Last Week in GAI Security Research - 04/21/25

Highlights from Last Week

• 👥 Characterizing LLM-driven Social Network: The Chirper.ai Case
• 😎 On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks
• 🐍 Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy? 
• ⚠️ Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors 
• 🎛 Progent: Programmable Privilege Control for LLM Agents
• 🐚 Can LLMs handle WebShell detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework

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👥 Characterizing LLM-driven Social Network: The Chirper.ai Case (http://arxiv.org/pdf/2504.10286v1.pdf)

• Chirper.ai's AI-generated posts show a disproportionally higher prevalence of emojis and hallucinated mentions compared to human-driven networks like Mastodon.
• LLM agents on Chirper.ai have a notably higher rate of self-disclosure in their posts, indicating a potential risk of oversharing personal information compared to other networks.
• Despite the sophistication of LLM agents, distinguishing Chirper-generated text from human-created content remains challenging, with AUROC scores under 0.75 for the best detection methods.

😎 On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks (http://arxiv.org/pdf/2504.13209v1.pdf)

• A significant 93.3% of participants engaged in high-risk behaviors such as clicking on phishing links, highlighting the vulnerability in digital interactions facilitated by multimodal large language models and AR technologies.
• The study's results indicated that trust-based interactions were highly effective, with 85% of targets accepting malicious emails, demonstrating the potential of hyper-personalized attacks using multimodal LLMs.
• Despite seamless interaction and emotional adaptability in AR environments, a gap in authenticity was perceived, showing around 20% of users experiencing fragmented social profiles and necessitating robust system integrations.

🐍 Detecting Malicious Source Code in PyPI Packages with LLMs: Does RAG Come in Handy? (http://arxiv.org/pdf/2504.13769v1.pdf)

• Large Language Models (LLMs) fine-tuned with YARA rules and GitHub Security Advisories achieve a maximum classification accuracy of 97% for malware detection in PyPI packages, demonstrating their potential in cybersecurity applications.
• Retrieval-Augmented LLMs (RAG) exhibit limited effectiveness with mediocre accuracy in few-shot learning for distinguishing between benign and malicious PyPI packages, necessitating improvements to knowledge bases and retrieval models.
• The study reveals that the fine-tuning of LLaMA-3.1-8B models significantly improves classification performance, increasing the precision score to 0.98 and achieving an F1-score greater than 0.95 for distinguishing between benign and malicious software packages.

⚠️ Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors (http://arxiv.org/pdf/2504.10713v1.pdf)

• Accuracy in generating CVSS vectors from CVE descriptions can reach up to 0.98 using the best-performing model, Gemma3, highlighting the potential use of Large Language Models (LLMs) in CVSS scoring.
• There is a significant 38% increase in published Common Vulnerabilities and Exposures (CVEs) expected between 2023 and 2024, emphasizing the need for automation in cybersecurity risk prioritization.
• Embedding-based methods combined with LLMs outperform traditional models in handling subjective CVSS components like confidentiality and integrity, suggesting a hybrid approach for improved vulnerability assessments.

🎛 Progent: Programmable Privilege Control for LLM Agents (http://arxiv.org/pdf/2504.11703v1.pdf)

• Progent reduces the attack success rate from 41.2% to as low as 2.2%, demonstrating a significant improvement in security by enforcing the principle of least privilege in LLM agents.
• The implementation of a JSON-based policy language allows for minimal code changes, with developers only needing to modify approximately 10 lines of code for integration into pre-existing systems.
• Progent's framework leads to an improved defense against adaptive attacks, maintaining high utility scores while effectively minimizing security risks associated with unauthorized tool use.

🐚 Can LLMs handle WebShell detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework (http://arxiv.org/pdf/2504.13811v1.pdf)

• Larger language models like GPT-4 achieve a near-perfect precision in WebShell detection, but they struggle with recall, resulting in a higher overall accuracy than smaller models.
• The Behavioral Function-Aware Detection Framework improves average F1 scores of language models by 13.82% by utilizing techniques like Critical Function Filtering and Context-Aware Code Extraction.
• WebShell attacks exploit a range of critical functions such as execution, communication, and obfuscation, which are detected more effectively through weighted behavioral function profiling that better distinguishes between benign and malicious scripts.

Other Interesting Research

• DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks (http://arxiv.org/pdf/2504.11358v1.pdf) - DataSentinel uses a game-theoretic approach to effectively reduce false positives and negatives in detecting prompt injection attacks, outperforming existing methods.
• Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails (http://arxiv.org/pdf/2504.11168v2.pdf) - The study highlights critical vulnerabilities in language model guardrails, demonstrating that character injection and adversarial evasion strategies can successfully bypass detection, emphasizing the need for improved defense mechanisms.
• StruPhantom: Evolutionary Injection Attacks on Black-Box Tabular Agents Powered by Large Language Models (http://arxiv.org/pdf/2504.09841v1.pdf) - StruPhantom showcases a 50% higher success rate in exploiting vulnerabilities of LLM-powered tabular agents through evolutionary injection attacks, revealing the urgent need for robust security measures.
• ControlNET: A Firewall for RAG-based LLM System (http://arxiv.org/pdf/2504.09593v2.pdf) - ControlNet effectively addresses privacy and security vulnerabilities in RAG-based LLM systems with high accuracy and minimal performance trade-offs.
• You've Changed: Detecting Modification of Black-Box Large Language Models (http://arxiv.org/pdf/2504.12335v1.pdf) - This research underscores the importance of detecting subtle changes in LLMs using statistical analysis on linguistic features, assisting developers in understanding variations in model behavior without intensive benchmarking.
• Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding (http://arxiv.org/pdf/2504.10465v1.pdf) - Pixel-SAIL leverages a novel, streamlined approach with a single transformer model to excel in precise pixel-level understanding and segmentation tasks, outperforming complex multi-modal models by integrating learnable upsampling and sophisticated prompt strategies.
• Concept Enhancement Engineering: A Lightweight and Efficient Robust Defense Against Jailbreak Attacks in Embodied AI (http://arxiv.org/pdf/2504.13201v1.pdf) - Concept Enhanced Engineering (CEE) provides a lightweight, efficient defense mechanism against jailbreak attacks in embodied AI systems, ensuring enhanced safety without compromising performance.
• The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections (http://arxiv.org/pdf/2504.11281v1.pdf) - This research reveals critical vulnerabilities in GUI agents to privacy attacks, emphasizing the need for improved design and oversight.
• Nondeterministic Polynomial-time Problem Challenge: An Ever-Scaling Reasoning Benchmark for LLMs (http://arxiv.org/pdf/2504.11239v1.pdf) - The research showcases groundbreaking improvements in reasoning accuracy and resilience of LLMs, setting a new paradigm with uncrushable, scalable benchmarking for artificial intelligence tasks.
• Energy-Based Reward Models for Robust Language Model Alignment (http://arxiv.org/pdf/2504.13134v1.pdf) - Energy-Based Reward Model (EBRM) vastly enhances robustness and efficiency in reward model alignment tasks, especially in safety-critical contexts.
• Exploring Backdoor Attack and Defense for LLM-empowered Recommendations (http://arxiv.org/pdf/2504.11182v1.pdf) - The P-Scanner method offers a robust solution to defend against backdoor attacks in large language model-assisted recommender systems, ensuring system trustworthiness and recommendation accuracy.
• BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-box Large Language Models (http://arxiv.org/pdf/2504.13775v1.pdf) - BadApex employs an adaptive mechanism to refine prompts, generating high-quality and semantically consistent poisoned texts that maintain a high attack success rate despite defensive measures.
• Propaganda via AI? A Study on Semantic Backdoors in Large Language Models (http://arxiv.org/pdf/2504.12344v1.pdf) - Semantic backdoors expose significant vulnerabilities in language models, necessitating robust detection frameworks like RA VEN for high-level semantic auditing.
• GraphAttack: Exploiting Representational Blindspots in LLM Safety Mechanisms (http://arxiv.org/pdf/2504.13052v1.pdf) - GraphAttack exploits deep semantic vulnerabilities in LLMs, achieving a high bypass success rate of 87% against their safety mechanisms.
• DETAM: Defending LLMs Against Jailbreak Attacks via Targeted Attention Modification (http://arxiv.org/pdf/2504.13562v1.pdf) - The introduction of DETAM as a finetuning-free defense method for LLMs has demonstrated superior effectiveness in thwarting jailbreak attacks by strategically reallocating model attention, all while preserving utility and lowering false rejection rates.
• The Structural Safety Generalization Problem (http://arxiv.org/pdf/2504.09712v1.pdf) - The research uncovers promising solutions for enhancing AI structural safety by examining language model vulnerabilities and proposing novel defense mechanisms like the Structure Rewriting Guardrail.
• Making Acoustic Side-Channel Attacks on Noisy Keyboards Viable with LLM-Assisted Spectrograms' "Typo" Correction (http://arxiv.org/pdf/2504.11622v1.pdf) - The study unveils the efficacy of combining LLMs with transformer models to mitigate errors in Acoustic Side-Channel Attacks, especially in environments with high noise levels, marking a significant advancement in cybersecurity measures.
• Mitigating Many-Shot Jailbreaking (http://arxiv.org/pdf/2504.09604v1.pdf) - Input sanitization and fine-tuning have proven key in countering many-shot jailbreaks, maintaining model safety and performance.
• ZeroSumEval: Scaling LLM Evaluation with Inter-Model Competition (http://arxiv.org/pdf/2504.12562v1.pdf) - A competition framework exposed creativity and planning weaknesses across large language models, calling for improvements in diversity and cost-efficiency of current benchmark evaluation strategies.
• LLM Unlearning Reveals a Stronger-Than-Expected Coreset Effect in Current Benchmarks (http://arxiv.org/pdf/2504.10185v2.pdf) - The discovery of the 'coreset effect' highlights that small, strategically selected data subsets can maintain unlearning efficacy and robustness, reducing the need for full dataset handling.
• Large Language Models for Validating Network Protocol Parsers (http://arxiv.org/pdf/2504.13515v1.pdf) - PARVAL uses LLMs to enhance protocol parser validation, extracting logic with high precision and uncovering critical bugs.
• Everything You Wanted to Know About LLM-based Vulnerability Detection But Were Afraid to Ask (http://arxiv.org/pdf/2504.13474v1.pdf) - Applying context-rich evaluations to LLMs exposes underestimated capabilities in software vulnerability detection, challenging prior assumptions of their ineffectiveness without context.
• An Investigation of Large Language Models and Their Vulnerabilities in Spam Detection (http://arxiv.org/pdf/2504.09776v1.pdf) - Large Language Models are promising for spam detection but are susceptible to adversarial attacks and lack generalization across varied datasets.
• Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design (http://arxiv.org/pdf/2504.10112v1.pdf) - Innovative LLM-driven testbeds are diversifying penetration testing with new benchmarks and economic considerations, despite challenges in error tracking and real-world scenario representation.
• ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges (http://arxiv.org/pdf/2504.12143v1.pdf) - The paper highlights the transformative potential of using large language models for automated and efficient Cyber Range configuration, paving the way for adaptive and scalable cybersecurity training solutions.

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