Vulnerability Database

327,921

Total vulnerabilities in the database

Vulnerabilities for products matching "Django"

Found 4 matching products. Filters apply to all results.

You can search for specific versions with /product/Django/1.2.3

djangoproject / django

136 vulnerabilities found
Title Severity Exploit Date Affected Version
Medium January 5, 2022 1/5/22
>= 2.2 < 2.2.26
>= 3.2 < 3.2.11
>= 4.0 < 4.0.1
High December 8, 2021 12/8/21
>= 2.2 < 2.2.25
>= 3.1 < 3.1.14
>= 3.2 < 3.2.10
Critical July 2, 2021 7/2/21
>= 3.1 < 3.1.13
>= 3.2 < 3.2.5
Low June 8, 2021 6/8/21
>= 3.2.0 < 3.2.4
>= 3.0.0 < 3.1.12
< 2.2.24
High June 8, 2021 6/8/21
>= 3.2 < 3.2.4
>= 3.0 < 3.1.12
>= 2.2 < 2.2.24
Medium May 6, 2021 5/6/21
>= 2.2 < 2.2.22
>= 3.1 < 3.1.10
>= 3.2 < 3.2.2
High May 5, 2021 5/5/21
>= 3.2 < 3.2.1
>= 3.1 < 3.1.9
>= 2.2 < 2.2.21
Medium April 6, 2021 4/6/21
>= 3.1 < 3.1.8
>= 3.0 < 3.0.14
>= 2.2 < 2.2.20
Medium February 15, 2021 2/15/21
>= 3.1 < 3.1.7
>= 3.0 < 3.0.13
>= 2.2 < 2.2.19
Medium February 2, 2021 2/2/21
>= 3.1 < 3.1.6
>= 3.0 < 3.0.12
>= 2.2 < 2.2.18
High September 1, 2020 9/1/20
>= 3.1 < 3.1.1
>= 3.0 < 3.0.10
>= 2.2 < 2.2.16
High September 1, 2020 9/1/20
>= 3.1 < 3.1.1
>= 3.0 < 3.0.10
>= 2.2 < 2.2.16
Medium June 3, 2020 6/3/20
>= 2.2 < 2.2.13
>= 3.0 < 3.0.7
Medium June 3, 2020 6/3/20
>= 2.2 < 2.2.13
>= 3.0 < 3.0.7
High March 5, 2020 3/5/20
>= 3.0 < 3.0.4
>= 2.2 < 2.2.11
>= 1.11 < 1.11.29
Critical February 3, 2020 2/3/20
>= 3.0 < 3.0.3
>= 2.2 < 2.2.10
>= 1.11 < 1.11.28
Critical December 18, 2019 12/18/19
>= 2.2 < 2.2.9
== 3.0
< 1.11.27
Medium December 2, 2019 12/2/19
>= 2.1 < 2.1.15
>= 2.2 < 2.2.8
High August 9, 2019 8/9/19
>= 2.2 < 2.2.4
>= 2.1 < 2.1.11
>= 1.11 < 1.11.23
Medium August 2, 2019 8/2/19
>= 2.2 < 2.2.4
>= 2.1 < 2.1.11
>= 1.11 < 1.11.23
High August 2, 2019 8/2/19
>= 2.2 < 2.2.4
>= 2.1 < 2.1.11
>= 1.11 < 1.11.23
Medium August 2, 2019 8/2/19
>= 2.2 < 2.2.4
>= 2.1 < 2.1.11
>= 1.11 < 1.11.23
Medium July 1, 2019 7/1/19
>= 1.11 < 1.11.22
>= 2.1 < 2.1.10
>= 2.2 < 2.2.3
Low June 3, 2019 6/3/19
>= 2.2 < 2.2.2
>= 2.1 < 2.1.9
>= 1.11 < 1.11.21
Medium February 11, 2019 2/11/19
>= 1.11.0 < 1.11.19
>= 2.0.0 < 2.0.11
>= 2.1.0 < 2.1.6
Low January 9, 2019 1/9/19
>= 1.11 < 1.11.18
>= 2.0 < 2.0.10
>= 2.1 < 2.1.5
Low October 2, 2018 10/2/18
>= 2.1 < 2.1.2
Medium August 3, 2018 8/3/18
>= 1.11 < 1.11.15
>= 2.0 < 2.0.8
Medium March 9, 2018 3/9/18
>= 1.8 < 1.8.19
>= 1.11 < 1.11.11
>= 2.0 < 2.0.3
Medium March 9, 2018 3/9/18
>= 1.8 < 1.8.19
>= 1.11 < 1.11.11
>= 2.0 < 2.0.3
Medium February 5, 2018 2/5/18
== 2.0
== 2.0.1
== 1.11.8
== 1.11.9
Low September 7, 2017 9/7/17
== 1.10.0
== 1.11.2
== 1.11.1
== 1.10.3
== 1.11.4
== 1.10.1
== 1.10.5
== 1.10.7
== 1.10.2
== 1.10.6
== 1.10.4
== 1.11.0
== 1.11.3
Medium April 4, 2017 4/4/17
== 1.10.0
== 1.9-a1
== 1.8.0-a1
== 1.8.15
== 1.8.2
== 1.9.6
== 1.9-rc2
== 1.10.3
== 1.8.14
== 1.9.9
== 1.8.1
== 1.8.0-b1
== 1.10.1
== 1.8.7
== 1.8.9
== 1.10.5
== 1.9.5
== 1.8.11
== 1.10.0-a1
== 1.9.12
== 1.8.3
== 1.8.12
== 1.8.4
== 1.9.11
== 1.10.2
== 1.8.0
== 1.8.16
== 1.9.3
== 1.9.4
== 1.8.6
== 1.8.0-c1
== 1.10.6
== 1.8.13
== 1.10.0-b1
== 1.10.0-rc1
== 1.9.7
== 1.8.17
== 1.8.8
== 1.8.5
== 1.9.1
== 1.9
== 1.10.4
== 1.9.8
== 1.8.0-b2
== 1.9.2
== 1.9-b1
== 1.9.10
== 1.9-rc1
== 1.8.10
Medium April 4, 2017 4/4/17
== 1.10.0
== 1.9-a1
== 1.8.0-a1
== 1.8.15
== 1.8.2
== 1.9.6
== 1.9-rc2
== 1.10.3
== 1.8.14
== 1.9.9
== 1.8.1
== 1.8.0-b1
== 1.10.1
== 1.8.7
== 1.8.9
== 1.10.5
== 1.9.5
== 1.8.11
== 1.10.0-a1
== 1.9.12
== 1.8.3
== 1.8.12
== 1.8.4
== 1.9.11
== 1.10.2
== 1.8.0
== 1.8.16
== 1.9.3
== 1.9.4
== 1.8.6
== 1.8.0-c1
== 1.10.6
== 1.8.13
== 1.10.0-b1
== 1.10.0-rc1
== 1.9.7
== 1.8.17
== 1.8.8
== 1.8.5
== 1.9.1
== 1.9
== 1.10.4
== 1.9.8
== 1.8.0-b2
== 1.9.2
== 1.9-b1
== 1.9.10
== 1.9-rc1
== 1.8.10
Medium December 9, 2016 12/9/16
== 1.10
== 1.10.1
== 1.10.2
== 1.8
== 1.8.1
== 1.8.10
== 1.8.11
== 1.8.12
== 1.8.13
== 1.8.14
== 1.8.15
== 1.8.2
== 1.8.3
== 1.8.4
== 1.8.5
== 1.8.6
== 1.8.7
== 1.8.8
== 1.8.9
== 1.9
== 1.9.1
== 1.9.10
== 1.9.2
== 1.9.3
== 1.9.4
== 1.9.5
== 1.9.6
== 1.9.7
== 1.9.8
== 1.9.9
High December 9, 2016 12/9/16
== 1.10
== 1.10.1
== 1.10.2
== 1.8
== 1.8.1
== 1.8.10
== 1.8.11
== 1.8.12
== 1.8.13
== 1.8.14
== 1.8.15
== 1.8.2
== 1.8.3
== 1.8.4
== 1.8.5
== 1.8.6
== 1.8.7
== 1.8.8
== 1.8.9
== 1.9
== 1.9.1
== 1.9.10
== 1.9.2
== 1.9.3
== 1.9.4
== 1.9.5
== 1.9.6
== 1.9.7
== 1.9.8
== 1.9.9
Medium October 3, 2016 10/3/16
== 1.9.6
== 1.9.9
<= 1.8.14
== 1.9.0
== 1.9.5
== 1.9.3
== 1.9.4
== 1.9.7
== 1.9.1
== 1.9.8
== 1.9.2
Low August 5, 2016 8/5/16
== 1.10-alpha1
== 1.9.6
== 1.9.0-rc1
== 1.9.5
<= 1.8.13
== 1.9.3
== 1.9.4
== 1.9.7
== 1.9.1
== 1.9
== 1.10-beta1
== 1.9.2
Low April 8, 2016 4/8/16
== 1.8.9
== 1.9.1
== 1.9
== 1.9.2
Low April 8, 2016 4/8/16
== 1.8.9
== 1.9.1
== 1.9
== 1.9.2
Medium February 8, 2016 2/8/16
== 1.9.1
== 1.9
Medium December 7, 2015 12/7/15
== 1.8.2
== 1.9.0-rc1
== 1.8.1
== 1.8.3
== 1.8.4
== 1.8.0
<= 1.7.10
== 1.8.6
== 1.8.5
Medium August 24, 2015 8/24/15
== 1.4.12
== 1.7.5
== 1.7.9
== 1.7.3
== 1.4.9
== 1.8.2
== 1.7-rc2
== 1.7-beta1
== 1.7-beta3
== 1.7.7
== 1.8.1
== 1.8-beta1
== 1.4.10
== 1.8.3
== 1.4.6
== 1.4.20
== 1.7.2
== 1.7.4
== 1.4.4
== 1.7.8
== 1.4.13
== 1.4.5
== 1.4.2
== 1.4.11
== 1.7.6
== 1.8.0
== 1.7-rc3
== 1.4.7
== 1.4.8
== 1.7-rc1
== 1.4
== 1.4.19
== 1.4.21
== 1.4.1
== 1.7.1
== 1.7-beta2
== 1.4.14
== 1.4.17
== 1.7-beta4
Medium August 24, 2015 8/24/15
== 1.4.12
== 1.7.5
== 1.7.9
== 1.7.3
== 1.4.9
== 1.8.2
== 1.7-rc2
== 1.7-beta1
== 1.7-beta3
== 1.7.7
== 1.8.1
== 1.8-beta1
== 1.4.10
== 1.8.3
== 1.4.6
== 1.4.20
== 1.7.2
== 1.7.4
== 1.4.4
== 1.7.8
== 1.4.13
== 1.4.5
== 1.4.2
== 1.4.11
== 1.7.6
== 1.8.0
== 1.7-rc3
== 1.4.7
== 1.4.8
== 1.7-rc1
== 1.4
== 1.4.19
== 1.4.21
== 1.4.1
== 1.7.1
== 1.7-beta2
== 1.4.14
== 1.4.17
== 1.7-beta4
High July 14, 2015 7/14/15
== 1.8.2
== 1.8.1
== 1.8.0
Low July 14, 2015 7/14/15
<= 1.4.20
== 1.5
== 1.5-alpha
== 1.5-beta
== 1.5.1
== 1.5.10
== 1.5.11
== 1.5.12
== 1.5.2
== 1.5.3
== 1.5.4
== 1.5.5
== 1.5.6
== 1.5.7
== 1.5.8
== 1.5.9
== 1.6
== 1.6-beta1
== 1.6-beta2
== 1.6-beta3
== 1.6-beta4
== 1.6.1
== 1.6.10
== 1.6.2
== 1.6.3
== 1.6.4
== 1.6.5
== 1.6.6
== 1.6.7
== 1.6.8
== 1.6.9
== 1.7-beta1
== 1.7-beta2
== 1.7-beta3
== 1.7-beta4
== 1.7-rc1
== 1.7-rc2
== 1.7-rc3
== 1.7.1
== 1.7.2
== 1.7.3
== 1.7.4
== 1.7.5
== 1.7.6
== 1.7.7
== 1.7.8
== 1.7.9
== 1.8-beta1
== 1.8.0
== 1.8.1
== 1.8.2
High July 14, 2015 7/14/15
== 1.4.20
== 1.5
== 1.5-alpha
== 1.5-beta
== 1.5.1
== 1.5.10
== 1.5.11
== 1.5.12
== 1.5.2
== 1.5.3
== 1.5.4
== 1.5.5
== 1.5.6
== 1.5.7
== 1.5.8
== 1.5.9
== 1.6
== 1.6-beta1
== 1.6-beta2
== 1.6-beta3
== 1.6-beta4
== 1.6.1
== 1.6.10
== 1.6.2
== 1.6.3
== 1.6.4
== 1.6.5
== 1.6.6
== 1.6.7
== 1.6.8
== 1.6.9
== 1.7-beta1
== 1.7-beta2
== 1.7-beta3
== 1.7-beta4
== 1.7-rc1
== 1.7-rc2
== 1.7-rc3
== 1.7.1
== 1.7.2
== 1.7.3
== 1.7.4
== 1.7.5
== 1.7.6
== 1.7.7
== 1.7.8
== 1.7.9
== 1.8.0
== 1.8.1
== 1.8.2
Medium June 2, 2015 6/2/15
== 1.8.1
== 1.8.0
Low March 25, 2015 3/25/15
<= 1.4.19
== 1.5
== 1.5-alpha
== 1.5-beta
== 1.5.1
== 1.5.10
== 1.5.11
== 1.5.12
== 1.5.2
== 1.5.3
== 1.5.4
== 1.5.5
== 1.5.6
== 1.5.7
== 1.5.8
== 1.5.9
== 1.6
== 1.6-beta1
== 1.6-beta2
== 1.6-beta3
== 1.6-beta4
== 1.6.1
== 1.6.10
== 1.6.2
== 1.6.3
== 1.6.4
== 1.6.5
== 1.6.6
== 1.6.7
== 1.6.8
== 1.6.9
== 1.7-beta1
== 1.7-beta2
== 1.7-beta3
== 1.7-beta4
== 1.7-rc1
== 1.7-rc2
== 1.7-rc3
== 1.7.1
== 1.7.2
== 1.7.3
== 1.7.4
== 1.7.5
== 1.7.6
== 1.8.0
Medium March 25, 2015 3/25/15
== 1.6
== 1.6-beta1
== 1.6-beta2
== 1.6-beta3
== 1.6-beta4
== 1.6.1
== 1.6.10
== 1.6.2
== 1.6.3
== 1.6.4
== 1.6.5
== 1.6.6
== 1.6.7
== 1.6.8
== 1.6.9
== 1.7-beta1
== 1.7-beta2
== 1.7-beta3
== 1.7-beta4
== 1.7-rc1
== 1.7-rc2
== 1.7-rc3
== 1.7.1
== 1.7.2
== 1.7.3
== 1.7.4
== 1.7.5
== 1.7.6
== 1.8.0
Python icon

Django

88 vulnerabilities found
Title Severity Exploit Date Affected Version
High May 5, 2021 5/5/21
>= 2.2.0 < 2.2.21
>= 3.0.0 < 3.1.9
>= 3.2.0 < 3.2.1
Medium April 6, 2021 4/6/21
>= 2.2 < 2.2.20
>= 3.0 < 3.0.14
>= 3.1 < 3.1.8
Medium February 2, 2021 2/2/21
>= 2.2 < 2.2.18
>= 3.1 < 3.1.6
>= 3.0 < 3.0.12
High September 1, 2020 9/1/20
>= 2.2 < 2.2.16
>= 3.0 < 3.0.10
>= 3.1 < 3.1.1
High September 1, 2020 9/1/20
>= 2.2 < 2.2.16
>= 3.0 < 3.0.10
>= 3.1 < 3.1.1
Medium June 3, 2020 6/3/20
>= 2.0.0 < 2.2.13
>= 3.0.0 < 3.0.7
Medium June 3, 2020 6/3/20
>= 2.0.0 < 2.2.13
>= 3.0.0 < 3.0.7
High March 5, 2020 3/5/20
< 1.11.29
>= 2.0.0 < 2.2.11
>= 3.0.0 < 3.0.4
Critical February 3, 2020 2/3/20
< 1.11.28
>= 2.0.0 < 2.2.10
>= 3.0.0 < 3.0.3
Critical December 18, 2019 12/18/19
< 1.11.27
>= 2.0.0 < 2.2.9
>= 3.0.0 < 3.0.1
Medium December 2, 2019 12/2/19
>= 2.1.0 < 2.1.15
>= 2.2.0 < 2.2.8
High August 9, 2019 8/9/19
>= 1.11.0 < 1.11.23
>= 2.1.0 < 2.1.11
>= 2.2.0 < 2.2.4
Medium August 2, 2019 8/2/19
>= 1.11.0 < 1.11.23
>= 2.1.0 < 2.1.11
>= 2.2.0 < 2.2.4
High August 2, 2019 8/2/19
>= 1.11.0 < 1.11.23
>= 2.1.0 < 2.1.11
>= 2.2.0 < 2.2.4
Medium August 2, 2019 8/2/19
>= 1.11.0 < 1.11.23
>= 2.1.0 < 2.1.11
>= 2.2.0 < 2.2.4
Medium July 1, 2019 7/1/19
>= 1.11.0 < 1.11.22
>= 2.1.0 < 2.1.10
>= 2.2.0 < 2.2.3
Low June 3, 2019 6/3/19
>= 1.11.0 < 1.11.21
>= 2.1.0 < 2.1.9
>= 2.2.0 < 2.2.2
Medium February 11, 2019 2/11/19
< 1.11.19
>= 2.0.0 < 2.0.11
>= 2.1.0 < 2.1.6
Low January 9, 2019 1/9/19
< 1.11.18
>= 2.0.0 < 2.0.10
>= 2.1.0 < 2.1.5
Low October 2, 2018 10/2/18
>= 2.1 < 2.1.2
Medium August 3, 2018 8/3/18
>= 1.11.0 < 1.11.15
>= 2.0 < 2.0.8
Medium March 9, 2018 3/9/18
>= 2.0 < 2.0.3
>= 1.11 < 1.11.11
>= 1.8 < 1.8.19
Medium March 9, 2018 3/9/18
>= 2.0 < 2.0.3
>= 1.11 < 1.11.11
>= 1.8 < 1.8.19
Medium February 5, 2018 2/5/18
>= 2.0.0 < 2.0.2
>= 1.11.8 < 1.11.10
Low September 7, 2017 9/7/17
>= 1.10.0 < 1.10.8
>= 1.11.0 < 1.11.5
Medium April 4, 2017 4/4/17
>= 1.10 < 1.10.7
>= 1.9 < 1.9.13
>= 1.8 < 1.8.18
Medium April 4, 2017 4/4/17
>= 1.10 < 1.10.7
>= 1.9 < 1.9.13
>= 1.8 < 1.8.18
High July 14, 2015 7/14/15
< 1.4.21
>= 1.5.0 < 1.7.9
>= 1.8.0 < 1.8.3
Medium April 23, 2014 4/23/14
< 1.4.11
>= 1.5.0 < 1.5.6
>= 1.6.0 < 1.6.3
Medium October 19, 2011 10/19/11
>= 1.2.0 < 1.2.7
>= 1.3.0 < 1.3.1
Medium October 19, 2011 10/19/11
< 1.2.7
>= 1.3.0 < 1.3.1
Medium October 19, 2011 10/19/11
>= 1.3.0 < 1.3.1
>= 1.2.0 < 1.2.7
Medium February 14, 2011 2/14/11
>= 1.1.0 < 1.1.4
>= 1.2.0 < 1.2.5
High February 14, 2011 2/14/11
>= 1.1.0 < 1.1.4
>= 1.2.0 < 1.2.5
Low February 14, 2011 2/14/11
>= 1.1.0 < 1.1.4
>= 1.2.0 < 1.2.5
Medium January 10, 2011 1/10/11
< 1.1.3
>= 1.2.0 < 1.2.4
Low January 10, 2011 1/10/11
< 1.1.3
>= 1.2.0 < 1.2.4
Low September 14, 2010 9/14/10
>= 1.2.0 < 1.2.2

Showing vulnerabilities for 4 products matching "Django". Each product has independent pagination.

Frequently Asked Questions

A security vulnerability is a weakness in software, hardware, or configuration that can be exploited to compromise confidentiality, integrity, or availability. Many vulnerabilities are tracked as CVEs (Common Vulnerabilities and Exposures), which provide a standardized identifier so teams can coordinate patching, mitigation, and risk assessment across tools and vendors.

CVSS (Common Vulnerability Scoring System) estimates technical severity, but it doesn't automatically equal business risk. Prioritize using context like internet exposure, affected asset criticality, known exploitation (proof-of-concept or in-the-wild), and whether compensating controls exist. A "Medium" CVSS on an exposed, production system can be more urgent than a "Critical" on an isolated, non-production host.

A vulnerability is the underlying weakness. An exploit is the method or code used to take advantage of it. A zero-day is a vulnerability that is unknown to the vendor or has no publicly available fix when attackers begin using it. In practice, risk increases sharply when exploitation becomes reliable or widespread.

Recurring findings usually come from incomplete Asset Discovery, inconsistent patch management, inherited images, and configuration drift. In modern environments, you also need to watch the software supply chain: dependencies, containers, build pipelines, and third-party services can reintroduce the same weakness even after you patch a single host. Unknown or unmanaged assets (often called Shadow IT) are a common reason the same issues resurface.

Use a simple, repeatable triage model: focus first on externally exposed assets, high-value systems (identity, VPN, email, production), vulnerabilities with known exploits, and issues that enable remote code execution or privilege escalation. Then enforce patch SLAs and track progress using consistent metrics so remediation is steady, not reactive.

SynScan combines attack surface monitoring and continuous security auditing to keep your inventory current, flag high-impact vulnerabilities early, and help you turn raw findings into a practical remediation plan.