Last Week in GAI Security Research - 07/01/24

Highlights from Last Week

• 🪱 Synthetic Cancer – Augmenting Worms with LLMs
• 🔗 Large Language Models for Link Stealing Attacks Against Graph Neural Networks 
• 🧑‍💻 Assessing the Effectiveness of LLMs in Android Application Vulnerability Analysis
• 🦠 MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization
• 🦜 Poisoned LangChain: Jailbreak LLMs by LangChain
• 🤝 Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models

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🪱 Synthetic Cancer – Augmenting Worms with LLMs (http://arxiv.org/pdf/2406.19570v1.pdf)

• LLM-based malware, capable of rewriting its code to avoid detection, presents a new form of cyber threat that spreads through socially engineered emails.
• Despite potential for abuse, the publication and open discussion of such malware is vital for advancing threat identification and developing adequate countermeasures.
• Safety features in language models, intended to prevent malicious use, can be circumvented, allowing for the potential spread of malware through seemingly innocuous email chains.

🔗 Large Language Models for Link Stealing Attacks Against Graph Neural Networks (http://arxiv.org/pdf/2406.16963v1.pdf)

• Fine-tuning Large Language Models (LLMs) with prompts specifically designed for link stealing tasks significantly enhances their performance, achieving up to 90% accuracy and F1 scores in identifying links between nodes in Graph Neural Networks (GNNs).
• The application of LLMs in link stealing demonstrates versatility across different datasets, where training on multiple datasets further improves performance, underlying the importance of generalization in attack methodologies.
• Experiments confirm the susceptibility of GNNs to privacy attacks via link stealing, with LLMs outperforming traditional methods in both white-box and black-box settings, indicating a substantial threat to the privacy and integrity of GNN-based systems.

🧑‍💻 Assessing the Effectiveness of LLMs in Android Application Vulnerability Analysis (http://arxiv.org/pdf/2406.18894v1.pdf)

• GPT-4 and Code Llama emerged as the top performers in detecting vulnerabilities within Android code out of nine tested large language models (LLMs), showcasing their effectiveness in identifying and suggesting improvements for security vulnerabilities.
• The study revealed a disparity in detection capabilities among LLMs, with some models excelling in specific vulnerability categories, indicating a strategic selection process could enhance targeted vulnerability detection using both open-source and commercial LLMs.
• Large Language Models (LLMs) demonstrated a potential to surpass static application security testing tools (SASTs) in detecting code vulnerabilities, offering a promising approach for improving the detection and mitigation of security risks in mobile applications.

🦠 MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization (http://arxiv.org/pdf/2406.18379v1.pdf)

• MALSIGHT introduces a dataset, MalS, with 90,000 functions from GitHub for training a novel summarization model, MalT5, showcasing effective binary malware summarization with high-quality annotations.
• MalT5 achieves comparable performance to ChatGPT3.5, illuminated by a novel evaluation benchmark (BLEURT-sum), confirming its robustness in generating concise and accurate malware function summaries.
• Experimentation across three distinct datasets verifies MALSIGHT's effectiveness in binary malware summarization, addressing challenges in semantic information loss and pseudocode summarization with a lightweight, 0.77B parameter model.

🦜 Poisoned LangChain: Jailbreak LLMs by LangChain (http://arxiv.org/pdf/2406.18122v1.pdf)

• The Poisoned-LangChain method achieved jailbreak success rates of 88.56%, 79.04%, and 82.69% across various scenarios, demonstrating significant vulnerabilities in large language models.
• Integration of malicious keywords and dialogues into a knowledge base was utilized to circumvent security filters, exposing critical weaknesses in the current design of language models.
• Defensive strategies and real-time updates to the models' knowledge bases are deemed essential to mitigate the effectiveness of jailbreak attacks and ensure the integrity of language model responses.

🤝 Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models (http://arxiv.org/pdf/2406.16244v1.pdf)

• The Sóley method identifies nine new logic vulnerabilities in Ethereum smart contracts, surpassing the detection capabilities of traditional logic vulnerabilities detection methods.
• Sóley's implementation and evaluation on a dataset of 50k smart contracts revealed a significant improvement in detecting logic vulnerabilities with accuracy rates better than baseline LLMs by 5%-9%.
• Introduced 15 mitigation strategies for the identified logic vulnerabilities, providing actionable insights for enhancing the security and sustainability of Ethereum smart contracts.

Other Interesting Research

• Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text Detection (http://arxiv.org/pdf/2406.16275v1.pdf) - FAILOpt significantly boosts AIGT detector robustness by utilizing diverse prompts and counteracting prompt-specific shortcuts.
• ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods (http://arxiv.org/pdf/2406.15968v1.pdf) - RECALL marks a significant step forward in transparently and efficiently detecting pretraining data within large language models through conditional log-likelihood analysis and ensemble methods.
• Automated Adversarial Discovery for Safety Classifiers (http://arxiv.org/pdf/2406.17104v1.pdf) - Automated methods for uncovering safety classifier vulnerabilities highlight critical gaps and opportunities for enhancing digital safety mechanisms.
• Enhancing Data Privacy in Large Language Models through Private Association Editing (http://arxiv.org/pdf/2406.18221v1.pdf) - PAE emerges as an efficient strategy to fortify LLMs against privacy threats, highlighting its potential for widespread adoption in securing sensitive data without compromising model integrity.
• Noisy Neighbors: Efficient membership inference attacks against LLMs (http://arxiv.org/pdf/2406.16565v1.pdf) - Exploring membership inference attacks reveals significant privacy vulnerabilities in LLMs, underscoring the importance of calibration strategies and the challenges of generalizing protective measures.
• Adversarial Search Engine Optimization for Large Language Models (http://arxiv.org/pdf/2406.18382v1.pdf) - Preference Manipulation Attacks reveal vulnerabilities in LLM-enhanced search engines, highlighting the need for robust defenses against economic-motivated manipulation.
• Inherent Challenges of Post-Hoc Membership Inference for Large Language Models (http://arxiv.org/pdf/2406.17975v1.pdf) - The research highlights the vulnerability of Large Language Models to Membership Inference Attacks and proposes mitigation strategies like RDD to counteract potential data memorization.
• CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference (http://arxiv.org/pdf/2406.17626v1.pdf) - Evaluating LLMs' safety in multi-turn dialogues exposes significant vulnerabilities, highlighting the effectiveness of specific interventions and the ongoing need for improved safety mechanisms.
• Machine Unlearning Fails to Remove Data Poisoning Attacks (http://arxiv.org/pdf/2406.17216v1.pdf) - Machine unlearning proves inadequate against data poisoning, failing to match the efficacy of full model retraining, and showcasing task-dependent algorithm performance.
• A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4 code interpreter (http://arxiv.org/pdf/2406.18075v1.pdf) - Adopting a context-driven approach to co-auditing smart contracts with GPT-4 significantly improves vulnerability detection and auditing efficiency.
• BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models (http://arxiv.org/pdf/2406.17092v1.pdf) - BEEAR significantly lowers the success rate of safety backdoor attacks in LLMs, offering robust defense even against sophisticated, stealthy threats without predefined assumptions on triggers.
• Virtual Context: Enhancing Jailbreak Attacks with Special Token Injection (http://arxiv.org/pdf/2406.19845v1.pdf) - Virtual Context significantly enhances the efficacy of jailbreak attacks against LLMs, raising success rates and efficiency while requiring fewer resources.
• Monitoring Latent World States in Language Models with Propositional Probes (http://arxiv.org/pdf/2406.19501v1.pdf) - Propositional probes offer a promising solution for monitoring and mitigating unfaithfulness and biases in language models by understanding and interpreting latent world states.
• SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance (http://arxiv.org/pdf/2406.18118v2.pdf) - SafeAligner enhances LLMs' defenses against jailbreak attacks by leveraging response disparity guidance, maintaining general capabilities and operational efficiency.
• WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs (http://arxiv.org/pdf/2406.18495v1.pdf) - WILDGUARD introduces a significant advancement in moderation tools, reducing jailbreak success rates and matching or exceeding GPT-4's performance in safety moderation.
• Jailbreaking LLMs with Arabic Transliteration and Arabizi (http://arxiv.org/pdf/2406.18725v1.pdf) - LLMs exhibit vulnerabilities to generating unsafe content when prompted with non-standard forms of Arabic, revealing significant gaps in current safety training protocols.
• WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models (http://arxiv.org/pdf/2406.18510v1.pdf) - WILDTEAMING's automated red-teaming framework and the WILDJAILBREAK dataset significantly advance LLM safety training and robustness against adversarial attacks.
• Seeing Is Believing: Black-Box Membership Inference Attacks Against Retrieval Augmented Generation (http://arxiv.org/pdf/2406.19234v1.pdf) - New MIA methods tailored for RAG systems demonstrate improved efficiency and highlight crucial privacy implications, urging the need for enhanced security measures.

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