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vulnersOsv
vulnersOsv
added 2026/05/12 6:30 p.m.7 views

b2aiprep (>=0.19.0 <=3.3.3), capstone-text-mining (>=0.0.6 <=0.1.2) +10 more potentially affected by CVE-2026-31222 via snorkel (>=0.10.0 <=0.9.9)

snorkel PYPI version =0.10.0, =0.19.0, =0.0.6, =1.0.2, =0.8.0, =0.1.1, =0.1.2, =0.1.0, =0.6.1, =0.0.0, =1.3.1a1 - t2r2 =0.0.1 - ws-benchmark =1.1.2rc0 Source cves: CVE-2026-31222 Source advisory: SNYK:PYTHON-SNORKEL-16758049...

8.8CVSS5.7AI score0.00392EPSS
Exploits0
vulnersOsv
vulnersOsv
added 2026/05/12 6:30 p.m.7 views

b2aiprep (>=0.19.0 <=3.3.3), capstone-text-mining (>=0.0.6 <=0.1.2) +10 more potentially affected by CVE-2026-31223 via snorkel (>=0.10.0 <=0.9.9)

snorkel PYPI version =0.10.0, =0.19.0, =0.0.6, =1.0.2, =0.8.0, =0.1.1, =0.1.2, =0.1.0, =0.6.1, =0.0.0, =1.3.1a1 - t2r2 =0.0.1 - ws-benchmark =1.1.2rc0 Source cves: CVE-2026-31223 Source advisory: SNYK:PYTHON-SNORKEL-16758051...

8.8CVSS5.7AI score0.00392EPSS
Exploits0
vulnersOsv
vulnersOsv
added 2026/05/12 6:30 p.m.6 views

b2aiprep (>=0.19.0 <=3.3.3), capstone-text-mining (>=0.0.6 <=0.1.2) +10 more potentially affected by CVE-2026-31224 via snorkel (>=0.10.0 <=0.9.9)

snorkel PYPI version =0.10.0, =0.19.0, =0.0.6, =1.0.2, =0.8.0, =0.1.1, =0.1.2, =0.1.0, =0.6.1, =0.0.0, =1.3.1a1 - t2r2 =0.0.1 - ws-benchmark =1.1.2rc0 Source cves: CVE-2026-31224 Source advisory: SNYK:PYTHON-SNORKEL-16758048...

8.8CVSS5.7AI score0.00392EPSS
Exploits0
Packet Storm News
Packet Storm News
added 2025/11/28 12:0 a.m.6 views

Identification of Malicious Posts on the Dark Web Using Supervised Machine Learning

Given the constant growth and increasing sophistication of cyberattacks, cybersecurity can no longer rely solely on traditional defense techniques and tools. Proactive detection of cyber threats has become essential to help security teams identify potential risks and implement effective mitigatio...

6.6AI score
Exploits0
Imperva Blog
Imperva Blog
added 2021/05/18 1:37 p.m.40 views

Fast, Effective N-grams Extraction and Analysis with SQL

Features extraction is expensive, especially when dealing with big data. That’s why it’s great when you have the ability to preprocess close to the database - the data stays in the DB and doesn’t have to move out, unless necessary. One common approach for text data representation is N-grams...

7.7AI score
Exploits0
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