Last Week in GAI Security Research - 01/13/25

Highlights from Last Week

• 📝 A survey of textual cyber abuse detection using cutting-edge language models and large language models
• 🪲 Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware
• 📦 FlipedRAG: Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
• 🌊 RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models
• 💬 SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs 

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📝 A survey of textual cyber abuse detection using cutting-edge language models and large language models (http://arxiv.org/pdf/2501.05443v1.pdf)

• Large Language Models (LLMs) like BERT and RoBERTa dominate the landscape in detecting hate speech and cyberbullying, with BERT being used in 77% of published papers for these tasks.
• Research highlights the significant challenge of class imbalance in cyber abuse detection, with 31.4% of studies addressing this through techniques like oversampling, undersampling, and weighted metrics.
• Cyberbullying detection has shown promising advancements in accuracy through models that combine BERT with Bi-LSTM networks, achieving a macro-averaged F1 score of up to 0.9231.

🪲 Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware (http://arxiv.org/pdf/2501.04848v1.pdf)

• MalParse, a model leveraging large language models for Android malware analysis, achieved a 77% categorization accuracy compared to 49.5% with simpler approaches, without needing pre-training on malware datasets.
• Through hierarchical and contextual summarization of Android application components, MalParse provides actionable insights into malicious behaviors, outperforming other scoping methods by efficiently tracing root causes within the software structure.
• The study underscores the potential of advanced prompt engineering and hierarchical-tiered summarization techniques in enhancing the accuracy and comprehensibility of large language models in cybersecurity, notably in identifying and classifying malware threats.

📦 FlipedRAG: Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models (http://arxiv.org/pdf/2501.02968v1.pdf)

• The FlipedRAG attack method demonstrated a successful 50% change in opinion polarity and induced a 20% shift in user cognition, revealing the vulnerability of retrieval-augmented generation models in opinion manipulation contexts.
• Advanced adversarial strategies targeting black-box retrieval systems showed that language models' outputs could be manipulated, affecting reliability and potentially spreading biased or false information.
• Defense mechanisms against opinion manipulation in RAG systems are currently inadequate, necessitating new defensive strategies to counteract the effects of adversarial retrieval attacks and safeguard against cognitive manipulation.

🌊 RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models (http://arxiv.org/pdf/2501.05249v1.pdf)

• RAG-WM detects IP infringement in RAG systems with 100% verification success and minimal impact on task performance across various large language models and datasets.
• The approach effectively embeds high-quality watermark texts within a retrieval-augmented generation system, maintaining stealthiness against adversarial paraphrasing and unrelated content removal attacks.
• Watermark detection methods using perplexity and duplicate text filtering provide robust protection for intellectual property without degrading the quality of generated content.

💬 SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs (http://arxiv.org/pdf/2501.04985v1.pdf)

• Fine-tuning LLMs, especially the Mixtral model, achieves near-perfect spam detection accuracy of 98.61%, with a false positive rate below 2%.
• Few-shot learning significantly improves the variability and performance of LLMs in detecting SMS spam, achieving a 97.18% accuracy with GPT-4.
• LLMs exhibit strong resilience against concept drift and adversarial attacks after fine-tuning, maintaining robust performance over new spam datasets.

Other Interesting Research

• Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense (http://arxiv.org/pdf/2501.02629v1.pdf) - Layer-AdvPatcher enhances LLM safety by efficiently editing toxic layers and substantially reducing vulnerabilities to jailbreak attacks while preserving essential model performance.
• Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering (http://arxiv.org/pdf/2501.05165v1.pdf) - The study provides actionable insights into improving defect prediction, leveraging machine learning for vulnerability detection, and enhancing software development with quantum computing capabilities.
• LLM4CVE: Enabling Iterative Automated Vulnerability Repair with Large Language Models (http://arxiv.org/pdf/2501.03446v1.pdf) - Leveraging an advanced LLM pipeline, software vulnerability repair can reach unprecedented levels of effectiveness, quality improvement, and operational efficiency.
• CommitShield: Tracking Vulnerability Introduction and Fix in Version Control Systems (http://arxiv.org/pdf/2501.03626v1.pdf) - COMMIT SHIELD's integration of natural language processing and code analysis for vulnerability fix detection significantly outperforms traditional SZZ algorithms in precision, recall, and F1-score, showcasing its efficacy in managing open-source software security issues.
• Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models (http://arxiv.org/pdf/2501.04312v1.pdf) - The paper presents DFUZZ, a highly efficient LLM-based framework that excels in bug detection and API coverage by extracting transferable edge cases in deep learning libraries like PyTorch and TensorFlow.
• CGP-Tuning: Structure-Aware Soft Prompt Tuning for Code Vulnerability Detection (http://arxiv.org/pdf/2501.04510v1.pdf) - CGP-Tuning demonstrates enhanced efficiency and performance in code vulnerability detection by integrating type-aware embeddings and optimizing graph-text interactions, outperforming traditional methods.
• Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasets (http://arxiv.org/pdf/2501.02628v1.pdf) - An evaluation of Stack v2 datasets reveals substantial security vulnerabilities and licensing issues, providing key insights into risks associated with large-scale LLM dataset curation and the necessity for enhanced automated code quality interventions.
• PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models (http://arxiv.org/pdf/2501.03544v1.pdf) - PromptGuard stands out for its efficiency and effectiveness in moderating NSFW content in text-to-image models without significant image quality loss.
• Navigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models (http://arxiv.org/pdf/2501.04323v2.pdf) - GuardedTuning offers a robust and privacy-focused solution for fine-tuning large language models by balancing utility, privacy, and communication costs.
• Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection (http://arxiv.org/pdf/2501.03940v1.pdf) - PAWN emerges as a robust, resource-efficient method for cross-domain AI-generated text detection, performing well even under adversarial conditions.
• Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization (http://arxiv.org/pdf/2501.05079v1.pdf) - The integration of language models with GNSS interference monitoring systems significantly enhances classification accuracy and supports real-time interference detection, promoting more resilient GNSS applications.
• HP-BERT: A framework for longitudinal study of Hinduphobia on social media via LLMs (http://arxiv.org/pdf/2501.05482v1.pdf) - HP-BERT shows strong potential for monitoring harmful sentiments like Hinduphobia on social media, especially during crises like the COVID-19 pandemic.

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