Colosseum
Reports/koalixcrm

koalixcrm

Open Source Python CRM and ERP based on Django

KoalixSwitzerland576 starsPythonBSD-3-Clause
Scanned: February 16, 2026
Report Version: 1.0.0

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Overall Grade

B

1,204 total findings

Architecture
A58
Correctness
A6
Performance
C1140

What Traditional Tools Missed

CategoryColosseumBanditSemgrep
Bandit602120
Semgrep602012
Total1,2041212

1,204 findings (100%) were not detected by Bandit or Semgrep. These include compound vulnerabilities, logic errors, and context-dependent security issues that rule-based scanners aren't designed to catch.

Notable Findings

CriticalPerformance

Potential hotspot: create_sales_document_from_reference has high complexity score (48)

Impact: Potential hotspot: create_sales_document_from_reference has high complexity score (48)
Recommended Fix: Review and remediate.
CriticalPerformance

Potential hotspot: export_xml has high complexity score (16)

Impact: Potential hotspot: export_xml has high complexity score (16)
Recommended Fix: Review and remediate.
CriticalPerformance

Potential hotspot: export_pdf has high complexity score (16)

Impact: Potential hotspot: export_pdf has high complexity score (16)
Recommended Fix: Review and remediate.
CriticalPerformance

Potential hotspot: import_module has high complexity score (10)

Impact: Potential hotspot: import_module has high complexity score (10)
Recommended Fix: Review and remediate.
CriticalPerformance

Potential hotspot: init_with_context has high complexity score (49)

Impact: Potential hotspot: init_with_context has high complexity score (49)
Recommended Fix: Review and remediate.
CriticalPerformance

Potential hotspot: resolve_name has high complexity score (11)

Impact: Potential hotspot: resolve_name has high complexity score (11)
Recommended Fix: Review and remediate.
CriticalPerformance

Potential hotspot: create has high complexity score (68)

Impact: Potential hotspot: create has high complexity score (68)
Recommended Fix: Review and remediate.
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Responsible Disclosure

This public report is intended to demonstrate the depth of analysis possible with modern code scanning tools and to help the broader open source community understand common vulnerability patterns.

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