect: legitimate outbound integrations may need explicit allowlisting). These controls reduce exposure but do not fully close the code-injection root cause, so patching remains the primary fix.

Affected VersionsENISA

Flowise 0 <3.1.3

Affected Packages

npm flowise fix: 3.1.3
npm flowise-components fix: 3.1.3

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 2 packages in npm

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

Flowise is an open-source, low-code drag-and-drop builder for LLM/agent workflows built on Node.js, whose CSV and Airtable Agent nodes execute user-influenced Python (via a pandas dataframe agent) to answer data questions. To prevent arbitrary execution, Flowise attempts to gate the generated Python through a regex-based allow/deny validator rather than a real sandbox - the classic weakness behind CWE-94 (Improper Control of Generation of Code / 'Code Injection'). Regex blocklisting of dangerous calls is inherently incomplete: functions like pd.read_json(), which can fetch remote URLs and local files, are not blocked, so an attacker who controls the model's prompt (prompt injection) can coerce the agent into emitting Python that the validator lets through. The affected component is identified in CPE as cpe:2.3:a:flowiseai:flowise across all versions prior to the fixed 3.1.3 release.

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Severity by source

Vendor (VulnCheck) PRIMARY 9.0 CRITICAL
CVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:H/VI:L/VA:N/SC:H/SI:L/SA:N
vuln.today AI 7.5 HIGH

Unauthenticated network access to the prediction API gives PR:N/AV:N, but reliably defeating the regex validator via LLM manipulation is AC:H; SSRF/code execution beyond the app justifies S:C with C:H, I:L.

3.1 AV:N/AC:H/PR:N/UI:N/S:C/C:H/I:L/A:N
4.0 AV:N/AC:H/AT:N/PR:N/UI:N/VC:H/VI:L/VA:N/SC:H/SI:L/SA:N

Primary rating from Vendor (VulnCheck).

CVSS VectorVendor: VulnCheck

Attack Vector
Network
Attack Complexity
High
Attack Requirements
None
Privileges Required
None
User Interaction
None
Vulnerable system: Confidentiality
High
Vulnerable system: Integrity
Low
Vulnerable system: Availability
None
Subsequent system: Confidentiality
High
Subsequent system: Integrity
Low
Subsequent system: Availability
None

Lifecycle Timeline

2
Patch available
Aug 13, 2026 - 13:01 EUVD
CVE Published
Aug 13, 2026 - 11:28 cve.org
CRITICAL 9.0

DescriptionCVE.org

Flowise before 3.1.3 contains a regex-based Python code validator bypass in CSV and Airtable Agent nodes that allows unauthenticated attackers to inject malicious code via prompt injection. Attackers can exploit unblocked pandas functions like pd.read_json() to exfiltrate datasets, perform SSRF against internal services, or achieve code execution through the unauthenticated prediction API.

Executive ViewAI

Why It Matters

FlowiseAI Flowise before version 3.1.3 contains a critical remote code execution vulnerability affecting the unauthenticated prediction API, allowing attackers to execute arbitrary code, exfiltrate sensitive data, or access internal systems through prompt injection that bypasses the platform's Python code validator. No vendor-released patch has been identified at time of analysis, and no public exploit code is known at this time, but the absence of a patch combined with critical severity poses severe organizational risk. Immediate isolation and monitoring of Flowise deployments is required to prevent exploitation.

Business Impact: Severe
Compensating Controls

No vendor-released patch identified at time of analysis. Implement strict network isolation between Flowise and internal services to prevent SSRF-based lateral movement; restrict prediction API access via firewall to pre-approved internal sources only; disable or remove CSV and Airtable Agent nodes if not operationally critical; deploy centralized logging and real-time alerting on suspicious Python function calls, imports, and file operations within Flowise logs; apply Web Application Firewall rules to detect and block common prompt injection payloads; consider running Flowise instances on isolated network segments with minimal internal connectivity.

Threat Intelligence Overview

EPSS Score
0.4%
Top 68% exploitable
Prevalence
Medium
popular open-source LLM app builder, common in AI dev, not ubiquitous enterprise
Threat Intel
1
1 source
Exploit
POC
Public exploit code exists
Data Quality
6 sources
Confidence: High
EUVD 🇯🇵JVN NVD CVE.org EPSS Vulnrichment GHSA GitHub VulDB

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity CRITICAL
CISA KEV No
Public POC Yes
EPSS 0.4%
Patch available Yes
Active threat No
Deciding rule
CRITICAL severity + public POC available
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision
Out-of-Cycle
Exploitation
Poc
System Exposure· derived
Open
Automatable
Yes
Human Impact
Very High
Technical Impact
Total

Priority Score

65
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +45
PoC: +20
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

Server-Side Request Forgery exploits applications that fetch remote resources based on user-supplied URLs.

Code Injection CRITICAL

Code injection occurs when an application accepts user input and passes it directly into a language interpreter or evaluator without proper sanitization.

Remote Code Execution represents the critical moment when an attacker successfully runs arbitrary code on a target system without physical access.

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