CVE-2026-33873: Langflow server-side RCE

Q: Is CVE-2026-33873 actively exploited?

No confirmed active exploitation of CVE-2026-33873 has been reported, but organizations should still patch proactively.

Q: How to fix CVE-2026-33873?

1. PATCH: Upgrade langflow to 1.9.0 via pip immediately. 2. DISABLE: If patching is not immediately feasible, restrict or disable access to the Agentic Assistant API endpoint (/api/v1/agentic or equivalent). 3. SANDBOX: Run Langflow in a container with no-root, read-only filesystem where possible, network egress restrictions, and secrets injected via environment — not hardcoded. 4. RESTRICT ACCESS: Enforce authentication and authorization on all Langflow endpoints; do not expose to the internet without strict ACLs. 5. DETECT: Alert on unexpected subprocess spawning, outbound network connections, and file writes from the Langflow process. Review logs for Agentic Assistant usage prior to patching — any such session should be treated as potentially malicious. 6. ROTATE: If exploitation cannot be ruled out, rotate all credentials accessible to the Langflow process (LLM API keys, DB passwords, cloud tokens).

Q: What systems are affected by CVE-2026-33873?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, LLM application development platforms, AI workflow automation, MLOps pipelines.

Q: What is the CVSS score for CVE-2026-33873?

No CVSS score has been assigned yet.

CISO Take

Any user who can access the Agentic Assistant in Langflow <= 1.8.1 can achieve full server-side code execution by influencing what Python the LLM generates — no exploit tooling required, just a crafted prompt. Patch to 1.9.0 immediately or disable the Agentic Assistant feature. Treat any Langflow instance accessible to untrusted users as fully compromised until patched.

What is the risk?

HIGH risk despite absent CVSS. The impact ceiling is full server compromise (RCE), and the attack barrier is low: access to the Agentic Assistant plus a crafted prompt is sufficient. Langflow instances routinely hold high-value credentials (LLM API keys, database connections, cloud IAM tokens) in environment variables, making post-exploitation impact severe. Exposure is significant — Langflow is commonly deployed as an internal developer tool with broad internal access, and some instances are internet-facing.

What systems are affected?

Package	Ecosystem	Vulnerable Range	Patched
Langflow	pip	<= 1.8.1	`1.9.0`
150.1K Pushed 6d ago 40% patched ~67d to patch Full package profile →

Do you use Langflow? You're affected.

How severe is it?

CVSS 3.1

N/A

EPSS

1.4%

chance of exploitation in 30 days

Higher than 70% of all CVEs

Source: EPSS v3 — FIRST.org

Exploitation Status

No known exploitation

Sophistication

Trivial

What should I do?

6 steps

PATCH

Upgrade langflow to 1.9.0 via pip immediately.
DISABLE

If patching is not immediately feasible, restrict or disable access to the Agentic Assistant API endpoint (/api/v1/agentic or equivalent).
SANDBOX

Run Langflow in a container with no-root, read-only filesystem where possible, network egress restrictions, and secrets injected via environment — not hardcoded.
RESTRICT ACCESS

Enforce authentication and authorization on all Langflow endpoints; do not expose to the internet without strict ACLs.
DETECT

Alert on unexpected subprocess spawning, outbound network connections, and file writes from the Langflow process. Review logs for Agentic Assistant usage prior to patching — any such session should be treated as potentially malicious.
ROTATE

If exploitation cannot be ruled out, rotate all credentials accessible to the Langflow process (LLM API keys, DB passwords, cloud tokens).

What does CISA's SSVC say?

Decision Track

Exploitation none

Automatable No

Technical Impact total

Source: CISA Vulnrichment (SSVC v2.0). Decision based on the CISA Coordinator decision tree.

How is it classified?

Code Execution Prompt Injection Framework Agent AML.T0049 - Exploit Public-Facing Application AML.T0050 - Command and Scripting Interpreter AML.T0051.000 - Direct AML.T0053 - AI Agent Tool Invocation AML.T0072 - Reverse Shell AML.T0102 - Generate Malicious Commands

Which compliance frameworks are affected?

This CVE is relevant to:

EU AI Act

Art.15 - Accuracy, Robustness and Cybersecurity

ISO 42001

8.4 - AI System Operation and Monitoring

NIST AI RMF

MANAGE-2.2 - Mechanisms to sustain the value of deployed AI

OWASP LLM Top 10

LLM01:2025 - Prompt Injection LLM05:2025 - Improper Output Handling LLM06:2025 - Excessive Agency

Frequently Asked Questions

What is CVE-2026-33873?

Any user who can access the Agentic Assistant in Langflow <= 1.8.1 can achieve full server-side code execution by influencing what Python the LLM generates — no exploit tooling required, just a crafted prompt. Patch to 1.9.0 immediately or disable the Agentic Assistant feature. Treat any Langflow instance accessible to untrusted users as fully compromised until patched.

Is CVE-2026-33873 actively exploited?

No confirmed active exploitation of CVE-2026-33873 has been reported, but organizations should still patch proactively.

How to fix CVE-2026-33873?

1. PATCH: Upgrade langflow to 1.9.0 via pip immediately. 2. DISABLE: If patching is not immediately feasible, restrict or disable access to the Agentic Assistant API endpoint (/api/v1/agentic or equivalent). 3. SANDBOX: Run Langflow in a container with no-root, read-only filesystem where possible, network egress restrictions, and secrets injected via environment — not hardcoded. 4. RESTRICT ACCESS: Enforce authentication and authorization on all Langflow endpoints; do not expose to the internet without strict ACLs. 5. DETECT: Alert on unexpected subprocess spawning, outbound network connections, and file writes from the Langflow process. Review logs for Agentic Assistant usage prior to patching — any such session should be treated as potentially malicious. 6. ROTATE: If exploitation cannot be ruled out, rotate all credentials accessible to the Langflow process (LLM API keys, DB passwords, cloud tokens).

What systems are affected by CVE-2026-33873?

This vulnerability affects the following AI/ML architecture patterns: agent frameworks, LLM application development platforms, AI workflow automation, MLOps pipelines.

What is the CVSS score for CVE-2026-33873?

No CVSS score has been assigned yet.

What is the AI security impact?

Affected AI Architectures

agent frameworksLLM application development platformsAI workflow automationMLOps pipelines

MITRE ATLAS Techniques

AML.T0049 Exploit Public-Facing Application

AML.T0050 Command and Scripting Interpreter

AML.T0051.000 Direct

AML.T0053 AI Agent Tool Invocation

AML.T0072 Reverse Shell

AML.T0102 Generate Malicious Commands

Compliance Controls Affected

EU AI Act: Art.15

ISO 42001: 8.4

NIST AI RMF: MANAGE-2.2

OWASP LLM Top 10: LLM01:2025, LLM05:2025, LLM06:2025

What are the technical details?

Original Advisory

Langflow is a tool for building and deploying AI-powered agents and workflows. Prior to version 1.9.0, the Agentic Assistant feature in Langflow executes LLM-generated Python code during its validation phase. Although this phase appears intended to validate generated component code, the implementation reaches dynamic execution sinks and instantiates the generated class server-side. In deployments where an attacker can access the Agentic Assistant feature and influence the model output, this can result in arbitrary server-side Python execution. Version 1.9.0 fixes the issue.

Exploitation Scenario

An attacker with access to a Langflow instance — an internal developer, a compromised account, or an unauthenticated user on an internet-exposed deployment — opens the Agentic Assistant and submits a prompt designed to steer the LLM toward generating a 'component' that embeds malicious Python: e.g., a reverse shell, credential dump from environment variables, or file exfiltration. Because Langflow's validation phase executes the generated code server-side and instantiates the generated class, the payload runs with the privileges of the Langflow process. The attacker exfiltrates all environment variables (LLM API keys, DB credentials, cloud tokens), establishes persistence, and pivots to connected infrastructure. No special tooling or AI/ML expertise is required — the LLM does the heavy lifting.

Weaknesses (CWE)

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

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.

[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.

Source: MITRE CWE corpus.