CVE-2026-54738 (GCVE-0-2026-54738)
Vulnerability from cvelistv5 – Published: 2026-08-19 20:27 – Updated: 2026-08-21 21:48
VLAI
EPSS
VEX
Title
Lemmy: Rate limit bypass via X-Forwarded-For header spoofing in actix-web ConnectionInfo
Summary
Lemmy is a link aggregator and forum for the fediverse. Prior to 0.19.19 and 1.0.0-beta.1, actix-web ConnectionInfo::realip_remote_addr reads the first value of X-Forwarded-For as the client address used by raw_ip_key in crates/utils/src/rate_limit/mod.rs. Lemmy's bundled docker/nginx.conf uses $proxy_add_x_forwarded_for instead of $remote_addr, which appends the real client address to an X-Forwarded-For value supplied by the client. An unauthenticated attacker can therefore place a different spoofed address first on each request and receive a new rate-limit bucket, bypassing limits on POST /api/v4/account/auth/register, POST /api/v4/account/auth/login, POST /api/v4/post, POST /api/v4/comment, GET /api/v4/search, POST /api/v4/image, and POST /api/v4/account/import_settings. This permits excessive account creation, brute-force attempts, spam, scraping, uploads, and repeated imports. This issue is fixed in versions 0.19.19 and 1.0.0-beta.1.
Severity
6.5 (Medium)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-21 21:09 UTC
CWE
- CWE-799 - Improper Control of Interaction Frequency
Assigner
References
8 references
| URL | Tags |
|---|---|
| https://github.com/LemmyNet/lemmy/security/adviso… | x_refsource_CONFIRM |
| https://github.com/LemmyNet/lemmy/pull/6574 | x_refsource_MISC |
| https://github.com/LemmyNet/lemmy/pull/6575 | x_refsource_MISC |
| https://github.com/LemmyNet/lemmy/commit/41513c89… | x_refsource_MISC |
| https://github.com/LemmyNet/lemmy/commit/8b5b2aa7… | x_refsource_MISC |
| https://github.com/LemmyNet/lemmy/releases/tag/0.19.19 | x_refsource_MISC |
| https://github.com/LemmyNet/lemmy/releases/tag/1.… | x_refsource_MISC |
| https://join-lemmy.org/news/2026-06-09_-_Lemmy_Re… | x_refsource_MISC |
Impacted products
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
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.
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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.
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.
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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.
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