Last Week in GAI Security Research - 11/25/24

Highlights from Last Week

• 🦹‍♂ RAG-Thief: Scalable Extraction of Private Data from Retrieval-Augmented Generation Applications with Agent-based Attacks
• 🐠 Adapting to Cyber Threats: A Phishing Evolution Network (PEN) Framework for Phishing Generation and Analyzing Evolution Patterns using Large Language Models
• 🧵 A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection
• 👹 ChatHTTPFuzz: Large Language Model-Assisted IoT HTTP Fuzzing
• 🪲 Feasibility Study for Supporting Static Malware Analysis Using LLM
• 🔊 Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models

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🦹‍♂ RAG-Thief: Scalable Extraction of Private Data from Retrieval-Augmented Generation Applications with Agent-based Attacks (http://arxiv.org/pdf/2411.14110v1.pdf)

• RAG-Thief can extract over 70% of targeted private information using tailored queries, showcasing significant vulnerability in RAG systems.
• Untargeted attacks using RAG-Thief maintain an extraction average three times more efficient than baseline methods, suggesting improvements in query generation strategies.
• Custom RAG applications demonstrated a recovery rate up to 80% on platforms like Coze, indicating a high risk of private data exposure in commercial environments.

🐠 Adapting to Cyber Threats: A Phishing Evolution Network (PEN) Framework for Phishing Generation and Analyzing Evolution Patterns using Large Language Models (http://arxiv.org/pdf/2411.11389v1.pdf)

• PEN-generated phishing samples, leveraging large language models, boost detection accuracy by 40% and improve model robustness against perturbations by 60%.
• The framework achieves an 80% mimicry rate and exhibits a 99% deceptive persuasion score with phishing samples, indicating high fidelity in generated content.
• Adversarial training using PEN-phishing data reduces success rates of phishing attacks by 20% to 70%, highlighting the robustness of detectors post-fine-tuning.

🧵 A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection (http://arxiv.org/pdf/2411.12946v1.pdf)

• The use of synthetic datasets for off-topic prompt detection in large language models (LLMs) has reduced false positives and improved classification performance, surpassing traditional models in precision and recall.
• A flexible, data-free guardrail development methodology allows large language models to detect off-topic prompts effectively in the absence of real-world datasets, making pre-production deployment more reliable.
• Fine-tuned classifiers trained on synthetic data have shown strong performance with fewer false positives, crucial for maintaining user trust and compliance in domains like healthcare and legal services.

👹 ChatHTTPFuzz: Large Language Model-Assisted IoT HTTP Fuzzing (http://arxiv.org/pdf/2411.11929v1.pdf)

• ChatHTTPFuzz outperforms existing fuzzing tools by identifying 68 previously undisclosed vulnerabilities, of which 23 have been assigned CVEs, demonstrating superior security detection capabilities.
• The LLM-assisted approach offers a 98.58% packet field coverage and achieves a false negative rate of 4.74% for HTTP parameter parsing, indicating high accuracy in identifying potential vulnerabilities.
• ChatHTTPFuzz effectively reduces the time needed for vulnerability discovery by about 50% when compared to models not utilizing its advanced seed template scheduling algorithms.

🪲 Feasibility Study for Supporting Static Malware Analysis Using LLM (http://arxiv.org/pdf/2411.14905v1.pdf)

• Large Language Models achieved a 90.9% accuracy in generating static malware function descriptions, indicating strong potential for assisting malware analysis.
• Analysts found the LLM-generated descriptions fluent, relevant, and informative, with an average practical score of 3.17 out of 4 on the Likert scale, highlighting usability in real-world applications.
• Challenges noted include possible confidentiality issues when using external LLM services like ChatGPT, emphasizing the need for localized server solutions to maintain data security.

🔊 Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models (http://arxiv.org/pdf/2411.14842v1.pdf)

• GPT-4o emerged as the most resilient large language model, maintaining robust performance across all tested adversarial audio attacks, including emotional variations and explicit noise interferences.
• The Chat-Audio Attacks benchmark revealed significant vulnerabilities in large language models to adversarial audio attacks, indicating a need for enhanced defense mechanisms in real-world audio applications.
• The Emotional and Explicit Noise Attacks posed the greatest challenge to the tested models, particularly affecting models like SpeechGPT and SALMONN, which showed vulnerabilities under these conditions.

Other Interesting Research

• GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs (http://arxiv.org/pdf/2411.14133v1.pdf) - GASP successfully generates adversarial prompts that bypass LLM safety measures by optimizing human-readable suffixes for higher attack success rates.
• JailbreakLens: Interpreting Jailbreak Mechanism in the Lens of Representation and Circuit (http://arxiv.org/pdf/2411.11114v1.pdf) - Uncontrolled jailbreak prompts expose critical vulnerabilities in language model safety mechanisms, requiring more robust interpretative frameworks and enhanced training strategies.
• CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection (http://arxiv.org/pdf/2411.13627v1.pdf) - The study highlights the intricate potential of LLMs in cryptographic protocol verification, yet underscores their dependency on human intervention for accurate vulnerability detection.
• Comparing Robustness Against Adversarial Attacks in Code Generation: LLM-Generated vs. Human-Written (http://arxiv.org/pdf/2411.10565v1.pdf) - The study reveals a significant robustness advantage of human-authored code over large language model-generated code against adversarial attacks.
• ProSec: Fortifying Code LLMs with Proactive Security Alignment (http://arxiv.org/pdf/2411.12882v1.pdf) - PROSEC's innovative approach significantly strengthens code security in large language models with minimal compromise on functional utility.
• A Code Knowledge Graph-Enhanced System for LLM-Based Fuzz Driver Generation (http://arxiv.org/pdf/2411.11532v1.pdf) - CodeGraphGPT leverages a structured knowledge graph to enhance fuzz driver generation, achieving superior code coverage and bug detection compared to traditional methods.
• The Dark Side of Trust: Authority Citation-Driven Jailbreak Attacks on Large Language Models (http://arxiv.org/pdf/2411.11407v1.pdf) - Exploring the vulnerability of LLMs towards authoritative sources reveals high success rates for jailbreak attacks, while effective strategies significantly bolster defenses.
• Next-Generation Phishing: How LLM Agents Empower Cyber Attackers (http://arxiv.org/pdf/2411.13874v1.pdf) - The integration of LLMs in phishing practices demonstrates a potent decrease in detection accuracy, highlighting a critical gap in current cybersecurity defenses.
• WaterPark: A Robustness Assessment of Language Model Watermarking (http://arxiv.org/pdf/2411.13425v1.pdf) - LLM watermarkers need tailored detectors for optimal security, as generic models show reduced effectiveness against complex attacks.
• When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations (http://arxiv.org/pdf/2411.12701v1.pdf) - Backdoor attacks on LLMs reduce explanation quality, introduce predictable patterns, and compromise prediction accuracy through altered attention and confidence dynamics.
• CROW: Eliminating Backdoors from Large Language Models via Internal Consistency Regularization (http://arxiv.org/pdf/2411.12768v1.pdf) - CROW offers a scalable and efficient backdoor defense for LLMs, achieving low attack rates with minimal impact on generative performance.
• TrojanRobot: Backdoor Attacks Against Robotic Manipulation in the Physical World (http://arxiv.org/pdf/2411.11683v1.pdf) - The integration of vision-language models into robotic manipulation systems creates new vulnerabilities to backdoor attacks that are stealthy and effective in altering task outcomes.
• Playing Language Game with LLMs Leads to Jailbreaking (http://arxiv.org/pdf/2411.12762v1.pdf) - Language game methods demonstrate high success rates in bypassing safety measures of advanced language models, exposing critical vulnerabilities and highlighting the need for improved defense mechanisms.
• SoK: A Systems Perspective on Compound AI Threats and Countermeasures (http://arxiv.org/pdf/2411.13459v1.pdf) - Addressing the multifaceted security challenges in compound AI systems demands a cross-layer approach integrating software, hardware, and algorithmic defenses.
• AttentionBreaker: Adaptive Evolutionary Optimization for Unmasking Vulnerabilities in LLMs through Bit-Flip Attacks (http://arxiv.org/pdf/2411.13757v1.pdf) - AttentionBreaker reveals that minimal targeted bit-flip attacks can severely compromise the reliability and performance of large language models, highlighting critical security vulnerabilities.
• LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection (http://arxiv.org/pdf/2411.10918v1.pdf) - The integration of large language models in cyber-physical systems skyrockets anomaly detection efficiency by extracting and validating physical invariants with unprecedented precision.
• On the Privacy Risk of In-context Learning (http://arxiv.org/pdf/2411.10512v1.pdf) - Prompting LLMs, while efficient, presents higher privacy risks but can be mitigated with strategic ensembling.
• Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment (http://arxiv.org/pdf/2411.11543v1.pdf) - PSA-VLM's concept-based safety strategy effectively aligns vision-language model outputs with high-level safety standards, enhancing robustness against unsafe content.
• Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations (http://arxiv.org/pdf/2411.10414v1.pdf) - Llama Guard 3 Vision effectively enhances multimodal human-AI interaction safety by addressing image reasoning challenges and providing a robust defense against adversarial attacks.
• Membership Inference Attack against Long-Context Large Language Models (http://arxiv.org/pdf/2411.11424v1.pdf) - Membership inference attacks reveal significant privacy risks in Long-Context Language Models, necessitating robust defense mechanisms.
• Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods (http://arxiv.org/pdf/2411.11795v1.pdf) - This paper highlights JPEG AI's pioneering role in balancing compression efficiency with robustness against adversarial attacks, underscoring its potential impact on future image compression standards.
• Universal and Context-Independent Triggers for Precise Control of LLM Outputs (http://arxiv.org/pdf/2411.14738v1.pdf) - Significant advancements in universal trigger techniques expose vulnerabilities in large language models, necessitating stronger defenses against context-independent attacks.
• Global Challenge for Safe and Secure LLMs Track 1 (http://arxiv.org/pdf/2411.14502v1.pdf) - The study reveals critical vulnerabilities in LLMs and proposes innovative methods to enhance security against automated jailbreak attacks, underlining the evolving need for robust defense mechanisms in AI technologies.
• Memory Backdoor Attacks on Neural Networks (http://arxiv.org/pdf/2411.14516v1.pdf) - Memory backdoor attacks present a significant threat to data privacy, enabling adversaries to extract training data from models while offering potential avenues for detection and prevention.
• Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models (http://arxiv.org/pdf/2411.14842v1.pdf) - The study establishes the Chat-Audio Attacks benchmark, uncovering resilience deficits in large language models against audio adversarial attacks and emphasizing the superiority of GPT-4o in maintaining coherence and semantic similarity.
  

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