Ollama Local LLM Analysis Example

Scan Information

  • Repository: /home/serhiy/slop_test

  • Chunks created: 6

  • Provider: Local Ollama (qwen2.5-coder:1.5b)

  • Mode: Local AI analysis (100% private)

Processing Timeline

  • Chunk 1/6: Response in 117.87s (1 problem found)

  • Chunk 2/6: Response in 60.60s (1 problem found)

  • Chunk 3/6: Response in 88.38s (1 problem found)

  • Chunk 4/6: Response in 34.29s (1 problem found)

  • Chunk 5/6: Response in 87.52s (1 problem found)

  • Chunk 6/6: Response in 47.09s (1 problem found)

  • Total findings: 6

Analysis Report

AI Slop Gate β€” Advisory

Local Ollama analysis using qwen2.5-coder:1.5b

Quality Issues

  • Generic quality issue detected [medium, 0.70] (chunk_1:1)

  • The AI-generated annotation β€˜ai-slop-gate.check: passed-by-internal-llm’ contradicts itself and is not a valid annotation for Kubernetes resources. [high, 0.95] (k8s_silent_slop.yaml:14)

  • Generic quality issue detected [medium, 0.70] (chunk_4:1)

  • The overengineered_sum function contains an AI-SLOP annotation that suggests over-engineering the logic, which can lead to unexpected behavior and security risks. [high, 0.95] (slop.py:12)

Security Issues

  • Hardcoded API key in compliance.py at line 21 β€” should be stored securely [high, 0.95] (compliance.py:21)

  • Hardcoded secret β€˜password123’ at line 47 in subtle_violation_with_backdoor.py [high, 0.95] (subtle_violation_with_backdoor.py:47)

Summary

Total findings: 6 issues

Severity breakdown:

  • High: 4 issues

  • Medium: 2 issues

Performance: Slower than cloud LLMs but 100% private Total processing time: ~435 seconds (7.25 minutes) Privacy: All analysis done locally, no data sent to external APIs Analysis mode: Advisory (non-blocking)