CVE-2026-54339 (GCVE-0-2026-54339)
Vulnerability from cvelistv5 – Published: 2026-09-17 20:21 – Updated: 2026-09-21 21:14
VLAI
EPSS
VEX
Title
Glean: Server-Side Request Forgery (SSRF) with Full Response Disclosure via Malicious RSS Feed in /api/feeds/discover
Summary
Glean is a self-hosted RSS reader and personal knowledge management tool. Prior to 0.2.6, POST /api/feeds/discover passes an attacker-supplied feed_url to discover_feed(feed_url), creates a subscription through FeedService.create_subscription(), and enqueues fetch_feed_task. The background path calls fetch_feed(feed.url) and parse_feed(), which assigns each RSS item link to ParsedEntry.url. The task then passes ParsedEntry.url to fetch_and_extract_fulltext(parsed_entry.url) without network-level validation in backend/packages/rss/glean_rss/extractor.py and backend/apps/worker/glean_worker/tasks/feed_fetcher.py. A malicious feed can therefore make the server request private, loopback, link-local, or cloud-metadata resources. The fetched response is stored in Entry.content and can be retrieved through GET /api/entries/{id}, producing non-blind server-side request forgery with full response disclosure. This can bypass network perimeters, probe internal services and ports, expose internal configuration or web content, and potentially disclose cloud metadata access tokens. This issue is fixed in version 0.2.6.
Severity
7.7 (High)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-21 21:14 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/LeslieLeung/glean/security/adv… | x_refsource_CONFIRM |
| https://github.com/LeslieLeung/glean/commit/cdc05… | x_refsource_MISC |
| https://github.com/LeslieLeung/glean/releases/tag… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| LeslieLeung | glean |
Affected:
< 0.2.6
|
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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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