CVE-2026-9198
Homebrew vulnerability analysis and mitigation

Overview

CVE-2026-9198 is a critical unauthenticated remote code execution (RCE) vulnerability in IBM Langflow OSS affecting versions 1.0.0 through 1.10.0. The flaw allows any network-accessible attacker to chain two API endpoints — /api/v1/auto_login and /api/v1/validate/code — to gain full code execution on default deployments without any credentials. It was published on July 17, 2026, with a patch available in version 1.10.1. The vulnerability carries a CVSS v3.1 base score of 9.8 (Critical) (GitHub Advisory, IBM Advisory).

Technical details

The root cause is classified as CWE-94 (Improper Control of Generation of Code / Code Injection). The /api/v1/auto_login endpoint mints SUPERUSER-level authentication tokens for any network caller without requiring credentials, effectively bypassing all authentication controls. These tokens can then be used to call /api/v1/validate/code, which executes user-supplied code via Python's exec() function server-side. The attack is fully network-based, requires no privileges or user interaction, and is exploitable against default Langflow deployments without any special configuration (GitHub Advisory, IBM Advisory).

Impact

Successful exploitation grants an unauthenticated remote attacker full code execution with the privileges of the Langflow process, resulting in high confidentiality, integrity, and availability impact. Attackers can read or exfiltrate sensitive data (API keys, model configurations, user data), modify or destroy application data, and potentially pivot to other systems accessible from the Langflow host. The vulnerability is rated as having total technical impact by NVD's SSVC analysis, and the attack is automatable at scale against any internet-exposed Langflow instance (GitHub Advisory, IBM Advisory).

Exploitability

A fully functional public proof-of-concept exploit (exploit.py) was published on GitHub by 0xdak on July 21, 2026, approximately four days after the CVE was disclosed. The exploit supports command execution, interactive shell mode, and reverse shell capabilities via command-line flags (-t <target>, -c <cmd>, --shell, --lhost/--lport). Additional PoC repositories have since appeared (e.g., ywh-jfellus and 0xgh057r3c0n on GitHub), and the vulnerability has been indexed by Sploitus. The EPSS score is approximately 1.886% (77th percentile), and as of the latest data there is no confirmed evidence of in-the-wild exploitation or CISA KEV listing (GitHub PoC, GitHub Advisory).

Exploitation steps

  1. Reconnaissance: Identify internet-facing IBM Langflow OSS instances (versions 1.0.0–1.10.0) using tools like Shodan or Censys, searching for Langflow's default port and API paths (e.g., /api/v1/).
  2. Obtain SUPERUSER token: Send an unauthenticated HTTP GET or POST request to /api/v1/auto_login on the target. The endpoint returns a SUPERUSER-level JWT or session token to any caller without requiring credentials.
  3. Prepare code injection payload: Craft a request to /api/v1/validate/code with the obtained token in the Authorization header, embedding an arbitrary Python payload in the request body (e.g., import os; os.system('id') or a reverse shell one-liner).
  4. Execute arbitrary code: Submit the crafted request; the server passes the payload to Python's exec() function and executes it with the privileges of the Langflow process.
  5. Establish persistence or pivot: Use the RCE to deploy a reverse shell (e.g., via --lhost/--lport flags in the public PoC), exfiltrate credentials or data, or move laterally to other systems reachable from the Langflow host (GitHub PoC, GitHub Advisory).

Indicators of compromise

  • Network: Unexpected HTTP requests to /api/v1/auto_login from external or untrusted IP addresses; subsequent requests to /api/v1/validate/code using tokens obtained from auto_login; outbound connections from the Langflow server to unknown external IPs (potential reverse shell callbacks).
  • Logs: Langflow access logs showing sequential calls to /api/v1/auto_login followed by /api/v1/validate/code from the same source IP; Python exec() errors or unusual stack traces in application logs; authentication events for SUPERUSER tokens not associated with legitimate admin sessions.
  • Process: Unusual child processes spawned by the Langflow Python process (e.g., /bin/bash, sh, curl, wget, python3, nc); unexpected network connections initiated by the Langflow process.
  • File System: New or modified files in the Langflow working directory or temp directories (e.g., web shells, downloaded binaries, cron job modifications); unexpected SSH authorized_keys modifications.
  • Detection Rules: Emerging Threats ruleset update (2026-07-31, v11247) includes signatures for this CVE; Nuclei template added to projectdiscovery/nuclei-templates (Emerging Threats, Nuclei Templates).

Mitigation and workarounds

IBM has released IBM Langflow OSS version 1.10.1 as the patched release; upgrading immediately is the recommended remediation (IBM Advisory). If immediate patching is not possible, restrict network access to the /api/v1/auto_login and /api/v1/validate/code endpoints via firewall rules or reverse proxy authentication requirements. Additionally, disabling the auto_login feature in the Langflow configuration (if not operationally required) eliminates the authentication bypass component of the attack chain. Deployments should not expose Langflow directly to the internet without additional authentication controls.

Community reactions

The vulnerability attracted rapid community attention, with multiple independent PoC repositories appearing within days of disclosure and indexing by exploit aggregators such as Sploitus and Vulners. The Emerging Threats ruleset was updated on July 31, 2026 (v11247) to include detection signatures, and a Nuclei detection template was contributed to the projectdiscovery repository on July 29, 2026. Security blogs including CyberHub and TonyHarris.io highlighted the vulnerability in weekly PoC roundups (Emerging Threats, Nuclei Templates).

Additional resources


SourceThis report was generated using AI

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