On-Device vs. Cloud Deployment Trade-offs for SLMs and LLMs in CWE Detection for Private Python Codebases

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the trade-off between inference throughput and pass@1 accuracy for SLMs vs. LLMs in CWE detection tasks on private Python codebases when deployed on-device vs. in cloud environments. Large Language Models (LLMs) have demonstrated significant capabilities in understanding and analyzing code for security vulnerabilities, such as Common Weakness Enumerations (CWEs). However, their reliance on cloud infrastructure and substantial computational requirements pose. 3 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the trade-off between inference throughput and pass@1 accuracy for SLMs vs. LLMs in CWE detection tasks on private Python codebases when deployed on-device vs. in cloud environments?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance