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Vulnerability from cleanstart
Published
2026-07-30 07:10
Modified
2026-07-30 09:36
Summary
Security fixes in tensorflow-gpu-jupyter 2.20.0-r1
Details
Package tensorflow-gpu-jupyter version 2.20.0-r1 fixes 4 vulnerabilities: CVE-2026-25645, CVE-2026-59884, CVE-2026-59885, CVE-2026-59886
References
| URL | Type | |
|---|---|---|
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"id": "CLEANSTART-2026-MR10520",
"modified": "2026-07-30T09:36:28Z",
"published": "2026-07-30T07:10:53Z",
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CVE-2026-25645 (GCVE-0-2026-25645)
Vulnerability from cvelistv5 – Published: 2026-03-25 17:02 – Updated: 2026-03-25 22:48
VLAI
EPSS
VEX
Title
Requests has Insecure Temp File Reuse in its extract_zipped_paths() utility function
Summary
Requests is a HTTP library. Prior to version 2.33.0, the `requests.utils.extract_zipped_paths()` utility function uses a predictable filename when extracting files from zip archives into the system temporary directory. If the target file already exists, it is reused without validation. A local attacker with write access to the temp directory could pre-create a malicious file that would be loaded in place of the legitimate one. Standard usage of the Requests library is not affected by this vulnerability. Only applications that call `extract_zipped_paths()` directly are impacted. Starting in version 2.33.0, the library extracts files to a non-deterministic location. If developers are unable to upgrade, they can set `TMPDIR` in their environment to a directory with restricted write access.
Severity
4.4 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-377 - Insecure Temporary File
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/psf/requests/security/advisori… | x_refsource_CONFIRM |
| https://github.com/psf/requests/commit/66d21cb07b… | x_refsource_MISC |
| https://github.com/psf/requests/releases/tag/v2.33.0 | x_refsource_MISC |
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CVE-2026-59884 (GCVE-0-2026-59884)
Vulnerability from cvelistv5 – Published: 2026-07-14 16:41 – Updated: 2026-07-15 13:50
VLAI
EPSS
VEX
Title
pyasn1 BER/CER/DER decoder denial of service via unbounded long-form tag IDs
Summary
pyasn1 is a generic ASN.1 library for Python. Prior to 0.6.4, the BER decoder shared by the CER and DER codecs parses long-form tags by accumulating continuation octets without an upper bound on the tag ID size, allowing a crafted input to force construction of an arbitrarily large integer with CPU cost growing quadratically and to trigger unhandled ValueError exceptions in Python 3.11+ error formatting paths. Any application decoding untrusted BER, CER, or DER input is affected. This issue is fixed in version 0.6.4.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/pyasn1/pyasn1/security/advisor… | x_refsource_CONFIRM |
| https://github.com/pyasn1/pyasn1/commit/628e36ecb… | x_refsource_MISC |
| https://github.com/pyasn1/pyasn1/releases/tag/v0.6.4 | x_refsource_MISC |
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CVE-2026-59885 (GCVE-0-2026-59885)
Vulnerability from cvelistv5 – Published: 2026-07-14 16:40 – Updated: 2026-07-14 17:32
VLAI
EPSS
VEX
Title
pyasn1: Quadratic complexity in OBJECT IDENTIFIER and RELATIVE-OID processing allows denial of service
Summary
pyasn1 is a generic ASN.1 library for Python. Prior to 0.6.4, the BER, CER, and DER decoders process OBJECT IDENTIFIER and RELATIVE-OID values in quadratic time relative to the number of arcs, so a small crafted payload containing an OID with many arcs consumes excessive CPU per decode() call and can deny service to applications that decode untrusted ASN.1 data. The corresponding encoders have the same quadratic behavior when an application re-encodes previously decoded attacker-supplied values. This issue is fixed in version 0.6.4.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/pyasn1/pyasn1/security/advisor… | x_refsource_CONFIRM |
| https://github.com/pyasn1/pyasn1/commit/45bdb19eb… | x_refsource_MISC |
| https://github.com/pyasn1/pyasn1/releases/tag/v0.6.4 | x_refsource_MISC |
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CVE-2026-59886 (GCVE-0-2026-59886)
Vulnerability from cvelistv5 – Published: 2026-07-14 16:38 – Updated: 2026-07-15 15:44
VLAI
EPSS
VEX
Title
pyasn1: Uncontrolled resource consumption when converting decoded REAL values
Summary
pyasn1 is a generic ASN.1 library for Python. Prior to 0.6.4, the univ.Real type converted its mantissa, base, and exponent value to a Python float using exact big-integer exponentiation. A BER, CER, or DER encoded REAL value only a few bytes long can carry a very large exponent, causing float conversion through prettyPrint(), str(), comparison, arithmetic, int(), or an explicit float() call to consume excessive CPU and memory and hang applications that decode untrusted ASN.1 data and then print, log, or compare decoded objects. This issue is fixed in version 0.6.4.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/pyasn1/pyasn1/security/advisor… | x_refsource_CONFIRM |
| https://github.com/pyasn1/pyasn1/commit/e60c691cb… | x_refsource_MISC |
| https://github.com/pyasn1/pyasn1/releases/tag/v0.6.4 | x_refsource_MISC |
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},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2026-59886",
"datePublished": "2026-07-14T16:38:29.691Z",
"dateReserved": "2026-07-07T16:40:07.983Z",
"dateUpdated": "2026-07-15T15:44:02.717Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
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Trend slope:
-
(linear fit over daily sighting counts)
Show additional events:
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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.
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