{"url":"/sota/vulnerability-detection-on-vulnerability-java","task":{"name":"Vulnerability Detection","url":"/task/vulnerability-detection","note":null},"dataset":{"name":"Vulnerability Java Dataset","url":"/dataset/vulnerability-java-dataset"},"category":"Miscellaneous","categories":["Miscellaneous"],"category_note":null,"description":"Vulnerability detection plays a crucial role in safeguarding against these threats by identifying weaknesses and potential entry points that malicious actors could exploit. Through advanced scanning techniques and penetration testing, vulnerability detection tools meticulously analyze web applications and websites for vulnerabilities such as SQL injection, cross-site scripting (XSS), and insecure authentication mechanisms.\r\n\r\nBy proactively identifying and addressing vulnerabilities, organizations can strengthen their online security posture and mitigate the risk of data breaches, financial loss, and reputational damage. Additionally, vulnerability detection empowers businesses to stay compliant with industry regulations and standards, demonstrating their commitment to safeguarding sensitive information and maintaining the trust of their customers. With the evolving threat landscape and increasingly sophisticated attack vectors, investing in robust vulnerability detection measures is paramount for staying one step ahead of cyber threats and ensuring the resilience of web-based platforms and services.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["AUC","F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AUC":"higher","F1":"higher"}},"counts":{"rows":2,"rows_with_code":2,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"WizardCoder","metrics":{"AUC":"0.86","F1":"0.27"},"uses_additional_data":false,"paper_date":"2024-01-30","paper":"/paper/finetuning-large-language-models-for","paper_url":"https://arxiv.org/abs/2401.17010v5","paper_title":"Finetuning Large Language Models for Vulnerability Detection","code":"https://github.com/rmusab/vul-llm-finetune","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"ContraBERT","metrics":{"AUC":"0.85","F1":"0.22"},"uses_additional_data":false,"paper_date":"2024-01-30","paper":"/paper/finetuning-large-language-models-for","paper_url":"https://arxiv.org/abs/2401.17010v5","paper_title":"Finetuning Large Language Models for Vulnerability Detection","code":"https://github.com/rmusab/vul-llm-finetune","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}