GSD-2020-7212
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
The _encode_invalid_chars function in util/url.py in the urllib3 library 1.25.2 through 1.25.7 for Python allows a denial of service (CPU consumption) because of an inefficient algorithm. The percent_encodings array contains all matches of percent encodings. It is not deduplicated. For a URL of length N, the size of percent_encodings may be up to O(N). The next step (normalize existing percent-encoded bytes) also takes up to O(N) for each step, so the total time is O(N^2). If percent_encodings were deduplicated, the time to compute _encode_invalid_chars would be O(kN), where k is at most 484 ((10+6*2)^2).
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-7212",
"description": "The _encode_invalid_chars function in util/url.py in the urllib3 library 1.25.2 through 1.25.7 for Python allows a denial of service (CPU consumption) because of an inefficient algorithm. The percent_encodings array contains all matches of percent encodings. It is not deduplicated. For a URL of length N, the size of percent_encodings may be up to O(N). The next step (normalize existing percent-encoded bytes) also takes up to O(N) for each step, so the total time is O(N^2). If percent_encodings were deduplicated, the time to compute _encode_invalid_chars would be O(kN), where k is at most 484 ((10+6*2)^2).",
"id": "GSD-2020-7212",
"references": [
"https://www.suse.com/security/cve/CVE-2020-7212.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "The _encode_invalid_chars function in util/url.py in the urllib3 library 1.25.2 through 1.25.7 for Python allows a denial of service (CPU consumption) because of an inefficient algorithm. The percent_encodings array contains all matches of percent encodings. It is not deduplicated. For a URL of length N, the size of percent_encodings may be up to O(N). The next step (normalize existing percent-encoded bytes) also takes up to O(N) for each step, so the total time is O(N^2). If percent_encodings were deduplicated, the time to compute _encode_invalid_chars would be O(kN), where k is at most 484 ((10+6*2)^2).",
"id": "GSD-2020-7212",
"modified": "2023-12-13T01:21:51.626955Z",
"schema_version": "1.4.0"
}
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"advisories": [
{
"affected_range": "\u003e=1.25.2,\u003c=1.25.7",
"affected_versions": "All versions starting from 1.25.2 up to 1.25.7",
"cvss_v2": "AV:N/AC:L/Au:N/C:N/I:N/A:C",
"cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"cwe_ids": [
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"CWE-400",
"CWE-937"
],
"date": "2020-03-09",
"description": "The `_encode_invalid_chars` function in `util/url.py` in the urllib3 library for Python allows a denial of service (CPU consumption) because of an inefficient algorithm. The `percent_encodings` array contains all matches of percent encodings. It is not deduplicated. For a URL of length N, the size of `percent_encodings` may be up to O(N). The next step (normalize existing percent-encoded bytes) also takes up to O(N) for each step, so the total time is O(N^2). If `percent_encodings` were deduplicated, the time to compute `_encode_invalid_chars` would be O(kN), where k is at most ((10+6*2)^2).",
"fixed_versions": [
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"identifier": "CVE-2020-7212",
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"package_slug": "pypi/urllib3",
"pubdate": "2020-03-06",
"solution": "Upgrade to version 1.25.8 or above.",
"title": "Uncontrolled Resource Consumption",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2020-7212",
"https://pypi.org/project/urllib3/1.25.8/"
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"uuid": "ac9c445d-7268-4ed4-9415-b5e24fabd4e9"
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "COMPLETE",
"baseScore": 7.8,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:C",
"version": "2.0"
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"impactScore": 3.6
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},
"lastModifiedDate": "2020-03-09T16:55Z",
"publishedDate": "2020-03-06T20:15Z"
}
}
}
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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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