Vulnerability-Lookup

CVE-2026-92395 (GCVE-0-2026-92395)

Vulnerability from cvelistv5 – Published: 2026-09-16 14:35 – Updated: 2026-09-17 18:39

Title

@fastify/proxy-addr vulnerable to IP spoofing via IPv4-mapped IPv6 trust subnet

Summary

@fastify/proxy-addr is a Fastify plugin that determines a request's client address behind trusted reverse proxies, and it backs Fastify request.ip and request.ips. In versions 3.0.0 through 5.1.0, a trust subnet written in IPv4-mapped IPv6 notation with an IPv4-sized prefix, such as ::ffff:10.0.0.0/8 instead of the correct ::ffff:10.0.0.0/104, is accepted without error but trusts every IPv4 address on the internet rather than the block it names. Because the socket peer then becomes trusted at hop 0, any unauthenticated client can supply an arbitrary X-Forwarded-For header and control the address the application reads, which defeats IP-based access control, rate limiting, geolocation, and audit logging. The plugin inherited this defect from the upstream proxy-addr module (CVE-2026-90711). The issue is fixed in @fastify/proxy-addr 5.1.1, and users should upgrade to 5.1.1 or later. As a workaround, ensure any IPv4-mapped IPv6 trust subnet uses a prefix length of at least 97, or express the range in plain IPv4 notation.

Severity

9.1 (Critical)


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

SSVC

Exploitation: none Automatable: yes Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-09-17 18:38 UTC

CWE

CWE-290 - Authentication Bypass by Spoofing
CWE-348 - Use of Less Trusted Source
CWE-697 - Incorrect Comparison

Assigner

openjs redhat root

References

2 references

URL	Tags
https://github.com/fastify/proxy-addr/security/ad…
https://cna.openjsf.org/security-advisories.html

Impacted products

1 product

Vendor	Product	Version	CPE status
@fastify/proxy-addr	@fastify/proxy-addr	Affected: 3.0.0 , < 5.1.1 (semver) Unaffected: 5.1.1 (semver)	guessed

Credits

kagebunsher UlisesGascon mcollina kustundag

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-92395

Vulnerability from fkie_nvd - Published: 2026-09-16 15:19 - Updated: 2026-09-17 19:17

Severity

9.1 (Critical) - CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N

Summary

References

	URL	Tags
	ce714d77-add3-4f53-aff5-83d477b104bb	https://cna.openjsf.org/security-advisories.html
	ce714d77-add3-4f53-aff5-83d477b104bb	https://github.com/fastify/proxy-addr/security/advisories/GHSA-8cmm-mhw6-v7xq

Impacted products

	Vendor	Product	Version

JSON

To clipboard

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  ],
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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.