Vulnerability-Lookup

SUSE-SU-2021:3209-1

Vulnerability from csaf_suse - Published: 2021-09-23 14:20 - Updated: 2026-09-20 14:06

Summary

Security update for python-reportlab

Severity

Moderate

Notes

Title of the patch: Security update for python-reportlab

Description of the patch: This update for python-reportlab fixes the following issues: - CVE-2020-28463: Fixed Server-side Request Forgery via img tags (bsc#1182503). - Add missing import in CVE-2020-28463 fix (bsc#1190110).

Patchnames: SUSE-2021-3209,SUSE-SLE-WE-12-SP5-2021-3209

CVE-2020-28463

5 (Medium)


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

Affected products

Recommended 3 products

Product	Version	Remediation
Unresolved product id: SUSE Linux Enterprise Server 12 SP5:python-reportlab-0:2.7-3.8.1.x86_64	—	Vendor Fix
Unresolved product id: SUSE Linux Enterprise Server for SAP Applications 12 SP5:python-reportlab-0:2.7-3.8.1.x86_64	—	Vendor Fix
Unresolved product id: SUSE Linux Enterprise Workstation Extension 12 SP5:python-reportlab-0:2.7-3.8.1.x86_64	—	Vendor Fix

Threats

Impact moderate

References

9 references

URL	Category
https://www.suse.com/support/security/rating/	external
https://ftp.suse.com/pub/projects/security/csaf/s…	self
https://www.suse.com/support/update/announcement/…	self
https://lists.suse.com/pipermail/sle-security-upd…	self
https://bugzilla.suse.com/1182503	self
https://bugzilla.suse.com/1190110	self
https://www.suse.com/security/cve/CVE-2020-28463/	self
https://www.suse.com/security/cve/CVE-2020-28463	external
https://bugzilla.suse.com/1182503	external

JSON

To clipboard

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          "text": "All versions of package reportlab are vulnerable to Server-side Request Forgery (SSRF) via img tags. In order to reduce risk, use trustedSchemes \u0026 trustedHosts (see in Reportlab\u0027s documentation) Steps to reproduce by Karan Bamal: 1. Download and install the latest package of reportlab 2. Go to demos -\u003e odyssey -\u003e dodyssey 3. In the text file odyssey.txt that needs to be converted to pdf inject \u003cimg src=\"http://127.0.0.1:5000\" valign=\"top\"/\u003e 4. Create a nc listener nc -lp 5000 5. Run python3 dodyssey.py 6. You will get a hit on your nc showing we have successfully proceded to send a server side request 7. dodyssey.py will show error since there is no img file on the url, but we are able to do SSRF",
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            "SUSE Linux Enterprise Workstation Extension 12 SP5:python-reportlab-0:2.7-3.8.1.x86_64"
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          "date": "2021-09-23T14:20:58Z",
          "details": "moderate"
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      "title": "CVE-2020-28463"
    }
  ]
}

CVE-2020-28463 (GCVE-0-2020-28463)

Vulnerability from cvelistv5 – Published: 2021-02-18 16:00 – Updated: 2024-09-17 01:27

Title

Server-side Request Forgery (SSRF)

Summary

All versions of package reportlab are vulnerable to Server-side Request Forgery (SSRF) via img tags. In order to reduce risk, use trustedSchemes & trustedHosts (see in Reportlab's documentation) Steps to reproduce by Karan Bamal: 1. Download and install the latest package of reportlab 2. Go to demos -> odyssey -> dodyssey 3. In the text file odyssey.txt that needs to be converted to pdf inject <img src="http://127.0.0.1:5000" valign="top"/> 4. Create a nc listener nc -lp 5000 5. Run python3 dodyssey.py 6. You will get a hit on your nc showing we have successfully proceded to send a server side request 7. dodyssey.py will show error since there is no img file on the url, but we are able to do SSRF

Severity

6.5 (Medium)


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

CWE

Server-side Request Forgery (SSRF)

Assigner

snyk mitre root

References

5 references

URL	Tags
https://snyk.io/vuln/SNYK-PYTHON-REPORTLAB-1022145
https://www.reportlab.com/docs/reportlab-userguide.pdf
https://lists.fedoraproject.org/archives/list/pac…	vendor-advisory
https://lists.fedoraproject.org/archives/list/pac…	vendor-advisory
https://lists.debian.org/debian-lts-announce/2023…	mailing-list

Impacted products

1 product

Vendor	Product	Version
n/a	reportlab	Affected: 0 , < unspecified (custom)

Date Public

2021-02-18 00:00

Credits

Karan Bamal

Show details on NVD website

JSON

To clipboard

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      ],
      "title": "Server-side Request Forgery (SSRF)"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "bae035ff-b466-4ff4-94d0-fc9efd9e1730",
    "assignerShortName": "snyk",
    "cveId": "CVE-2020-28463",
    "datePublished": "2021-02-18T16:00:21.220Z",
    "dateReserved": "2020-11-12T00:00:00.000Z",
    "dateUpdated": "2024-09-17T01:27:03.761Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

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.

Action not permitted

SUSE-SU-2021:3209-1

CVE-2020-28463 (GCVE-0-2020-28463)

Tags

Sightings

Nomenclature

Related by attack behaviour