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CVE-2026-19297 – Authentication Bypass via Brute Force (Missing Rate Limiting) – IBM Langflow OSS 1.

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Summary

CVE-2026-19297 is a critical vulnerability in IBM Langflow OSS caused by improper restriction of excessive authentication attempts (CWE-307) on the login endpoint. A remote, unauthenticated attacker can repeatedly attempt credentials against the login endpoint without being throttled or locked out, allowing brute-force access to user accounts. The issue carries a CVSS v3.1 base score of 9.1 (Critical).

Technical details

  • Root cause: The Langflow OSS login endpoint does not enforce adequate protections (such as rate limiting or account lockout) against repeated authentication attempts.
  • Trigger conditions: An attacker sends repeated login requests to the exposed login endpoint of an internet-facing Langflow OSS instance.
  • Attack vector: Network-based, no privileges or user interaction required, low attack complexity.
  • Impact: Successful brute forcing grants unauthorized access to user accounts, with high impact to confidentiality and integrity; there is no direct impact to availability.

Affected software

  • IBM Langflow OSS versions 1.0.0 through 1.9.6

Severity

  • CVSS v3.1 Base Score: 9.1 (Critical)
  • Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

Mitigation and recommended actions

  • Immediate: Upgrade to Langflow OSS version 1.10.0 or later, available via PyPI. Version 1.10.0 introduces IP-based rate limiting on the /login endpoint, returning HTTP 429 with a Retry-After header once a configured attempt threshold is exceeded.
  • If patching is not immediately possible: Restrict network exposure of the Langflow login endpoint (e.g., place it behind a VPN, IP allowlist, or reverse proxy with rate limiting/WAF rules), enforce strong, unique account passwords, and monitor authentication logs for repeated failed login attempts from the same source.

How IONIX identifies potentially affected assets

IONIX matches the following signals against data already collected when it crawled the asset; identifying the technology sends no request beyond that crawl.

  • Raw response body: <title>Langflow</title> followed by a <script src="./assets/index-*.js"> tag.

References

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How IONIX’s External Exposure Management Platform Detects and Validates
Zero-Days to Shrink MTTR

1

Map your entire attack surface (continously)

IONIX uses multi-factor discovery methods, including DNS analysis, certificate mapping, metadata inspection, and more, to automatically map every internet-facing asset across your environment. This includes cloud instances, third-party platforms, shadow IT, and even forgotten infrastructure that traditional tools miss.

2

Monitor for new CVEs

Dozens of threat intel feeds using agentic technology are continuously analyzed to detect the appearance of proof-of-concept code, exploit kits, and indicators of active targeting. IONIX goes further by applying AI to proactively evaluate whether emerging vulnerabilities are likely to be exploited, even before PoCs go public.

3

Identify Potential External Exposures

Not all CVEs matter. IONIX filters vulnerabilities by asking attacker-centric questions: Can it be reached from the internet? Does it require authentication? Is it being exploited in the wild? This dramatically reduces noise and focuses teams on threats that can actually be weaponized.

4

Create Safe, Scalable Exploit Validations

IONIX transforms real-world PoCs into safe, non-intrusive test payloads that can be run in production environments without disruption. These simulations are precisely targeted to the systems that are vulnerable, ensuring rapid validation without unnecessary load.

5

Execute Exploit Validations

By combining context about software stack, versioning, exposure status, and reachability, IONIX ensures that only the right payloads are executed against the right assets, maximizing efficiency and minimizing risk.

6

Drive Fast and Actionable Remediation

Results are routed through integrations with ticketing, SOAR, and SIEM tools. Issues are written in plain language, bundled into remediation clusters, and prioritized based on asset criticality, exploitability, and blast radius. This shortens mean time to remediation (MTTR) and empowers teams to act with confidence.

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  • Mapping of all Assets with this Technology
  • Identification of potentially exposed assets to this CVE
  • Confirmation of verified exploitable assets
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