Last Week in GAI Security Research - 07/29/24

Highlights from Last Week

• 🔴 RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent
• 🩺 CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models
• ❤‍🩹 PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation
• 📚 Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs)
• 🖐🏻 LLMmap: Fingerprinting For Large Language Models 
• ⏳ From Sands to Mansions: Enabling Automatic Full-Life-Cycle Cyberattack Construction with LLM

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🔴 RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent (http://arxiv.org/pdf/2407.16667v1.pdf)

• RedAgent demonstrated a significant improvement in jailbreaking LLMs, achieving over a 90% success rate with less than five queries on average.
• The research revealed 60 severe vulnerabilities in LLM applications, highlighting the importance of context-aware jailbreak prompts in identifying and mitigating security risks.
• By leveraging a novel context-aware strategy, RedAgent efficiently exploited vulnerabilities in LLM applications, enhancing their security through actionable feedback communicated to developers.

🩺 CVE-LLM : Automatic vulnerability evaluation in medical device industry using large language models (http://arxiv.org/pdf/2407.14640v1.pdf)

• The integration of Large Language Models (LLMs) in the automatic vulnerability evaluation for medical devices indicates a significant advance in cybersecurity, enhancing detection, evaluation, and mitigation processes.
• LLMs trained with domain-specific data can surpass traditional methods in speed and accuracy for cybersecurity vulnerability assessments, potentially reducing the evaluation time from hours to seconds.
• Adopting a human-in-the-loop framework with LLMs for vulnerability evaluation in medical devices proposes a synergy that ensures both the efficiency of AI and the nuanced judgment of cybersecurity experts.

❤‍🩹 PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation (http://arxiv.org/pdf/2407.17788v1.pdf)

• PenHeal improves the identification of cybersecurity vulnerabilities and automates their remediation, increasing coverage by 31%, improving effectiveness by 32%, and reducing costs by 46%.
• The integration of LLMs in PenHeal enhances penetration testing and remediation processes with limited human intervention, establishing a more efficient cybersecurity framework.
• PenHeal's use of Counterfactual Prompting and a two-module approach for penetration testing and remediation respectively offers a novel method in cybersecurity, demonstrating the significant potential of LLMs in enhancing security practices.

📚 Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs) (http://arxiv.org/pdf/2407.14937v1.pdf)

• Red-teaming is crucial for assessing the safety and identifying vulnerabilities in large language models (LLMs), highlighting the unpredictable nature and potential harms such as misinformation and biased outputs.
• The taxonomy of red-teaming attacks reveals a wide range of strategies targeting the LLMs during various stages of their lifecycle, from development to deployment, emphasizing the need for comprehensive defense mechanisms.
• Operationalizing threat models for red-teaming involves systematizing attacks and defenses, which is instrumental in navigating the development of secure, resilient applications using LLMs.

🖐🏻 LLMmap: Fingerprinting For Large Language Models (http://arxiv.org/pdf/2407.15847v2.pdf)

• LLMmap achieved a 95.2% accuracy rate in classifying 32 out of 40 Large Language Models (LLMs) using a closed-set classifier approach.
• For open-set fingerprinting, LLMmap was able to achieve an average accuracy of 90%, demonstrating its ability to identify LLMs even when they were not included in the training set.
• The effectiveness of LLMmap's querying strategy significantly depends on the selection of queries, with the first three achieving an average accuracy of 90%, and accuracy leveling off after eight queries to 95%.

⏳ From Sands to Mansions: Enabling Automatic Full-Life-Cycle Cyberattack Construction with LLM (http://arxiv.org/pdf/2407.16928v1.pdf)

• AURORA, an automated end-to-end cyberattack construction system, was able to construct and evaluate 20 full-life-cycle cyberattacks, demonstrating the capability to automate the planning, building, and execution of complex multi-step cyberattacks.
• The construction of full-life-cycle cyberattacks by leveraging Large Language Models (LLMs) and a multi-agent system shows a significant reduction in manual effort and time, with attacks being assembled in minutes without human intervention.
• The attack procedure knowledge graph developed as part of AURORA covers 74.5% of attack techniques and connects 70.6% of those techniques into executable plans, highlighting the framework's effectiveness in emulating real-world cyber attacks.

Other Interesting Research

• Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context (http://arxiv.org/pdf/2407.14644v1.pdf) - Research uncovers the effectiveness of situation-driven adversarial prompt attacks on LLMs, underlining significant security vulnerabilities and the potential for generating harmful responses with targeted, coherent prompts.
• Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts (http://arxiv.org/pdf/2407.15050v1.pdf) - Arondight's red teaming framework exposes significant vulnerabilities in VLMs, highlighting the urgent need for enhanced security and ethical guidelines in multi-modal AI applications.
• Figure it Out: Analyzing-based Jailbreak Attack on Large Language Models (http://arxiv.org/pdf/2407.16205v1.pdf) - ABJ reveals significant vulnerabilities in LLMs' defense mechanisms, demonstrating over 94% success in bypassing safety protocols and underscoring the urgency for enhanced security measures.
• PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing (http://arxiv.org/pdf/2407.16318v1.pdf) - PrimeGuard revolutionizes LLM safety with high compliance and helpfulness through tuning-free routing, dramatically reducing attack success rates.
• Exploring Scaling Trends in LLM Robustness (http://arxiv.org/pdf/2407.18213v1.pdf) - Scaling language models improves capabilities and robustness to adversarial attacks but introduces higher training complexities and costs.
• The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models (http://arxiv.org/pdf/2407.17915v1.pdf) - The research underscores the critical vulnerabilities in large language models due to jailbreak function calling, highlighting the urgent need for enhanced security measures.
• Course-Correction: Safety Alignment Using Synthetic Preferences (http://arxiv.org/pdf/2407.16637v1.pdf) - Synthetic preference learning significantly improves LLMs' course-correction abilities, making them safer without affecting general performance.
• Targeted Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs (http://arxiv.org/pdf/2407.15549v1.pdf) - Adversarial training specifically targeted enhances LLM safety without notable compromise on function, but relearning vulnerabilities persist, suggesting room for advance.
• Imposter.AI: Adversarial Attacks with Hidden Intentions towards Aligned Large Language Models (http://arxiv.org/pdf/2407.15399v1.pdf) - Imposter.AI reveals the covert vulnerability of Large Language Models to nuanced adversarial tactics, underscoring the critical need for advanced defense mechanisms.
• SCoPE: Evaluating LLMs for Software Vulnerability Detection (http://arxiv.org/pdf/2407.14372v1.pdf) - SCoPE's data processing framework highlights the intricate challenges and modest successes in enhancing LLMs' effectiveness for software vulnerability detection in C/C++.
• Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection (http://arxiv.org/pdf/2407.16235v1.pdf) - LLMs significantly boost vulnerability detection capabilities, especially when combined with SAST tools, albeit with variations in effectiveness across different programming languages.
• Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization (http://arxiv.org/pdf/2407.14573v1.pdf) - Research unveils critical vulnerabilities in AI-driven financial systems through backdoor attacks and proposes robust Bayesian Optimization for enhancing stock market predictions.
• Can Large Language Models Automatically Jailbreak GPT-4V? (http://arxiv.org/pdf/2407.16686v1.pdf) - AutoJailbreak's groundbreaking jailbreak technique efficiently breaches GPT-4V security with a 95.3% success rate, underscoring urgent security enhancement needs for Multimodal Large Language Models.
• Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective (http://arxiv.org/pdf/2407.16997v1.pdf) - Targeted unlearning via a causal intervention framework offers a robust and efficient way to selectively forget information while preserving the utility of language models.
• The Shadow of Fraud: The Emerging Danger of AI-powered Social Engineering and its Possible Cure (http://arxiv.org/pdf/2407.15912v1.pdf) - AI-driven social engineering attacks present a notable financial risk, underscoring the need for advanced defensive measures.
• Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era? (http://arxiv.org/pdf/2407.17870v1.pdf) - Research highlights vulnerabilities in digital forensics due to neural text generators, emphasizing the need for advanced strategies to combat co-authored threats and improve authorship attribution accuracy.
• Revisiting the Robust Alignment of Circuit Breakers (http://arxiv.org/pdf/2407.15902v1.pdf) - Revealing critical vulnerabilities in circuit breaker models for LLMs, this study underscores the complexity of ensuring AI robustness against adversarial attacks.
• Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection (http://arxiv.org/pdf/2407.14838v1.pdf) - RAG-LLMs offer a significant advancement in smart contract auditing efficiency, yet underscore the irreplaceable value of human oversight.
• Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks (http://arxiv.org/pdf/2407.16166v1.pdf) - Large language models can generate synthetic clinical notes that balance data privacy with utility, paving the way for improved data sharing practices in biomedical research.
• Watermark Smoothing Attacks against Language Models (http://arxiv.org/pdf/2407.14206v1.pdf) - Watermark smoothing attacks reveal vulnerabilities in the robustness of watermarking techniques for LLMs, highlighting an effective strategy for generating undetectable, high-quality text.
• Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? (http://arxiv.org/pdf/2407.17417v1.pdf) - Watermarking LLMs curbs copyright infringement and improves copyright text detection while complicating inference attacks.

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