arXiv:2605.24171v1 Announce Type: new Abstract: Large language models are increasingly used for vulnerability detection, yet their reliability under different prompt formulations remains uncharacterized. We present PromptAudit, a controlled evaluation framework that isolates prompt effects by fixing the dataset, decoding, and parsing while varying only the prompting strategy. Using five prompting strategies across five open-weight models on 1,000 CVEs (6,074 code samples spanning 16 programming languages), we evaluate accuracy, recall, abstention, coverage, and effective F1. We find that stand
The proliferation of Large Language Models (LLMs) into critical applications like cybersecurity necessitates immediate scrutiny into their reliability and robustness, as current deployments often lack sufficient validation.
Enterprises and governments increasingly rely on LLM-based tools for vulnerability detection; understanding prompt sensitivity is crucial for safe deployment, reducing false positives/negatives, and ensuring model integrity.
The research highlights significant variability in LLM performance based purely on prompt formulation, shifting the focus towards prompt engineering as a critical factor for secure LLM integration rather than just model architecture.
- · Prompt engineering specialists
- · Cybersecurity firms integrating LLMs
- · Companies developing LLM evaluation frameworks
- · Open-source LLM developers improving prompt robustness
- · Organizations deploying un-audited LLM security tools
- · LLM providers with poor prompt sensitivity documentation
- · Security teams reliant on 'black box' LLM solutions
Demand for prompt engineering expertise and tools for LLM security applications will increase.
New standards and regulations for validating LLM reliability in critical security functions may emerge.
The development of 'prompt-agnostic' or highly robust LLMs will become a key competitive differentiator in the AI security market.
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Read at arXiv cs.LG