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Cybersecurity LLM benchmarks unreliable due to pipeline dependency

By PulseAugur Editorial · [1 sources] ·

A new audit of cybersecurity LLM benchmarks reveals that benchmark scores are highly dependent on the evaluation pipeline used, rather than being fixed datasets. Researchers identified 15 systematic failure modes, demonstrating that a single pipeline choice can alter a model's score by over 80 percentage points and significantly change its ranking. Even semantically similar tasks can yield different rankings due to incompatible evaluation conventions. The study advocates for pipeline-aware auditing to ensure reliable model evaluation. AI

IMPACT Highlights the need for standardized evaluation methodologies to ensure accurate and comparable LLM performance metrics.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM benchmark reliability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Cybersecurity LLM benchmarks unreliable due to pipeline dependency

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The cluster contains an academic paper detailing research findings on LLM benchmark reliability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Aymene Berriche, Cathrine Shalby, Mohannad Alhanahnah, Yazan Boshmaf ·

    Benchmark Scores Are Pipeline-Dependent: A Reliability Audit of Cybersecurity LLM Benchmarks

    arXiv:2609.08765v1 Announce Type: cross Abstract: Large language model (LLM) benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pipelines. We audit eight cybersecurity benchmarks across 10 proprietary, open-weig…

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