LLM judgment over correct context problem

📰 Reddit r/artificial

Improve LLM judgment accuracy by addressing context problems in network config analysis for CVE false positives

advanced Published 8 Aug 2026

Action Steps

Test different LLM models to determine the best fit for your specific use case
Configure Gemma4 12B model to analyze network configs for CVE false positives
Batch process CVEs to improve efficiency and reduce memory usage
Optimize LLM input to focus on relevant context and reduce reasoning issues
Evaluate and refine the LLM model's performance using metrics such as pass rate

Who Needs to Know This

DevOps and security teams can benefit from this approach to enhance their vulnerability assessment and remediation workflows

Key Insight

💡 LLM models like Gemma4 can be optimized for specific use cases, but context problems can limit their effectiveness

Key Takeaways

Improve LLM judgment accuracy by addressing context problems in network config analysis for CVE false positives

Full Article

Ive tested all the models where it can fit into my 4080 vram. Even some slightly bigger. Gemma4 outperforms all of them so that's what I'm sticking with for now. Gemma4 12B model judging network configs for CVE false positives stuck at ~77.8% pass rate and it's a reasoning issue. Have the complete CVE list for the code base and the device config. I pull each networks device's running config, batch ~10 CVEs per call to a local gemma4:12b (Ollama), and have

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