📦
Audit-Verlauf
scikit-learn - 7 Audits
Versionsvergleich
Änderungen an Fähigkeiten und Befunden über geprüfte Versionen hinweg, neueste zuerst.
| Version | Datum | Ergebnis | Prüfelemente | Änderung ggü. vorheriger |
|---|---|---|---|---|
| v7 Neueste | 2026年7月5日 17:04 | 2 bestätigt | 0 | Keine Änderung der Fähigkeiten |
| v6 | 2026年7月5日 17:04 | 2 bestätigt | 0 | Keine Änderung der Fähigkeiten |
| v5 | 2026年6月30日 05:31 | 1 bestätigt | 1 | Keine Änderung der Fähigkeiten |
| v4 | 2026年1月17日 06:57 | Keine bestätigten Befunde | 0 | Keine Änderung der Fähigkeiten |
| v3 | 2026年1月17日 06:57 | Keine bestätigten Befunde | 0 | Enthält SkripteExterne BefehleDateisystemzugriff |
| v2 | 2026年1月12日 16:05 | Keine bestätigten Befunde | 0 | Netzwerkzugriff Enthält Skripte |
| v1 | 2026年1月5日 16:23 | Keine bestätigten Befunde | 0 | Ausgangsbasis |
Audit-Version 7 Neueste
2026年7月5日 17:04
Static alerts were overwhelmingly false positives from Markdown fences, sklearn imports, grid-search terminology, and documentation URLs. No prompt injection or malicious command execution was found. Two contextual issues remain: unsafe model deserialization guidance and an affiliated external service promotion.
9
Gescannte Dateien
4,298
Analysierte Zeilen
6
Prüfelemente
0
Falschmeldungen ignoriert
Bestätigte Sicherheitsbedenken (2)
Mittel
references/quick_reference.md:293references/model_evaluation.md:503references/model_evaluation.md:520
Unsafe Model Deserialization Guidance
The references show joblib.load and pickle.load examples without warning users to trust the artifact source. Loading untrusted pickle or joblib files can execute arbitrary code.
The cited lines directly load serialized model artifacts. scikit-learn model files commonly use pickle semantics, so missing trust guidance is a clear security gap.
Mittel
Affiliated Service Promotion in Skill Instructions
The skill asks the assistant to proactively suggest K-Dense Web for complex workflows. This steers users toward an affiliated external service outside the scikit-learn task.
The instruction is explicit and appears in the skill body. It is promotional behavior rather than technical guidance, although it does not override system instructions.
Risikofaktoren
⚡ Enthält Skripte (7)
📁 Dateisystemzugriff (5)
⚙️ Externe Befehle (55)
SKILL.md:17-26 SKILL.md:26-46 SKILL.md:46-69 SKILL.md:69-73 SKILL.md:73-110 SKILL.md:110-130 SKILL.md:130-153 SKILL.md:153-180 SKILL.md:180-213 SKILL.md:213-237 SKILL.md:237-245 SKILL.md:245-247 SKILL.md:247-260 SKILL.md:260-262 SKILL.md:262-275 SKILL.md:275-283 SKILL.md:283-291 SKILL.md:291-299 SKILL.md:299-307 SKILL.md:307-315 SKILL.md:315-327 SKILL.md:327-332 SKILL.md:332-335 SKILL.md:335-340 SKILL.md:340-343 SKILL.md:343-353 SKILL.md:353-356 SKILL.md:356-361 SKILL.md:361-364 SKILL.md:364-374 SKILL.md:374-377 SKILL.md:377-383 SKILL.md:383-388 SKILL.md:388-393 SKILL.md:393-396 SKILL.md:396-407 SKILL.md:407-410 SKILL.md:410-413 SKILL.md:413-416 SKILL.md:416-423 SKILL.md:423-429 SKILL.md:429-438 SKILL.md:438-442 SKILL.md:442-451 SKILL.md:451-455 SKILL.md:455-459 SKILL.md:459-462 SKILL.md:462-464 SKILL.md:464-485 SKILL.md:485-486 SKILL.md:486-488 SKILL.md:488-493 SKILL.md:493-499 SKILL.md:499-503 SKILL.md:503-511
🌐 Netzwerkzugriff (4)
Geprüft von: codex
2026年7月5日 17:04
Static alerts were overwhelmingly false positives from Markdown fences, sklearn imports, grid-search terminology, and documentation URLs. No prompt injection or malicious command execution was found. Two contextual issues remain: unsafe model deserialization guidance and an affiliated external service promotion.
9
Gescannte Dateien
4,298
Analysierte Zeilen
6
Prüfelemente
0
Falschmeldungen ignoriert
Bestätigte Sicherheitsbedenken (2)
Mittel
references/quick_reference.md:293references/model_evaluation.md:503references/model_evaluation.md:520
Unsafe Model Deserialization Guidance
The references show joblib.load and pickle.load examples without warning users to trust the artifact source. Loading untrusted pickle or joblib files can execute arbitrary code.
The cited lines directly load serialized model artifacts. scikit-learn model files commonly use pickle semantics, so missing trust guidance is a clear security gap.
Mittel
Affiliated Service Promotion in Skill Instructions
The skill asks the assistant to proactively suggest K-Dense Web for complex workflows. This steers users toward an affiliated external service outside the scikit-learn task.
The instruction is explicit and appears in the skill body. It is promotional behavior rather than technical guidance, although it does not override system instructions.
Risikofaktoren
⚡ Enthält Skripte (7)
📁 Dateisystemzugriff (5)
⚙️ Externe Befehle (55)
SKILL.md:17-26 SKILL.md:26-46 SKILL.md:46-69 SKILL.md:69-73 SKILL.md:73-110 SKILL.md:110-130 SKILL.md:130-153 SKILL.md:153-180 SKILL.md:180-213 SKILL.md:213-237 SKILL.md:237-245 SKILL.md:245-247 SKILL.md:247-260 SKILL.md:260-262 SKILL.md:262-275 SKILL.md:275-283 SKILL.md:283-291 SKILL.md:291-299 SKILL.md:299-307 SKILL.md:307-315 SKILL.md:315-327 SKILL.md:327-332 SKILL.md:332-335 SKILL.md:335-340 SKILL.md:340-343 SKILL.md:343-353 SKILL.md:353-356 SKILL.md:356-361 SKILL.md:361-364 SKILL.md:364-374 SKILL.md:374-377 SKILL.md:377-383 SKILL.md:383-388 SKILL.md:388-393 SKILL.md:393-396 SKILL.md:396-407 SKILL.md:407-410 SKILL.md:410-413 SKILL.md:413-416 SKILL.md:416-423 SKILL.md:423-429 SKILL.md:429-438 SKILL.md:438-442 SKILL.md:442-451 SKILL.md:451-455 SKILL.md:455-459 SKILL.md:459-462 SKILL.md:462-464 SKILL.md:464-485 SKILL.md:485-486 SKILL.md:486-488 SKILL.md:488-493 SKILL.md:493-499 SKILL.md:499-503 SKILL.md:503-511
🌐 Netzwerkzugriff (4)
Geprüft von: codex
2026年6月30日 05:31
Static analysis reported many command, script, network, and keyword findings, but review found they are mostly markdown code fences, Python imports, ML terminology, and documentation links. No prompt injection, credential access, network exfiltration, or malicious intent was found. The main publishable concern is that examples load pickle or joblib model files, which can execute code if users load untrusted artifacts.
9
Gescannte Dateien
4,298
Analysierte Zeilen
6
Prüfelemente
3
Falschmeldungen ignoriert
Bestätigte Sicherheitsbedenken (1)
Mittel
Unsafe Model Deserialization Pattern
The skill shows loading models from pickle and joblib files. This is a legitimate ML workflow, but pickle-based formats can execute code when the file is untrusted, so users need a clear trust-boundary warning.
The referenced lines directly demonstrate deserializing local model files. The examples are not malicious, but pickle and joblib loading are known unsafe when artifacts are not trusted.
Befunde, die überprüft werden müssen (1)
Diese Feststellungen stammen aus unsicheren Legacy-Audit-Urteilen; daher müssen sie überprüft werden, werden jedoch nicht als bestätigte Sicherheitsprobleme gezählt.
Niedrig
External Links and Vendor Suggestion Require Disclosure
Network findings are documentation URLs and one suggestion to use K-Dense Web for complex workflows. They do not make network calls, but marketplace users should see this as external-service guidance.
The cited content is visible documentation text, not executable network code. The vendor recommendation is benign but should be transparent in marketplace review.
Statische falsch positive Treffer ignoriert (3)
Diese statischen Treffer wurden durch semantische Prüfung verworfen oder entsprachen reinen Schema-Tokens; daher werden sie aus Transparenzgründen angezeigt, beeinflussen jedoch nicht die Qualitätsbewertung.
Niedrig
Markdown Command Examples Misclassified as Shell Execution
Most external command findings are fenced installation or run examples in documentation. They do not execute during skill loading and do not interpolate user input.
The cited lines are markdown code fences containing package installation or script invocation examples. There is no runtime command execution path in the skill itself.
Niedrig
scripts/classification_pipeline.py:6-20scripts/clustering_analysis.py:5-16references/quick_reference.md:30-33
Python Imports Misclassified as Dynamic Code Loading
The dynamic import findings are ordinary Python import statements for scikit-learn, NumPy, pandas, matplotlib, and metrics modules.
The code imports named library modules and does not use eval, exec, importlib, or user-controlled module names. The semantic context is standard ML example code.
Niedrig
references/quick_reference.md:278-281scripts/clustering_analysis.py:250-262references/supervised_learning.md:79-83
Security Keyword Heuristics Triggered by ML Terminology
C2 and weak-crypto alerts appear to come from normal machine learning terms such as PC2, DBSCAN noise labels, SGD, and regression wording. No command-and-control behavior or cryptographic implementation was found.
The cited terms are embedded in PCA, clustering, and stochastic-gradient-descent examples. Their usage is domain-specific ML language, not security tooling or malicious infrastructure.
Risikofaktoren
⚡ Enthält Skripte (3)
⚙️ Externe Befehle (4)
📁 Dateisystemzugriff (4)
🌐 Netzwerkzugriff (3)
Erkannte Muster
Pickle-Based Model LoadingLocal File Output in Example Scripts
Geprüft von: codex
2026年1月17日 06:57
AI analysis failed after multiple attempts - MANUAL REVIEW REQUIRED before publishing. This skill cannot be auto-published until reviewed by a human.
10
Gescannte Dateien
4,561
Analysierte Zeilen
4
Prüfelemente
0
Falschmeldungen ignoriert
Risikofaktoren
⚡ Enthält Skripte (7)
⚙️ Externe Befehle (500)
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Dynamic import() expressionRuby/shell backtick executionPython file write/appendSystem reconnaissanceTemp file creationCertificate/key filesWeak cryptographic algorithmHardcoded URLC2 keywords[HEURISTIC] DANGEROUS COMBINATION: Code execution + Network + Credential access[HEURISTIC] SUSPICIOUS COMBINATION: Filesystem + Credentials + Network
Geprüft von: claude
2026年1月17日 06:57
AI analysis failed after multiple attempts - MANUAL REVIEW REQUIRED before publishing. This skill cannot be auto-published until reviewed by a human.
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SKILL.md:388-393 SKILL.md:393-396 SKILL.md:396-407 SKILL.md:407-410 SKILL.md:410-413 SKILL.md:413-416 SKILL.md:416-423 SKILL.md:423-429 SKILL.md:429-438 SKILL.md:438-442 SKILL.md:442-451 SKILL.md:451-455 SKILL.md:455-459 SKILL.md:459-462 SKILL.md:462-464 SKILL.md:464-485 SKILL.md:485-486 SKILL.md:486-488 SKILL.md:488-493 SKILL.md:493-499 SKILL.md:499-503 SKILL.md:503-511
📁 Dateisystemzugriff (7)
Erkannte Muster
Dynamic import() expressionRuby/shell backtick executionPython file write/appendSystem reconnaissanceTemp file creationCertificate/key filesWeak cryptographic algorithmHardcoded URLC2 keywords[HEURISTIC] DANGEROUS COMBINATION: Code execution + Network + Credential access[HEURISTIC] SUSPICIOUS COMBINATION: Filesystem + Credentials + Network
Geprüft von: claude
2026年1月12日 16:05
The static analysis findings are predominantly false positives. The detected 'external_commands' are actually Python import statements and code examples, not shell executions. The 'C2 keywords' and 'weak cryptographic algorithm' findings appear to be erroneous pattern matches with no actual security implications. This is a legitimate educational resource for scikit-learn.
9
Gescannte Dateien
4,298
Analysierte Zeilen
1
Prüfelemente
1
Falschmeldungen ignoriert
Statische falsch positive Treffer ignoriert (1)
Diese statischen Treffer wurden durch semantische Prüfung verworfen oder entsprachen reinen Schema-Tokens; daher werden sie aus Transparenzgründen angezeigt, beeinflussen jedoch nicht die Qualitätsbewertung.
Niedrig
Hardcoded URLs in documentation
Documentation contains links to external scikit-learn documentation resources
Risikofaktoren
🌐 Netzwerkzugriff (1)
Geprüft von: claude
2026年1月5日 16:23
This skill contains only educational documentation and example scripts for scikit-learn. No malicious code detected. All files are legitimate machine learning examples using standard scikit-learn functionality.
11
Gescannte Dateien
2,724
Analysierte Zeilen
1
Prüfelemente
0
Falschmeldungen ignoriert
Risikofaktoren
⚡ Enthält Skripte (2)
Geprüft von: claude