Vulnerability-Lookup

GHSA-RQHF-RG9P-W5GG

Vulnerability from github – Published: 2026-07-21 21:32 – Updated: 2026-07-23 18:30

Details

lmdeploy's OpenAI-compatible API server contains a server-side request forgery vulnerability that allows unauthenticated attackers to access internal services and cloud metadata endpoints by supplying a crafted image_url that redirects to internal targets. Attackers can send a POST request to the chat completions endpoint with an image_url pointing to an attacker-controlled server that responds with an HTTP 302 redirect to internal addresses such as loopback or instance-metadata endpoints, bypassing the initial URL safety check because redirects are followed without re-validating each hop through the safety guard.

Severity

9.3 (Critical)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:L/A:N

9.2 (Critical)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:L/SC:N/SI:H/SA:L

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JSON

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  "affected": [],
  "aliases": [
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  "database_specific": {
    "cwe_ids": [
      "CWE-918"
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    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-21T21:16:53Z",
    "severity": "CRITICAL"
  },
  "details": "lmdeploy\u0027s OpenAI-compatible API server contains a server-side request forgery vulnerability that allows unauthenticated attackers to access internal services and cloud metadata endpoints by supplying a crafted image_url that redirects to internal targets. Attackers can send a POST request to the chat completions endpoint with an image_url pointing to an attacker-controlled server that responds with an HTTP 302 redirect to internal addresses such as loopback or instance-metadata endpoints, bypassing the initial URL safety check because redirects are followed without re-validating each hop through the safety guard.",
  "id": "GHSA-rqhf-rg9p-w5gg",
  "modified": "2026-07-23T18:30:56Z",
  "published": "2026-07-21T21:32:42Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-63764"
    },
    {
      "type": "WEB",
      "url": "https://github.com/InternLM/lmdeploy/issues/4761"
    },
    {
      "type": "WEB",
      "url": "https://github.com/InternLM/lmdeploy/pull/4734"
    },
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      "type": "WEB",
      "url": "https://github.com/InternLM/lmdeploy/commit/03c313006d17cc3feae86b633c44206a997c44db"
    },
    {
      "type": "WEB",
      "url": "https://github.com/InternLM/lmdeploy"
    },
    {
      "type": "WEB",
      "url": "https://www.vulncheck.com/advisories/lmdeploy-server-side-request-forgery-via-http-redirect-bypass"
    },
    {
      "type": "WEB",
      "url": "https://www.vulncheck.com/advisories/lmdeploy-server-side-request-forgery-via-http-redirect-bypass-of-private-ip-guard-in-vision-image-fetch"
    }
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}

CVE-2026-63764 (GCVE-0-2026-63764)

Vulnerability from cvelistv5 – Published: 2026-07-21 20:36 – Updated: 2026-07-23 17:47 X_Open Source

Title

LMDeploy Server-Side Request Forgery via HTTP Redirect Bypass

Summary

LMDeploy through 0.14.0, fixed in commit 03c3130, contains a server-side request forgery (SSRF) vulnerability in the _load_http_url function within the connection.py media handler, where the private-IP guard validates only the original URL without re-validating hosts after HTTP redirects. An unauthenticated attacker can submit a crafted image_url to the chat completions endpoint pointing to an attacker-controlled host that returns a redirect to a private IP or cloud-metadata endpoint, causing the server to follow the redirect and expose internal service content through the model pipeline.

Severity

7.7 (High)


                        
                          CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:N/SC:H/SI:N/SA:N

8.6 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:N/A:N

SSVC

Exploitation: poc Automatable: yes Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-07-23 14:12 UTC

CWE

CWE-918 - Server-Side Request Forgery (SSRF)

Assigner

VulnCheck mitre root

References

4 references

URL	Tags
https://github.com/InternLM/lmdeploy/issues/4761	technical-descriptionexploit
https://github.com/InternLM/lmdeploy/pull/4734	issue-tracking
https://github.com/InternLM/lmdeploy/commit/03c31…	patch
https://www.vulncheck.com/advisories/lmdeploy-ser…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version
InternLM	lmdeploy	Affected: 0 , ≤ 0.14.0 (semver) Unaffected: 03c313006d17cc3feae86b633c44206a997c44db (git) cpe:2.3:a:internlm:lmdeploy::::::::

Date Public

2026-07-18 00:00

Credits

George Chen

Show details on NVD website

JSON

To clipboard

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Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.