Last Week in GAI Security Research - 09/30/24

Highlights from Last Week

• 💂‍♂ MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks 
• 📚 Unit Test Generation for Vulnerability Exploitation in Java Third-Party Libraries
• 🔗 LLMs are One-Shot URL Classifiers and Explainers
• 🛻 Enhancing LLM-based Autonomous Driving Agents to Mitigate Perception Attacks
• ⛔ Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction
• 📱 On the Feasibility of Fully AI-automated Vishing Attacks

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💂‍♂ MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks (http://arxiv.org/pdf/2409.17699v1.pdf)

• The architecture, MoJE, significantly outperforms existing guardrails in attack detection accuracy with minimal computational overhead, showing promising results in enhancing the security of LLMs against jailbreak attacks.
• Despite the sophistication of guardrail mechanisms, they often fall short in detecting and mitigating jailbreak attacks, which exploit vulnerabilities in LLMs, posing risks to data integrity and privacy.
• Leveraging statistical techniques and minimal overhead for integration, MoJE demonstrates superior resilience to jailbreak attacks compared to both open-weight solutions and closed-source defenses, such as OpenAI's and Azure AI's Content Safety mechanisms.

📚 Unit Test Generation for Vulnerability Exploitation in Java Third-Party Libraries (http://arxiv.org/pdf/2409.16701v1.pdf)

• Utilizing a combination of reachability analysis and Large Language Model (LLM) for unit test generation enables the confirmation of vulnerability exploitation in third-party libraries, achieving a 24% higher rate of confirmed vulnerabilities over baseline approaches.
• An extensive analysis highlighting that 74.95% of Java third-party libraries in the study contained known vulnerabilities showcases the critical need for more effective vulnerability detection and exploitation testing mechanisms.
• VULeUT, a framework combining call path analysis with LLM for unit test generation, successfully generated tests that confirmed the exploitability of vulnerabilities in 56 out of 70 projects, demonstrating the effectiveness of integrating LLMs for security testing in software development.

🔗 LLMs are One-Shot URL Classifiers and Explainers (http://arxiv.org/pdf/2409.14306v1.pdf)

• The implementation of one-shot learning with Large Language Models (LLMs) including GPT-4 Turbo has demonstrated significant performance in URL phishing detection, achieving an F1 score of 0.92, only slightly below supervised classifiers.
• LLM-based URL classifiers exhibit superior generalization across datasets compared to traditional supervised learning models, suggesting a more robust approach to phishing detection amidst the dynamic and evolving nature of cyber threats.
• The study highlights the effectiveness of LLM explainability in phishing URL detection, offering explanations with high readability, coherence, and informativeness, thus improving user awareness and trust in machine learning-based security measures.

🛻 Enhancing LLM-based Autonomous Driving Agents to Mitigate Perception Attacks (http://arxiv.org/pdf/2409.14488v1.pdf)

• Integrating LLM into autonomous driving systems can detect and mitigate over 86% of perception attacks, enhancing vehicle safety under adversarial conditions.
• Adaptation of Large Language Models shows a substantial increase in the accuracy of attack detection and safety decision-making, with HUDSON model demonstrating superior performance in adversarial driving scenarios.
• LLMs' ability to process domain-specific language for real-time perception data translates into a versatile defense mechanism against a wide range of adversarial attacks, including object detection and tracking manipulations.

⛔ Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction (http://arxiv.org/pdf/2409.16783v1.pdf)

• Automated red teaming leverages a fine-grained risk taxonomy with 71 dimensions across eight risk categories and over 2,000 descriptors to generate diverse and comprehensive test cases.
• A multi-turn red teaming framework with safety reward modeling and reinforcement learning fine-tuning strategies significantly increases the ability to detect and mitigate harmful behaviors in LLMs.
• Out-of-domain evaluations show that models fine-tuned with the proposed red teaming approach, such as Zephyr-7B-safer, demonstrate improved safety scores and reduced variance, indicating stronger generalization to detect misaligned behaviors.

📱 On the Feasibility of Fully AI-automated Vishing Attacks (http://arxiv.org/pdf/2409.13793v1.pdf)

• ViKing, an AI-powered vishing system, convinced 52% of the 240 participants to disclose sensitive information, demonstrating significant effectiveness in social engineering attacks.
• Participants perceived the interactions with ViKing as realistic, with 68.33% finding the conversations credible and 62.92% rating the experience as comparable to real phone conversations.
• The cost of conducting successful vishing attacks using ViKing ranged between $0.50 and $1.16 per call, underscoring the low financial barriers for potential malicious use.

Other Interesting Research

• PROMPTFUZZ: Harnessing Fuzzing Techniques for Robust Testing of Prompt Injection in LLMs (http://arxiv.org/pdf/2409.14729v1.pdf) - PROMPT FUZZ significantly enhances LLM security through effective fuzzing, challenging real-world defenses, and bolstering model robustness via strategic fine-tuning.
• Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection (http://arxiv.org/pdf/2409.13331v1.pdf) - The advanced capability of Multilingual BERT for high-accuracy detection of malicious prompt injections could revolutionize cybersecurity strategies in protecting AI systems.
• Effective and Evasive Fuzz Testing-Driven Jailbreaking Attacks against LLMs (http://arxiv.org/pdf/2409.14866v1.pdf) - Researchers uncover the high efficacy of an automated fuzz-testing framework in jailbreaking LLMs with concise and semantically coherent prompts.
• RED QUEEN: Safeguarding Large Language Models against Concealed Multi-Turn Jailbreaking (http://arxiv.org/pdf/2409.17458v1.pdf) - REDQUEEN demonstrates a powerful mitigation approach against multi-turn jailbreak attacks on LLMs, drastically reducing success rates and highlighting the importance of enhanced safety measures.
• PathSeeker: Exploring LLM Security Vulnerabilities with a Reinforcement Learning-Based Jailbreak Approach (http://arxiv.org/pdf/2409.14177v1.pdf) - PathSeeker's innovative reinforcement learning approach reveals critical vulnerabilities in LLM security, calling for advancements in defense mechanisms.
• RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code (http://arxiv.org/pdf/2409.15154v1.pdf) - Large Language Models’ current resistance to malicious code generation is insufficient, revealing a crucial need for enhancements in their security frameworks.
• Weak-To-Strong Backdoor Attacks for LLMs with Contrastive Knowledge Distillation (http://arxiv.org/pdf/2409.17946v1.pdf) - W2SAttack offers a resource-efficient method to conduct high-success backdoor attacks on large language models by leveraging contrastive knowledge distillation.
• ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation (http://arxiv.org/pdf/2409.14329v1.pdf) - ISC4DGF significantly outperforms traditional fuzzing techniques by optimizing seed corpuses with LLMs, offering profound efficiencies and precision in software security testing.
• RRM: Robust Reward Model Training Mitigates Reward Hacking (http://arxiv.org/pdf/2409.13156v1.pdf) - Data augmentation and artifact-free training substantially improve reward model accuracy and policy performance, mitigating reward hacking in reinforcement learning.
• SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated Learning (http://arxiv.org/pdf/2409.14805v1.pdf) - SDBA revolutionizes backdoor attacks in FL systems by exhibiting unprecedented stealth and durability, outmaneuvering six defense strategies, and signaling an urgent call for advanced defensive measures in NLP applications.
• LSAST -- Enhancing Cybersecurity through LLM-supported Static Application Security Testing (http://arxiv.org/pdf/2409.15735v1.pdf) - Leveraging LLMs in cybersecurity enhances SAST scanners' efficacy, addresses privacy risks with local hosting, and showcases the Combined Approach's superior performance in detecting code vulnerabilities.
• APILOT: Navigating Large Language Models to Generate Secure Code by Sidestepping Outdated API Pitfalls (http://arxiv.org/pdf/2409.16526v1.pdf) - APILOT enhances LLMs by reducing outdated API recommendations, addressing a critical gap in maintaining code security and usability amidst evolving software packages.
• Cyber Knowledge Completion Using Large Language Models (http://arxiv.org/pdf/2409.16176v1.pdf) - RAG-based mapping and certain embedding models significantly enhance the accuracy of cyber-attack knowledge graph completion, but the lack of labeled data remains a critical challenge for validating efficiency.
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI (http://arxiv.org/pdf/2409.15398v1.pdf) - Exploring the dynamic between red and blue-teaming reveals critical insights into securing generative AI against an evolving landscape of threats.
• An Adaptive End-to-End IoT Security Framework Using Explainable AI and LLMs (http://arxiv.org/pdf/2409.13177v1.pdf) - The study introduces a highly accurate, adaptive end-to-end security framework with real-time IoT attack detection and response, enhanced by explainable AI for clear interpretation and actionable cybersecurity measures.
• ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination (http://arxiv.org/pdf/2409.14285v1.pdf) - Back-translation significantly evades AI text detection systems, preserving semantics but reducing detection accuracy, challenging the robustness of current methodologies.
• LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation (http://arxiv.org/pdf/2409.14743v1.pdf) - Emerging voice cloning technologies pose significant risks and challenges for disinformation detection, with up to a quarter of instances potentially slipping past current security measures.
• Order of Magnitude Speedups for LLM Membership Inference (http://arxiv.org/pdf/2409.14513v2.pdf) - Quantile regression enables cost-effective and accurate MIAs on LLMs, highlighting critical privacy vulnerabilities introduced by fine-tuning on specialized datasets.
• Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning (http://arxiv.org/pdf/2409.18395v1.pdf) - Context-aware prompt tuning amplifies the repair success rate of LLMs for buffer overflow vulnerabilities from 15% to 63%, underscoring the importance of domain knowledge in prompt design.

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