Zahin Wahab

Sadif Ahmed, Md Nafiu Rahman, Zahin Wahab et al.

In the digital era, accidental exposure of sensitive information such as API keys, tokens, and credentials is a growing security threat. While most prior work focuses on detecting secrets in source code, leakage in software issue reports remains largely unexplored. This study fills that gap through a large-scale analysis and a practical detection pipeline for exposed secrets in GitHub issues. Our pipeline combines regular expression-based extraction with large language model (LLM)-based contextual classification to detect real secrets and reduce false positives. We build a benchmark of 54,148 instances from public GitHub issues, including 5,881 manually verified true secrets. Using this dataset, we evaluate entropy-based baselines and keyword heuristics used by prior secret detection tools, classical machine learning, deep learning, and LLM-based methods. Regex and entropy based approaches achieve high recall but poor precision, while smaller models such as RoBERTa and CodeBERT greatly improve performance (F1 = 92.70%). Proprietary models like GPT-4o perform moderately in few-shot settings (F1 = 80.13%), and fine-tuned open-source larger LLMs such as Qwen and LLaMA reach up to 94.49% F1. Finally, we also validate our approach on 178 real-world GitHub repositories, achieving an F1-score of 81.6% which demonstrates our approach's strong ability to generalize to in-the-wild scenarios.

Md Nafiu Rahman, Sadif Ahmed, Zahin Wahab et al.

GitHub and GitLab are widely used collaborative platforms whose issue-tracking systems contain large volumes of unstructured text, including logs, code snippets, and configuration examples. This creates a significant risk of accidental secret exposure, such as API keys and credentials, yet these platforms provide no mechanism to warn users before submission. We present \textsc{IssueGuard}, a tool for real-time detection and prevention of secret leaks in issue reports. Implemented as a Chrome extension, \textsc{IssueGuard} analyzes text as users type and combines regex-based candidate extraction with a fine-tuned CodeBERT model for contextual classification. This approach effectively separates real secrets from false positives and achieves an F1-score of 92.70\% on a benchmark dataset, outperforming traditional regex-based scanners. \textsc{IssueGuard} integrates directly into the web interface and continuously analyzes the issue editor, presenting clear visual warnings to help users avoid submitting sensitive data. The source code is publicly available at \href{https://github.com/disa-lab/IssueGuard}{https://github.com/disa-lab/IssueGuard} , and a demonstration video is available at \href{https://youtu.be/kvbWA8rr9cU}{https://youtu.be/kvbWA8rr9cU} .