Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-1285 is a high-severity Inefficient Algorithmic Complexity (CWE-407) vulnerability in Djangoproject Django. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked in the top 41% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-1285 is a denial-of-service vulnerability in Django's text truncation utilities, specifically affecting the `django.utils.text.Truncator.chars()` and `Truncator.words()` methods when used with `html=True`, as well as the `truncatechars_html` and `truncatewords_html` template filters. The issue impacts Django versions 6.0 before 6.0.2, 5.2 before 5.2.11, and 4.2 before 4.2.28. Earlier unsupported series, such as 5.0.x, 4.1.x, and 3.2.x, were not evaluated but may also be affected.
A remote unauthenticated attacker can exploit this vulnerability over the network with low complexity by supplying crafted inputs containing a large number of unmatched HTML end tags to the affected functions or filters. Successful exploitation leads to resource exhaustion and potential denial-of-service, with no impact on confidentiality or integrity. The vulnerability carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) and is associated with CWE-407.
Django has issued security releases addressing this issue, recommending upgrades to version 6.0.2, 5.2.11, or 4.2.28 or later for supported series. Mitigation details and patches are documented in the official advisories at https://docs.djangoproject.com/en/dev/releases/security/, the django-announce group at https://groups.google.com/g/django-announce, and the security release weblog at https://www.djangoproject.com/weblog/2026/feb/03/security-releases/. The issue was reported by Seokchan Yoon.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-5251
Vulnerability Data
An issue was discovered in 6.0 before 6.0.2, 5.2 before 5.2.11, and 4.2 before 4.2.28. `django.utils.text.Truncator.chars()` and `Truncator.words()` methods (with `html=True`) and the `truncatechars_html` and `truncatewords_html` template filters allow a remote attacker to cause a potential denial-of-service via crafted inputs…
more
containing a large number of unmatched HTML end tags. Earlier, unsupported Django series (such as 5.0.x, 4.1.x, and 3.2.x) were not evaluated and may also be affected. Django would like to thank Seokchan Yoon for reporting this issue.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.9
Mitigating Controls (NIST 800-53 r5) AI
Denial-of-service protection directly reduces the impact of resource exhaustion triggered by worst-case algorithmic inputs.
Resource availability allocation limits blast radius when an inefficient algorithm is forced into its worst case.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices (code review, complexity analysis, safe algorithm selection) prevent introduction of exploitable worst-case behavior.
Runtime monitoring of software and resources can detect the performance impact of triggered worst-case complexity.
Identifying and recording algorithmic-complexity vulnerabilities directly addresses the root cause before exploitation.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing can uncover performance issues stemming from algorithmic complexity.
Redundancy of processing facilities can absorb resource exhaustion from inefficient algorithms.
Monitoring activities can identify anomalous resource consumption indicative of algorithmic complexity attacks.
Secure development life cycle includes design reviews that can catch inefficient algorithms before deployment.
Secure system architecture principles encourage selection of algorithms with acceptable worst-case complexity.
Secure coding practices can include guidelines to avoid or mitigate inefficient algorithms.