RHSA-2026:57010
Vulnerability from csaf_redhat - Published: 2026-08-19 14:17 - Updated: 2026-08-22 00:14Summary
Red Hat Security Advisory: Red Hat Hardened Images RPMs bug fix and enhancement update
Severity
Low
Notes
Topic: An update for Red Hat Hardened Images RPMs is now available.
Details: This update includes the following RPMs:
python3.10:
* python3.10-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-debug-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-devel-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-idle-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-libs-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-test-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-tkinter-3.10.21-1.hum1 (aarch64, x86_64)
* python3.10-3.10.21-1.hum1.src (src)
Terms of Use: This content is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). If you distribute this content, or a modified version of it, you must provide attribution to Red Hat Inc. and provide a link to the original.
A flaw was found in the `csv.Sniffer.sniff()` function within Python. An attacker can exploit this by providing specially crafted CSV samples, which can trigger super-linear regular-expression processing during dialect sniffing. This excessive processing consumes significant CPU resources, potentially leading to a Denial of Service (DoS) for applications that process unbounded input using this function.
CWE-1333 - Inefficient Regular Expression ComplexityAffected products
Fixed
3 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: Red Hat Hardened Images:python3-10-main@aarch64 | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Hardened Images:python3-10-main@src | — |
Vendor Fix
fix
Workaround
|
|
| Unresolved product id: Red Hat Hardened Images:python3-10-main@x86_64 | — |
Vendor Fix
fix
Workaround
|
Threats
Impact
Low
References
12 references
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"text": "This update includes the following RPMs:\n\npython3.10:\n * python3.10-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-debug-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-devel-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-idle-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-libs-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-test-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-tkinter-3.10.21-1.hum1 (aarch64, x86_64)\n * python3.10-3.10.21-1.hum1.src (src)",
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"vulnerabilities": [
{
"cve": "CVE-2026-18503",
"cwe": {
"id": "CWE-1333",
"name": "Inefficient Regular Expression Complexity"
},
"discovery_date": "2026-08-10T14:11:09.811226+00:00",
"ids": [
{
"system_name": "Red Hat Bugzilla ID",
"text": "2513510"
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"notes": [
{
"category": "description",
"text": "A flaw was found in the `csv.Sniffer.sniff()` function within Python. An attacker can exploit this by providing specially crafted CSV samples, which can trigger super-linear regular-expression processing during dialect sniffing. This excessive processing consumes significant CPU resources, potentially leading to a Denial of Service (DoS) for applications that process unbounded input using this function.",
"title": "Vulnerability description"
},
{
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"text": "python: Python: Denial of Service via super-linear regular expression work in csv.Sniffer.sniff()",
"title": "Vulnerability summary"
},
{
"category": "other",
"text": "Red Hat has evaluated this issue and determined it has a Low security impact. The csv.Sniffer.sniff() function in Python\u0027s standard library is vulnerable to excessive CPU consumption when processing specially crafted CSV input, due to quadratic time complexity in the dialect sniffing regex. Most applications are not affected as they use csv.reader() or csv.DictReader() directly without invoking the sniffing functionality.",
"title": "Statement"
},
{
"category": "general",
"text": "The CVSS score(s) listed for this vulnerability do not reflect the associated product\u0027s status, and are included for informational purposes to better understand the severity of this vulnerability.",
"title": "CVSS score applicability"
}
],
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{
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"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-18503"
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{
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"summary": "https://github.com/python/cpython/issues/98820",
"url": "https://github.com/python/cpython/issues/98820"
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"summary": "https://github.com/python/cpython/pull/153694",
"url": "https://github.com/python/cpython/pull/153694"
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"summary": "https://mail.python.org/archives/list/security-announce@python.org/thread/KQ7NBMCPAZJHRROQXJQE4GMXGLD5KHBS/",
"url": "https://mail.python.org/archives/list/security-announce@python.org/thread/KQ7NBMCPAZJHRROQXJQE4GMXGLD5KHBS/"
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"details": "For details on how to apply this update, which includes the changes described in this advisory, refer to:\nhttps://images.redhat.com/",
"product_ids": [
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{
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"details": "Do not pass untrusted or unbounded CSV input to csv.Sniffer.sniff(). If dialect detection is needed, limit the size of the sample passed to sniff() or use a known dialect directly with csv.reader().",
"product_ids": [
"Red Hat Hardened Images:python3-10-main@aarch64",
"Red Hat Hardened Images:python3-10-main@src",
"Red Hat Hardened Images:python3-10-main@x86_64"
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"scores": [
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"attackVector": "LOCAL",
"availabilityImpact": "LOW",
"baseScore": 2.8,
"baseSeverity": "LOW",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "REQUIRED",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:R/S:U/C:N/I:N/A:L",
"version": "3.1"
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"products": [
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"threats": [
{
"category": "impact",
"details": "Low"
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],
"title": "python: Python: Denial of Service via super-linear regular expression work in csv.Sniffer.sniff()"
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]
}
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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.
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.
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