Large language models for in-file vulnerability localization can be “Lost in the End”
Informazioni aggiuntive
Autori
Sovrano F.,
Bauer A.,
Bacchelli A.
Tipo
Contributo in atti di convegno
Anno
2025
Lingua
Inglese
Sommario
Traditionally, software vulnerability detection research has focused on individual small functions due to earlier language processing technologies’ limitations in handling larger inputs. However, this function-level approach may miss bugs that span multiple functions and code blocks. Recent advancements in artificial intelligence have enabled processing of larger inputs, leading everyday software developers to increasingly rely on chat-based large language models (LLMs) like GPT-3.5 and GPT-4 to detect vulnerabilities across entire files, not just within functions. This new development practice requires researchers to urgently investigate whether commonly used LLMs can effectively analyze large file-sized inputs, in order to provide timely insights for software developers and engineers about the pros and cons of this emerging technological trend. Hence, the goal of this paper is to evaluate the effectiveness of several state-of-the-art chat-based LLMs, including the GPT models, in detecting in-file vulnerabilities. We conducted a costly investigation into how the performance of LLMs varies based on vulnerability type, input size, and vulnerability location within the file. To give enough statistical power (𝛽 ≥.8) to our study, we could only focus on the three most common (as well as dangerous) vulnerabilities: XSS, SQL injection, and path traversal. Our findings indicate that the effectiveness of LLMs in detecting these vulnerabilities is strongly influenced by both the location of the vulnerability and the overall size of the input. Specifically, regardless of the vulnerability type, LLMs tend to significantly (𝑝 < .05) underperform when detecting vulnerabilities located toward the end of larger files—a pattern we call the ‘lost-in-the-end’ effect. Finally, to further support software developers and practitioners, we also explored the optimal input size for these LLMs and presented a simple strategy for identifying it, which can be applied to other models and vulnerability types. Eventually, we show how adjusting the input size can lead to significant improvements in LLM-based vulnerability detection, with an average recall increase of over 37% across all models.
Parole chiave
Large Language Models, In-file vulnerability detection, XSS, SQL injection, Path traversal, ‘Lost-in-the-End’ issue, Code context
Titolo atti di convegno
ACM on Software Engineering
Number ( Month )
FSE
Nome convegno
ACM International Conference on the Foundations of Software Engineering (FSE)
Luogo convegno
Trondheim, Norway
Data convegno
Mon 23 - Fri 27 June 2025
Volume
2
ISSN
2994-970X
DOI
Diffusione
Licenza
CC BY
Visibilità
Pubblico
Status open access
Hybrid
