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CVE-2026-14890 SGLang CVE debrief

CVE-2026-14890 is a critical vulnerability in SGLang's expert-parallel backup subsystem. The subsystem exposes a ZeroMQ PULL socket on a routable network interface without authentication or deserialization safeguards. This allows an attacker to provide a malicious pickle file, resulting in unauthenticated remote code execution when the feature is enabled and the service is reachable over the network. Organizations using SGLang with the expert-parallel backup feature enabled should prioritize patching. Network administrators and security teams should assess their exposure and implement compensating controls if necessary.

Vendor
SGLang
Product
Unknown
CVSS
CRITICAL 9.1
CISA KEV
Not listed in stored evidence
Original CVE published
2026-07-16
Original CVE updated
2026-08-10
Advisory published
2026-07-16
Advisory updated
2026-08-10

Who should care

Organizations using SGLang with the expert-parallel backup feature enabled should prioritize patching. Network administrators and security teams should assess their exposure and implement compensating controls if necessary. SGLang users with the expert-parallel backup feature enabled are at risk of unauthenticated remote code execution.

Technical summary

The vulnerability exists in SGLang's expert-parallel backup subsystem, which exposes a ZeroMQ PULL socket without authentication or deserialization safeguards. An attacker can exploit this by providing a malicious pickle file, leading to unauthenticated remote code execution when the feature is enabled and the service is network-reachable. The expert-parallel backup feature in SGLang is exposed on a routable network interface without authentication or deserialization safeguards.

Defensive priority

High priority should be given to patching SGLang installations with the expert-parallel backup feature enabled. In the absence of a patch, consider disabling the feature or restricting network access to the service.

Recommended defensive actions

  • Apply the vendor's official patch as soon as available.
  • Disable the expert-parallel backup feature if not in use.
  • Restrict network access to the ZeroMQ PULL socket.
  • Monitor for suspicious activity related to SGLang services.
  • Inventory SGLang installations and assess exposure.
  • Review compensating controls for exposed systems while remediation is scheduled and verified.
  • Track exceptions, retest remediated assets, and close the item only after evidence is documented.

Evidence notes

The CVE record was published on 2026-07-16T16:19:00.413Z and last modified on 2026-07-16T19:16:44.403Z. The NVD entry is currently Awaiting Analysis. References include CERT and GitHub links. The expert-parallel backup feature in SGLang is exposed on a routable network interface without authentication or deserialization safeguards, allowing an attacker to provide a malicious pickle file. This results in unauthenticated remote code execution when the feature is enabled and the service is reachable over the network. The vulnerability exists in SGLang's expert-parallel backup subsystem. The CVE record was last modified on 2026-07-16T19:16:44.403Z.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-14890 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-14890

    CVE Program - Official CVE Program record with source-provided CVE metadata.

  • CVE-2026-14890 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-14890

    NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.

Supplemental references

  • Source reference

    Unverified legacy reference

    URL: https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/elastic_ep/expert_backup_manager.py

    [email protected]

  • Source reference

    Unverified legacy reference

    URL: https://www.kb.cert.org/vuls/id/326070

    af854a3a-2127-422b-91ae-364da2661108

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.