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VulnerabilityPublished

CVE-2026-92971: InternLM lmdeploy · Reachable Assertion

Affects InternLM lmdeploySource CVE Database V5

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Not known to be exploited · CVSS 8.7 High : upgrade to 0.17.1 soon.

Data as of 1 Nov 2026, 11:03 UTC

CVSS 4.0 base score
8.7High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.7% chance of exploitation in 30 days – higher than 53% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.17.1
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 17 Sep 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 1 Nov 2026.

What to do

Upgrade internlm/lmdeploy to 0.17.1.

Affected software

ProductAffectedFixed in
InternLMlmdeployAffected
  • <=0.17.0
Fixed in–
GitHubinternlm/lmdeploypkg:github/internlm/lmdeployOther threatsAffected
  • <0.17.1
Fixed in
  • 0.17.1

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

If you cannot patch yet, consider implementing input validation or filtering to block migration_request messages with empty remote_block_ids if possible.

Description

InternLM LMDeploy through 0.17.0 contains a reachable assertion vulnerability in the DistServe decode migration loop that allows unauthenticated attackers to terminate the inference engine. Attackers can submit a migration_request with an empty remote_block_ids list to trigger an AssertionError that crashes the engine loop and causes subsequent inference requests to fail.

References

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