# 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

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## Analysis Report

### AI Slop Gate — Advisory

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

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### Quality Issues

- Generic quality issue detected [medium, 0.70] (chunk_1:1)
- The AI-generated annotation '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 — should be stored securely [high, 0.95] (compliance.py:21)
- Hardcoded secret 'password123' at line 47 in subtle_violation_with_backdoor.py [high, 0.95] (subtle_violation_with_backdoor.py:47)

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## 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)