Last Week in GAI Security Research - 03/17/25

Highlights from Last Week

• 📜 A Survey on Trustworthy LLM Agents: Threats and Countermeasures
• 🗡️ KNighter: Transforming Static Analysis with LLM-Synthesized Checkers
• 🎛 Control Flow-Augmented Decompiler based on Large Language Model
• 🐡 Large Language Models-Aided Program Debloating 
• 🚂 MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming 
• 🔐 Privacy Auditing of Large Language Models

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📜 A Survey on Trustworthy LLM Agents: Threats and Countermeasures (http://arxiv.org/pdf/2503.09648v1.pdf)

• Approximately 96% of examined LLM ecosystems were found to be vulnerable due to the expanded attack surface introduced by integrating additional modules and functionalities.
• Integration of Large Language Models (LLMs) into multi-agent systems revealed inherent vulnerabilities, with 116 identified vulnerabilities spanning privacy, fairness, truthfulness, and robustness dimensions.
• TrustAgent framework proposes a comprehensive methodology to address intrinsic and extrinsic trustworthiness issues through modular, multi-dimensional connotations enabling precise evaluation, defense mechanisms, and future research guidance.

🗡️ KNighter: Transforming Static Analysis with LLM-Synthesized Checkers (http://arxiv.org/pdf/2503.09002v1.pdf)

• KNighter identified 70 new bugs within the Linux kernel, of which 56 were confirmed, and 41 were fixed, including 11 that were assigned CVE numbers.
• The approach reduces false positive rates to 35% and uncovers diverse bug patterns such as Null-Pointer-Dereference, Integer-Overflow, and Use-After-Free bugs.
• KNighter's multi-stage synthesis process minimizes the computational costs and constraints typically associated with using large language models for static code analysis.

🎛 Control Flow-Augmented Decompiler based on Large Language Model (http://arxiv.org/pdf/2503.07215v1.pdf)

• In evaluations, models using control flow-augmented decompilation show superior performance with readability rates as high as 41.51% for complex binaries on 64-bit datasets, outperforming models like GPT-4o and Deepseek-Coder.
• In experiments on decompiled code similarity, CFADecLLM demonstrates significant improvements in both execution accuracy and readability by incorporating control flow information, leading to more structured and logically consistent code.
• Across multiple datasets, the proposed method achieves high re-executability and semantic similarity scores, showcasing its effectiveness in generating human-readable high-level code from assembly instructions.

🐡 Large Language Models-Aided Program Debloating (http://arxiv.org/pdf/2503.08969v1.pdf)

• A debloating approach utilizing large language models (LLMs) achieves a 95.5% test case pass rate while reducing program size by 42.5% and cutting vulnerabilities by 79.1%.
• Introducing LLMs into the debloating process enhances documentation-guided test augmentation, resulting in higher code coverage of up to 76% compared to traditional methods.
• The LEADER framework demonstrates superior debloating efficacy, maintaining functionality correctness at 96% while reducing total introduced vulnerabilities to 76.1, significantly improving over previous methods.

🚂 MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming (http://arxiv.org/pdf/2503.06253v1.pdf)

• MAD-MAX achieves a 97% success rate in executing malicious goals on the GPT-4o model, significantly outperforming TAP with fewer required queries (10.9 average compared to 23.3).
• The modular attack library of MAD-MAX allows for diverse and adaptable malicious attack styles, enhancing its effectiveness against large language models compared to traditional methods.
• Introducing attack style diversification and a similarity filter stage in MAD-MAX dramatically reduces the computational cost and improve overall attack efficiency and success rates.

🔐 Privacy Auditing of Large Language Models (http://arxiv.org/pdf/2503.06808v1.pdf)

• Crafting canaries with a high True Positive Rate (TPR) of 49.6% and False Positive Rate (FPR) of 1% significantly outperforms previous methods that had a TPR of only 4.2% at the same FPR, reflecting a major advancement in privacy auditing for language models.
• Extensive evaluations demonstrate that incorporating stronger privacy audits into non-privately trained large language models helps in accurately identifying and understanding privacy leakage, with new canary designs being pivotal for effective detection.
• The introduction of easy-to-memorize canaries is confirmed to enhance the privacy audit's performance by providing more reliable privacy leakage estimates, particularly under black-box settings and in the presence of distribution shifts.

Other Interesting Research

• Advancing AI Negotiations: New Theory and Evidence from a Large-Scale Autonomous Negotiations Competition (http://arxiv.org/pdf/2503.06416v1.pdf) - The study highlights how technology-enhanced negotiation strategies substantially outperform traditional approaches.
• Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States (http://arxiv.org/pdf/2503.09066v1.pdf) - Understanding the mechanisms of latent subspace manipulations in LLMs offers a path to bolster AI security against prompt-injection exploits.
• CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data (http://arxiv.org/pdf/2503.09334v1.pdf) - A novel dataset, CyberLLMInstruct, significantly improves language model accuracy in cybersecurity while revealing vulnerabilities in safety during adversarial testing.
• ASIDE: Architectural Separation of Instructions and Data in Language Models (http://arxiv.org/pdf/2503.10566v1.pdf) - ASIDE architecture enhances language model robustness by significantly improving instruction-data separation and reducing vulnerability to prompt injections.
• JBFuzz: Jailbreaking LLMs Efficiently and Effectively Using Fuzzing (http://arxiv.org/pdf/2503.08990v1.pdf) - JBFuzz dramatically streamlines the process of exposing vulnerabilities in large language models, offering a high success rate in a fraction of the time compared to traditional methods.
• Utilizing Jailbreak Probability to Attack and Safeguard Multimodal LLMs (http://arxiv.org/pdf/2503.06989v1.pdf) - This paper proposes innovative methods to mitigate the risk of jailbreak attacks in multimodal large language models, achieving enhanced safety and reduced attack success rates through strategic probability-based defenses.
• Siege: Autonomous Multi-Turn Jailbreaking of Large Language Models with Tree Search (http://arxiv.org/pdf/2503.10619v1.pdf) - This study successfully exploits multi-turn dialogue vulnerabilities in advanced language models, achieving high attack success rates with fewer queries.
• SafeSpeech: A Comprehensive and Interactive Tool for Analysing Sexist and Abusive Language in Conversations (http://arxiv.org/pdf/2503.06534v1.pdf) - SafeSpeech leverages advanced AI to detect, summarize, and analyze sexism and abuse in digital conversations, outperforming traditional models in contextual toxicity detection.
• CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection (http://arxiv.org/pdf/2503.09433v1.pdf) - The CASTLE Benchmark provides a comprehensive evaluation framework for comparing the effectiveness of vulnerability detection tools, revealing high performance by ESBMC and the potential of strategic tool combinations.
• Interpreting the Repeated Token Phenomenon in Large Language Models (http://arxiv.org/pdf/2503.08908v1.pdf) - The study explores the vulnerability of Large Language Models to attention sinks, which cause issues with repeated token divergence, and presents effective patching methods that minimally impact model performance.
• Life-Cycle Routing Vulnerabilities of LLM Router (http://arxiv.org/pdf/2503.08704v1.pdf) - The study underscores that DNN-based routers are significantly vulnerable to both adversarial and backdoor attacks, while training-free routers show enhanced robustness due to their parameter-free nature.
• Can Small Language Models Reliably Resist Jailbreak Attacks? A Comprehensive Evaluation (http://arxiv.org/pdf/2503.06519v1.pdf) - Small language models, crucial for low-resource environments, are significantly vulnerable to jailbreak attacks, challenging current security norms.
• Safety Guardrails for LLM-Enabled Robots (http://arxiv.org/pdf/2503.07885v1.pdf) - OBOGUARD effectively mitigates safety risks in LLM-enabled robots, drastically reducing unsafe behavior through an innovative two-stage guardrail system.
• Dialogue Injection Attack: Jailbreaking LLMs through Context Manipulation (http://arxiv.org/pdf/2503.08195v1.pdf) - The study highlights the persistent security vulnerabilities in large language models, particularly when faced with sophisticated dialogue injection attacks that manipulate historical contexts to bypass safety measures.
• Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Models (http://arxiv.org/pdf/2503.06269v1.pdf) - Subspace techniques offer a robust and efficient approach to mitigate adversarial attacks on sophisticated language models, improving their interpretability and safety.
• Reinforced Diffuser for Red Teaming Large Vision-Language Models (http://arxiv.org/pdf/2503.06223v1.pdf) - Red Team Diffuser's framework reveals serious alignment failings in VLMs, allowing high occurrences of toxic continuations, especially when leveraging adversarial prompts.
• Prompt Inference Attack on Distributed Large Language Model Inference Frameworks (http://arxiv.org/pdf/2503.09291v1.pdf) - The study highlights critical privacy risks in distributed large language model inference frameworks due to prompt inference attacks.
• Prompt Inversion Attack against Collaborative Inference of Large Language Models (http://arxiv.org/pdf/2503.09022v2.pdf) - The study exposes significant privacy risks in collaborative Large Language Model inference, offering a highly effective prompt inversion attack that demonstrates the potential for sensitive data recovery.
• TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors (http://arxiv.org/pdf/2503.08708v2.pdf) - The study sheds light on the challenges of creating evading attacks for AI Text Detectors that retain text quality while minimizing computational costs.
• PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models (http://arxiv.org/pdf/2503.07697v1.pdf) - The PoisonedParrot attack exemplifies the persistent challenge of securing LLMs against data poisoning strategies that embed copyrighted content.
• Enhancing NLP Robustness and Generalization through LLM-Generated Contrast Sets: A Scalable Framework for Systematic Evaluation and Adversarial Training (http://arxiv.org/pdf/2503.06648v1.pdf) - Adversarial training enhances NLP model robustness but faces scalability challenges in generating diverse contrast sets.
• Backtracking for Safety (http://arxiv.org/pdf/2503.08919v1.pdf) - BSAFE backtracking efficiently enhances language model safety by correcting mistakes without the need to discard generated content.
• Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies (http://arxiv.org/pdf/2503.07306v1.pdf) - Chinese Medical LLMs face substantial challenges in accuracy and reasoning, but proposed optimization strategies offer promising pathways for enhancement.
• CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation (http://arxiv.org/pdf/2503.06950v1.pdf) - Innovative CtrlRAG attack reveals significant vulnerabilities in Retrieval-Augmented Generation systems by manipulating emotional tones and factual accuracy in responses.

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