arXiv subjects

Yepeng Yao

Publications and source records attributed to Yepeng Yao.

3 recordsLinked to original sources

arXiv2026

PYPILINE: Malicious PyPI Package Detection via Suspicious API Knowledge and Agent Workflow

Detecting malicious PyPI packages is crucial for maintaining the security of the open source software supply chain. Traditional static rule detection methods require continuous maintenance by experienced security personnel, resulting in high labor costs. Dynamic analysis methods require actual execution of the target package code, posing a risk of malicious code proliferation, and incurring significant runtime overhead and low detection efficiency. Machine learning and LLM methods iterate the detection kernel but cannot invoke multiple tools, resulting in insufficient automation.To address these issues, we propose a novel detection method called PYPILINE, which combines suspicious API knowledge and agent workflow. PYPILINE first performs static analysis on known malicious packages, extracting abstract syntax trees and generating API call graphs. From these graphs, a structured suspicious API knowledge base is extracted and constructed. In the agent workflow, PYPILINE uses RAG technology to invoke this knowledge base to enhance analytical capabilities, performing in-depth semantic analysis of the packages, outputting structured evaluation reports, and automatically sending the reports to a mail server.Experimental results show that PYPILINE achieves precision of 96.7\%, recall of 99.6\%, and F1 score of 98.1\%. F1 score is improved by 5.7 to 21.6 percentage points compared to baseline tools. When 30 threads execute concurrently, detecting a single package takes an average of only 0.6 seconds.Furthermore, we conducted a large scale empirical study of malware packages, systematically revealing common attack strategies and the most frequently abused APIs. PYPILINE provides an intelligent, efficient, and automated package detection solution, enhancing the security of the open source software ecosystem.

cs.CR↗

arXiv2026

PhishSigma++: Malicious Email Detection with Typed Entity Relations

Here is a further shortened version (pure text, no extra formatting, academic style preserved, no content change): Abstract. With the rise of AI-generated content (AIGC), phishing actors now possess richer linguistic capabilities and evasion techniques. Most existing detectors over-rely on mutable textual features, achieving high accuracy on clean data but degrading severely under text-focused adversarial manipulation. This mirrors the lab-to-real performance gap. We investigate invariant signals in phishing emails: even when attackers modify surface text, functional intent constrains relations among typed entities. Threat-actor tradecraft is described via high-level TTPs, but rule-based systems like Sigma express invariants only through manually curated, field-specific patterns, limiting flexibility. We introduce PhishSigma++, an entity-relation-based malicious email detector for RFC822 messages that generalizes Sigma's design. It extracts 40 typed entity classes, computes 5 cross-type relations to build a typed email graph, and uses particle swarm optimization (PSO) to select a sparse discriminative mask, supporting classification and type-level evidence summary. On 29,142 messages, PhishSigma++ achieves 0.9675 F1 on clean data and outperforms text-centric baselines under non-adaptive Good Word padding at \r{ho}=0.8. It maintains 0.9579 F1, while a token-based Bayesian filter collapses to 0.0243 and a DistilBERT phishing checkpoint falls to 0.7284. Compared with traditional Sigma rules, PhishSigma++ offers higher detection, broader relational invariance coverage, and data-driven feature selection. We also show that thresholded typed relation scores encode a useful fragment of Sigma-style field conditions, unifying hand-crafted rule logic and learned relation masks in a single-email framework.

cs.CR↗

arXiv2024

Can LLMs Deeply Detect Complex Malicious Queries? A Framework for Jailbreaking via Obfuscating Intent

To demonstrate and address the underlying maliciousness, we propose a theoretical hypothesis and analytical approach, and introduce a new black-box jailbreak attack methodology named IntentObfuscator, exploiting this identified flaw by obfuscating the true intentions behind user prompts.This approach compels LLMs to inadvertently generate restricted content, bypassing their built-in content security measures. We detail two implementations under this framework: "Obscure Intention" and "Create Ambiguity", which manipulate query complexity and ambiguity to evade malicious intent detection effectively. We empirically validate the effectiveness of the IntentObfuscator method across several models, including ChatGPT-3.5, ChatGPT-4, Qwen and Baichuan, achieving an average jailbreak success rate of 69.21\%. Notably, our tests on ChatGPT-3.5, which claims 100 million weekly active users, achieved a remarkable success rate of 83.65\%. We also extend our validation to diverse types of sensitive content like graphic violence, racism, sexism, political sensitivity, cybersecurity threats, and criminal skills, further proving the substantial impact of our findings on enhancing 'Red Team' strategies against LLM content security frameworks.

cs.CR↗