arXiv subjects

Fujiao Ji

Publications and source records attributed to Fujiao Ji.

4 recordsLinked to original sources

arXiv2026

Poster: Mind the Gap -- Characterizing the Temporal Blind Spot Between GSB and DNS Resolution

Google Safe Browsing (GSB) and DNS resolution operate concurrently during browser navigation, yet their packet-level synchronization remains understudied. This work characterizes the timing gap ($\Delta_{time}$) between GSB-related query close events and parallel DNS resolution responses, identifying a consistent temporal offset with potential security relevance. Using packet-capture analysis across general and CNAME-domain datasets, we observe positive timing gaps in approximately 79\% of measurements. In these instances, DNS responses lag behind GSB-related query closures with median delays of 67-79 ms and maximum delays surpassing 2,400 ms. These empirical results highlight a measurable window within the browsing workflow. We suggest that such temporal inconsistencies, particularly in complex CNAME-domain resolutions, may create a security-relevant timing precondition under DNS-manipulation threat models. These results provide a foundation for further research into timing-based risks and mitigations in browser safety mechanisms.

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arXiv2025

How Can We Effectively Use LLMs for Phishing Detection?: Evaluating the Effectiveness of Large Language Model-based Phishing Detection Models

Large language models (LLMs) have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor generalization to previously unseen websites and a lack of interpretability. However, LLMs' effectiveness for phishing detection remains unexplored. This study investigates how to effectively leverage LLMs for phishing detection (including target brand identification) by examining the impact of input modalities (screenshots, logos, HTML, and URLs), temperature settings, and prompt engineering strategies. Using a dataset of 19,131 real-world phishing websites and 243 benign sites, we evaluate seven LLMs -- two commercial models (GPT 4.1 and Gemini 2.0 flash) and five open-source models (Qwen, Llama, Janus, DeepSeek-VL2, and R1) -- alongside two deep learning (DL)-based baselines (PhishIntention and Phishpedia). Our findings reveal that commercial LLMs generally outperform open-source models in phishing detection, while DL models demonstrate better performance on benign samples. For brand identification, screenshot inputs achieve optimal results, with commercial LLMs reaching 93-95% accuracy and open-source models, particularly Qwen, achieving up to 92%. However, incorporating multiple input modalities simultaneously or applying one-shot prompts does not consistently enhance performance and may degrade results. Furthermore, higher temperature values reduce performance. Based on these results, we recommend using screenshot inputs with zero temperature to maximize accuracy for LLM-based detectors with HTML serving as auxiliary context when screenshot information is insufficient.

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arXiv2024

Evaluating the Effectiveness and Robustness of Visual Similarity-based Phishing Detection Models

Phishing attacks pose a significant threat to Internet users, with cybercriminals elaborately replicating the visual appearance of legitimate websites to deceive victims. Visual similarity-based detection systems have emerged as an effective countermeasure, but their effectiveness and robustness in real-world scenarios have been underexplored. In this paper, we comprehensively scrutinize and evaluate the effectiveness and robustness of popular visual similarity-based anti-phishing models using a large-scale dataset of 451k real-world phishing websites. Our analyses of the effectiveness reveal that while certain visual similarity-based models achieve high accuracy on curated datasets in the experimental settings, they exhibit notably low performance on real-world datasets, highlighting the importance of real-world evaluation. Furthermore, we find that the attackers evade the detectors mainly in three ways: (1) directly attacking the model pipelines, (2) mimicking benign logos, and (3) employing relatively simple strategies such as eliminating logos from screenshots. To statistically assess the resilience and robustness of existing models against adversarial attacks, we categorize the strategies attackers employ into visible and perturbation-based manipulations and apply them to website logos. We then evaluate the models' robustness using these adversarial samples. Our findings reveal potential vulnerabilities in several models, emphasizing the need for more robust visual similarity techniques capable of withstanding sophisticated evasion attempts. We provide actionable insights for enhancing the security of phishing defense systems, encouraging proactive actions.

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arXiv2024

Ocassionally Secure: A Comparative Analysis of Code Generation Assistants

$ $Large Language Models (LLMs) are being increasingly utilized in various applications, with code generations being a notable example. While previous research has shown that LLMs have the capability to generate both secure and insecure code, the literature does not take into account what factors help generate secure and effective code. Therefore in this paper we focus on identifying and understanding the conditions and contexts in which LLMs can be effectively and safely deployed in real-world scenarios to generate quality code. We conducted a comparative analysis of four advanced LLMs--GPT-3.5 and GPT-4 using ChatGPT and Bard and Gemini from Google--using 9 separate tasks to assess each model's code generation capabilities. We contextualized our study to represent the typical use cases of a real-life developer employing LLMs for everyday tasks as work. Additionally, we place an emphasis on security awareness which is represented through the use of two distinct versions of our developer persona. In total, we collected 61 code outputs and analyzed them across several aspects: functionality, security, performance, complexity, and reliability. These insights are crucial for understanding the models' capabilities and limitations, guiding future development and practical applications in the field of automated code generation.

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