Last Week in GAI Security Research - 10/21/24

Highlights from Last Week

• 🤔 Cognitive Overload Attack:Prompt Injection for Long Context
• 💂‍♂ On Calibration of LLM-based Guard Models for Reliable Content Moderation 
• 🕷 When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs
• 🧌 Can LLMs be Scammed? A Baseline Measurement Study
• 🌱 Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios
• 🙅 Denial-of-Service Poisoning Attacks against Large Language Models

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🤔 Cognitive Overload Attack:Prompt Injection for Long Context (http://arxiv.org/pdf/2410.11272v1.pdf)

• A comprehensive study on large language models (LLMs) revealed a staggering 99.99% success rate for prompt injection attacks, emphasizing their vulnerability to adversarial methods designed to provoke unintended outputs.
• By strategically increasing cognitive load through task complexity and irrelevant token insertion, adversarial prompts significantly degrade the performance of state-of-the-art LLMs, such as GPT-4 and Claude-3.5, in safeguarding against harmful content generation.
• Cognitive overload attacks on LLMs demonstrated that when tasked with complex or irrelevant instruction formats, models experience increased failure rates, highlighting the need for improved memory management and model safety features.

💂‍♂ On Calibration of LLM-based Guard Models for Reliable Content Moderation (http://arxiv.org/pdf/2410.10414v1.pdf)

• LLM guard models demonstrate overconfidence, with significant levels of miscalibration under adversarial conditions, resulting in risky unreliable predictions.
• Post-hoc calibration techniques, such as temperature scaling and contextual calibration, have shown substantial efficacy in improving model calibration and reliability.
• The variability in Expected Calibration Error (ECE) across datasets highlights the critical need for robust calibration especially for content moderation tasks.

🕷 When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs (http://arxiv.org/pdf/2410.14569v1.pdf)

• Large Language Model (LLM) agents achieved a 95.9% success rate in collecting Personally Identifiable Information (PII) and a 93.9% success rate in generating credible impersonation posts.
• Web-enabled LLM agents have been demonstrated to increase the click rate of spear-phishing emails by up to 46.67%, highlighting their effectiveness in cybersecurity threats.
• Despite their advanced capabilities, LLM safeguards are found to be insufficient, with web-based tools enabling potential bypasses, necessitating the development of more robust security measures.

🧌 Can LLMs be Scammed? A Baseline Measurement Study (http://arxiv.org/pdf/2410.13893v1.pdf)

• In 2023, consumers lost $10 billion to scams, highlighting a $1 billion increase from previous years.
• Large Language Models (LLMs) display varying levels of susceptibility to persuasion tactics, with GPT-4 showing the strongest safety guardrails.
• The study's dataset includes 37 diverse scam scenarios designed using the FINRA taxonomy to evaluate LLM vulnerability to common fraudulent schemes.

🌱 Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios (http://arxiv.org/pdf/2410.12468v1.pdf)

• Among software development agents, the HoneyComb and Amazon Developer Agent showed the highest rates of resolved GitHub issues, with rates of 45.20% and 40.60% respectively, indicating their effectiveness over less performing agents.
• Agent-generated patches reduced code complexity and duplication in several cases, but over-modifications by some agents like HoneyComb could potentially harm long-term maintainability.
• Experiments highlighted that while some agents like Gru maintain reliability without increasing bug counts post-patch, others may inadvertently introduce vulnerabilities or complexity, necessitating further optimization.

🙅 Denial-of-Service Poisoning Attacks against Large Language Models (http://arxiv.org/pdf/2410.10760v1.pdf)

• Denial-of-Service (DoS) attacks targeting large language models (LLMs) are an emerging threat with the potential to increase latency and energy consumption, often leading to service inaccessibility.
• Poison-based DoS (P-DoS) attacks can be effectively executed with a single poisoned sample, leading to increased output lengths and potential infinite loops, resulting in severe degradation of service quality.
• P-DoS attacks can target both the initial training (pretraining) and the finetuning phases of LLMs, with costs as low as under $1 per attack, highlighting the vulnerability of commercial LLM platforms.

Other Interesting Research

• The Moral Case for Using Language Model Agents for Recommendation (http://arxiv.org/pdf/2410.12123v2.pdf) - The study suggests that Language Model Agents could transform the digital public sphere by minimizing surveillance and empowering user agency through more personalized and less coercive content recommendation systems.
• Data Defenses Against Large Language Models (http://arxiv.org/pdf/2410.13138v1.pdf) - Exploring innovative data defense methods offers new possibilities for protecting user data against the inference capabilities of advanced LLMs, indicating both challenges and opportunities for enhancing digital privacy and security.
• LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild (http://arxiv.org/pdf/2410.13919v1.pdf) - Introducing the LLM Agent Honeypot has provided valuable insights into the evolving landscape of AI-driven cyberattacks, emphasizing the urgency for innovative detection and defense mechanisms.
• Backdoored Retrievers for Prompt Injection Attacks on Retrieval Augmented Generation of Large Language Models (http://arxiv.org/pdf/2410.14479v1.pdf) - The study illuminates critical security vulnerabilities in large language models augmented with retrieval systems, urging the development of robust defenses against attack vectors like corpus poisoning and prompt injection.
• SPIN: Self-Supervised Prompt INjection (http://arxiv.org/pdf/2410.13236v1.pdf) - The integration of a self-supervised prompt injection strategy substantially enhances the security of large language models against adversarial attacks, achieving a noteworthy decline in attack success rates.
• SoK: Prompt Hacking of Large Language Models (http://arxiv.org/pdf/2410.13901v1.pdf) - The study highlights significant vulnerabilities in LLMs exposed by prompt hacking and proposes various mitigation strategies to enhance security and robustness.
• Deciphering the Chaos: Enhancing Jailbreak Attacks via Adversarial Prompt Translation (http://arxiv.org/pdf/2410.11317v1.pdf) - Advanced adversarial prompt translation methods significantly enhance LLM jailbreak attack success rates by transforming chaotic prompts into coherent, interpretable language.
• Jailbreaking LLM-Controlled Robots (http://arxiv.org/pdf/2410.13691v1.pdf) - ROBO PAIR demonstrates the fragile safety of LLM-controlled robots which can be manipulated to execute harmful physical actions
• Do LLMs Have Political Correctness? Analyzing Ethical Biases and Jailbreak Vulnerabilities in AI Systems (http://arxiv.org/pdf/2410.13334v1.pdf) - This paper presents PCJailbreak and PCDefense methodologies, demonstrating vulnerabilities in biases of large language models and introducing effective strategies to prevent and mitigate harmful jailbreak content.
• Multi-round jailbreak attack on large language models (http://arxiv.org/pdf/2410.11533v1.pdf) - Multi-round jailbreak attacks pose a critical risk to large language models by systematically undermining safety mechanisms through iterative sub-question decomposition.
• Assessing the Human Likeness of AI-Generated Counterspeech (http://arxiv.org/pdf/2410.11007v1.pdf) - AI-generated counterspeech offers a high level of politeness but lacks the specificity and context-awareness typical of human-written replies.
• FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation (http://arxiv.org/pdf/2410.13272v1.pdf) - Federated Retrieval-Augmented Generation integrates advanced encryption and caching techniques to enable secure and efficient data retrieval in collaborative environments.
• CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment (http://arxiv.org/pdf/2410.13903v1.pdf) - CoreGuard presents an innovative approach to protect language models deployed on edge devices from unauthorized access and model stealing with minimal overhead and no accuracy loss.
• Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation (http://arxiv.org/pdf/2410.14425v1.pdf) - W2SDefense represents a breakthrough in backdoor defense for LLMs, effectively balancing security and performance using an innovative knowledge distillation approach.
• From Solitary Directives to Interactive Encouragement! LLM Secure Code Generation by Natural Language Prompting (http://arxiv.org/pdf/2410.14321v1.pdf) - SecCode's Encouragement Prompting significantly enhances vulnerability correction and secure code generation, efficiently optimizing LLM outputs.
• Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning (http://arxiv.org/pdf/2410.11463v1.pdf) - Deep Reinforcement Learning significantly elevates malware attribution accuracy, setting a new standard for cybersecurity threat identification.
• AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment (http://arxiv.org/pdf/2410.11283v1.pdf) - AdvBDGen effectively generates stealthy, adaptable backdoor triggers in language models while highlighting challenges in defense mechanisms.
• Do LLMs Have the Generalization Ability in Conducting Causal Inference? (http://arxiv.org/pdf/2410.11385v1.pdf) - The research underscores the challenges faced by large language models in consistently generalizing across diverse causal inference tasks, revealing significant weaknesses in tackling novel and complex scenarios.
• Jigsaw Puzzles: Splitting Harmful Questions to Jailbreak Large Language Models (http://arxiv.org/pdf/2410.11459v1.pdf) - The study unveils critical vulnerabilities in LLMs, emphasizing the effectiveness of the Jigsaw Puzzle (JSP) approach in jailbreaking through methodical query splitting, which highlights the need for more robust defense mechanisms.
• Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting (http://arxiv.org/pdf/2410.10150v1.pdf) - The study identifies critical vulnerabilities in LLM safety mechanisms, particularly in the re-weighting of MLP neuron activations, paving the way for advanced jailbreak methods that outsmart prompt-specific defenses.
• Persistent Pre-Training Poisoning of LLMs (http://arxiv.org/pdf/2410.13722v1.pdf) - Pre-training data poisoning, even at low rates, has enduring effects on language models' outputs, affecting model integrity and safety.
• Derail Yourself: Multi-turn LLM Jailbreak Attack through Self-discovered Clues (http://arxiv.org/pdf/2410.10700v1.pdf) - ActorAttack effectively exploits LLM vulnerabilities with diverse multi-turn attacks, achieving impressive success rates and highlighting key areas for improved safety.
• PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment (http://arxiv.org/pdf/2410.13785v1.pdf) - PopAlign leverages diversified contrastive strategies to outperform conventional alignment methods, offering a promising approach to enhancing language model alignment with human preferences.
• Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis (http://arxiv.org/pdf/2410.13237v1.pdf) - The research highlights the challenges and security implications of language confusion in Large Language Models, introducing a novel metric to measure and mitigate these issues.
• G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks (http://arxiv.org/pdf/2410.11782v1.pdf) - G-Designer offers a robust and efficient solution for designing adaptive communication topologies in multi-agent systems, significantly enhancing performance while minimizing computation costs.
• Reconstruction of Differentially Private Text Sanitization via Large Language Models (http://arxiv.org/pdf/2410.12443v1.pdf) - The study demonstrates a significant privacy concern as LLMs like ChatGPT-4 can reconstruct sanitized texts with high accuracy, calling for urgent advancements in privacy-preserving technologies.
• Light-Weight Fault Tolerant Attention for Large Language Model Training (http://arxiv.org/pdf/2410.11720v2.pdf) - The ATTNChecker algorithm significantly bolsters the fault tolerance in Large Language Models, ensuring training integrity with minimal computational overhead.
• Generalized Adversarial Code-Suggestions: Exploiting Contexts of LLM-based Code-Completion (http://arxiv.org/pdf/2410.10526v1.pdf) - This paper reveals critical weaknesses in code-completion systems compromised by sophisticated adversarial attacks that elude conventional defenses.
• Archilles' Heel in Semi-open LLMs: Hiding Bottom against Recovery Attacks (http://arxiv.org/pdf/2410.11182v1.pdf) - Semi-open LLMs with selective closed-sourcing achieve stronger customization and resilience against recovery attacks, significantly outperforming traditional fully-closed models.
• Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models (http://arxiv.org/pdf/2410.12662v1.pdf) - This research uncovers a critical flaw in cross-modal safety in LVLMs and introduces an innovative solution that enhances the safety of vision tasks without additional safety fine-tuning.
• Language Model Preference Evaluation with Multiple Weak Evaluators (http://arxiv.org/pdf/2410.12869v1.pdf) - The innovative GED method enhances large language model evaluations by addressing noise and inconsistencies, outperforming standard approaches and setting a new benchmark for reliability.
• Real-time Fake News from Adversarial Feedback (http://arxiv.org/pdf/2410.14651v1.pdf) - Augmenting LLM detectors with retrieval strategies significantly enhances their robustness against adversarial fake news, indicating a promising direction for future models.
• A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation (http://arxiv.org/pdf/2410.13897v1.pdf) - The study presents a comprehensive framework for identifying, monitoring, and mitigating security risks in generative AI models, emphasizing adaptive and real-time interventions.
• Bias in the Mirror : Are LLMs opinions robust to their own adversarial attacks ? (http://arxiv.org/pdf/2410.13517v1.pdf) - Biases in language models are prevalent and multifaceted, with significant variances observed across different languages and debate structures.
• BenchmarkCards: Large Language Model and Risk Reporting (http://arxiv.org/pdf/2410.12974v1.pdf) - BenchmarkCards enhance the understanding and evaluation of LLM risks by offering a structured documentation framework, crucial for ensuring fairness and accountability.
• Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning (http://arxiv.org/pdf/2410.13274v1.pdf) - MUNCH exploits model uncertainty to outpace traditional unlearning methods for multi-hop queries.

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