CVE-2026-86000 (GCVE-0-2026-86000)
Vulnerability from cvelistv5 – Published: 2026-09-17 15:21 – Updated: 2026-09-17 15:45
VLAI
EPSS
VEX
Title
Soup Sieve: Polynomial-time ReDoS (O(n²)) in the `IDENTIFIER` / `VALUE` selector sub-patterns
Summary
Soup Sieve is a CSS selector library designed to be used with Beautiful Soup 4. Prior to 2.9, the selector parser in src/soupsieve/css_parser.py defines IDENTIFIER with adjacent quantified groups over overlapping character classes, and VALUE embeds IDENTIFIER for attribute selectors. When an attacker-controlled selector contains a long identifier or unquoted attribute-value run followed by input that makes the overall match fail, the regular expression engine explores quadratically many splits between the overlapping groups. User-controlled selectors can reach this path through soupsieve.compile(), soupsieve.select(), or BeautifulSoup.select(), while applications using only hard-coded selectors are unaffected. The resulting CPU consumption can hold the Python GIL, exhaust application workers, and stall a service; successful plain identifier matches are linear, and the issue does not cause memory corruption or code execution. The issue is fixed in version 2.9.
Severity
5.3 (Medium)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-17 15:45 UTC
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/facelessuser/soupsieve/securit… | x_refsource_CONFIRM |
| https://github.com/facelessuser/soupsieve/commit/… | x_refsource_MISC |
| https://github.com/facelessuser/soupsieve/release… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| facelessuser | soupsieve |
Affected:
< 2.9
|
guessed |
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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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