Vulnerability-Lookup

CNVD-2020-20393

Vulnerability from cnvd - Published: 2020-03-31

Title

PySAML2 XML签名包装漏洞

Description

PySAML2是SAML2的纯Python实现。 PySAML2 5.0.0之前版本存在XML签名包装漏洞。该漏洞源于PySAML2不检查SAML文档中的签名是否已封装。攻击者可利用该漏洞影响已签名断言的验证。

Severity

中

Patch Name

PySAML2 XML签名包装漏洞的补丁

Patch Description

PySAML2是SAML2的纯Python实现。 PySAML2 5.0.0之前版本存在XML签名包装漏洞。该漏洞源于PySAML2不检查SAML文档中的签名是否已封装。攻击者可利用该漏洞影响已签名断言的验证。目前，供应商发布了安全公告及相关补丁信息，修复了此漏洞。

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://github.com/IdentityPython/pysaml2/releases/tag/v5.0.0

Reference

https://nvd.nist.gov/vuln/detail/CVE-2020-5390

Impacted products

Name
PySAML2 PySAML2 <5.0.0

Show details on source website

JSON

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{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-5390",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-5390"
    }
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  "description": "PySAML2\u662fSAML2\u7684\u7eafPython\u5b9e\u73b0\u3002\n\nPySAML2 5.0.0\u4e4b\u524d\u7248\u672c\u5b58\u5728XML\u7b7e\u540d\u5305\u88c5\u6f0f\u6d1e\u3002\u8be5\u6f0f\u6d1e\u6e90\u4e8ePySAML2\u4e0d\u68c0\u67e5SAML\u6587\u6863\u4e2d\u7684\u7b7e\u540d\u662f\u5426\u5df2\u5c01\u88c5\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u5f71\u54cd\u5df2\u7b7e\u540d\u65ad\u8a00\u7684\u9a8c\u8bc1\u3002",
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  "number": "CNVD-2020-20393",
  "openTime": "2020-03-31",
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  "products": {
    "product": "PySAML2 PySAML2 \u003c5.0.0"
  },
  "referenceLink": "https://nvd.nist.gov/vuln/detail/CVE-2020-5390",
  "serverity": "\u4e2d",
  "submitTime": "2020-01-14",
  "title": "PySAML2 XML\u7b7e\u540d\u5305\u88c5\u6f0f\u6d1e"
}

CVE-2020-5390 (GCVE-0-2020-5390)

Vulnerability from cvelistv5 – Published: 2020-01-13 18:11 – Updated: 2024-08-04 08:30

Summary

PySAML2 before 5.0.0 does not check that the signature in a SAML document is enveloped and thus signature wrapping is effective, i.e., it is affected by XML Signature Wrapping (XSW). The signature information and the node/object that is signed can be in different places and thus the signature verification will succeed, but the wrong data will be used. This specifically affects the verification of assertion that have been signed.

Severity

No CVSS data available.

CWE

Assigner

mitre mitre root

References

8 references

URL	Tags
https://github.com/IdentityPython/pysaml2/commit/…	x_refsource_CONFIRM
https://github.com/IdentityPython/pysaml2/commit/…	x_refsource_CONFIRM
https://github.com/IdentityPython/pysaml2/release…	x_refsource_CONFIRM
https://github.com/IdentityPython/pysaml2/releases	x_refsource_CONFIRM
https://pypi.org/project/pysaml2/5.0.0/	x_refsource_MISC
https://usn.ubuntu.com/4245-1/	vendor-advisoryx_refsource_UBUNTU
https://www.debian.org/security/2020/dsa-4630	vendor-advisoryx_refsource_DEBIAN
https://lists.debian.org/debian-lts-announce/2020…	mailing-listx_refsource_MLIST

Show details on NVD website

JSON

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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.