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CVE-2026-18684 – Command Injection (RCE) – GL.iNet GL-MT3000 firmware 4.4.0 through 4.4.5

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Summary

CVE-2026-18684 is an unauthenticated command injection vulnerability in the GL.iNet GL-MT3000 (Beryl AX) travel router. The flaw resides in the remove_profile function of /cgi-bin/glc, part of the modem.so component, and allows a remote attacker to execute arbitrary operating-system commands. It is rated critical severity.

Technical details

  • Root cause: Improper neutralization of input in the remove_profile function of the /cgi-bin/glc endpoint within the modem.so component (CWE-77 / CWE-74), allowing attacker-controlled data to be passed into an OS command.
  • Trigger conditions: A crafted request to /cgi-bin/glc invoking the affected function; the disclosure indicates the attack requires no authentication or user interaction.
  • Attack vector: Network — remotely exploitable over the device’s web interface.
  • Impact: Command injection leading to arbitrary command execution with full compromise of confidentiality, integrity, and availability of the device. A public exploit is available.

Affected software

  • GL.iNet GL-MT3000 (Beryl AX) firmware versions 4.4.0, 4.4.1, 4.4.2, 4.4.3, 4.4.4, and 4.4.5 (all versions up to and including 4.4.5).

Severity

  • CVSS v3.1 base score: 9.8 (Critical)
  • Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
  • (The vendor-assigned CVSS v4.0 score is 9.3.)

Mitigation and recommended actions

  • Immediate: No fixed firmware version is documented in the public advisory at time of writing. Monitor the GL.iNet security updates page and apply the vendor firmware release that addresses this issue as soon as it is published.
  • If no patch: Do not expose the router’s administrative/web management interface to the internet. Restrict access to the management interface to trusted local networks, disable remote/WAN administration, and place the device behind a firewall that blocks inbound access to the web interface.

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.

  • Page title: GL.iNet, GL-MT3000, Beryl AX, GL.iNet Admin Panel

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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