CVE-2026-48737 (GCVE-0-2026-48737)
Vulnerability from cvelistv5 – Published: 2026-09-15 14:40 – Updated: 2026-09-16 16:04
VLAI
EPSS
VEX
Title
pyLoad: SSRF guard bypass via IPv6 6to4/NAT64 transition wrappers of internal IPs
Summary
pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev101, is_global_address in src/pyload/core/utils/web/check.py relies on Python's global-address classification without examining IPv4 destinations embedded in 6to4 or NAT64 IPv6 addresses. A low-privileged user can submit an IPv6 literal through parse_urls to the pre-resolution is_global_host guard. Because host_to_ip is pinned to AF_INET, that guard does not evaluate a hostname's AAAA result. Separately, curl resolves hostnames before the pycurl PREREQFUNC in src/pyload/core/network/http/http_request.py applies the same vulnerable is_global_address check to the actual connection address, so a transition-form AAAA result can be permitted even when it terminates at an embedded loopback, private, CGNAT, or link-local IPv4 address. Exploitation requires the pyLoad host to route the applicable transition mechanism, including 6to4 on affected Python 3.9 through 3.11 deployments or NAT64 on a network with a NAT64 gateway. Successful exploitation can enable internal-network reconnaissance, timing-based confirmation, limited service disruption, or cloud metadata disclosure where the wrapped address is routable. This issue is fixed in version 0.5.0b3.dev101.
Severity
4.9 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-16 16:04 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/pyload/pyload/security/advisor… | x_refsource_CONFIRM |
| https://github.com/pyload/pyload/commit/ba7491046… | 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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