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Patrick Deininger

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Open access Sep 2026

Raw CVE/CWE Retrieval Does Not Improve LLM-Based Vulnerability Detection in Python: A Pre-Specified Null Result and a Retrieval-Dose Audit

Retrieval-Augmented Generation (RAG) over vulnerability databases is widely expected to improve LLM-based vulnerability detection. We report a pre-specified evaluation in which it does not, and derive from it an evaluation protocol. On a near-balanced benchmark of 100 Python 3.12 snippets, raw CVE/CWE retrieval never m...

Patrick Deininger, S. Rappl, Helmut Lindner et al. · 0 citations
#small language model Open access Sep 2026

Valid but Not Always Runnable: An Open, Reproducible Benchmark of Large Language Models Drafting Gherkin Scenarios

This work benchmarks eight LLMs generating Gherkin from three requirement corpora, scoring validity, runner acceptance, judged coverage and quality, similarity to gold standard, stability, and cost, and releases the corpora, gold standard, and prototype.

Patrick Deininger, Wolfgang Slany · 0 citations
#large language models Open access Sep 2026

Fixing, Breaking, or Faking It? An Execution-Calibrated Evaluation of LLM Vulnerability Patching in JavaScript, Python, Go, and Java, and the Limits of LLM-as-Judge

Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness, leaving no single judge trustworthy.

Patrick Deininger, Wolfgang Slany · 0 citations
Open access Jul 2026

GPU Passthrough Across Virtualization Platforms for LLM Inference: Configuration Complexity and a Small-Model Performance Baseline

A structured, paired comparison of configuration complexity and performance across six platforms (Proxmox VM and LXC, native KVM, OpenStack VM and Zun, and Podman) on a single NVIDIA RTX 4500 Ada GPU, emphasizing the under-documented container paths

Priska Steininger, Manfred Pamsl, Helmut Lindner et al. · 0 citations

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