Last Week in GAI Security Research - 01/20/25

Highlights from Last Week

• 😡 AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages
• 🧙 Gandalf the Red: Adaptive Security for LLMs
• 🧭 I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution
• 🗳 Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards

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😡 AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages (http://arxiv.org/pdf/2501.08284v2.pdf)

• The introduction of 15 multilingual datasets from the AfriHate project significantly advances hate speech detection across African languages, offering crucial resources for underrepresented languages.
• Performance of fine-tuned multilingual models like AfroXLMR-76L indicates better capability to handle low-resource settings, achieving a macro F1-score of 78.16 across hate speech detection tasks.
• Native speakers' involvement in collecting and annotating datasets ensures high-quality data and culturally relevant lexicons, which are pivotal in addressing socio-cultural nuances within hate speech content.

🧙 Gandalf the Red: Adaptive Security for LLMs (http://arxiv.org/pdf/2501.07927v1.pdf)

• Adaptive defenses can increase security by blocking up to 75% of attacks while managing the trade-off between security and usability in LLM applications.
• Red-teaming techniques, including crowd-sourced methods like the Gandalf platform, provide valuable insights into defense strategies by enabling the generation and classification of realistic and diverse attack scenarios.
• Defenses like prompt sanitization, input/output classifier integration, and session-based history offer a combination that effectively reduces the attack surface for LLM-based applications.

🧭 I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution (http://arxiv.org/pdf/2501.08165v1.pdf)

• Large language models (LLMs) such as GPT-4o and Gemini-1.5-p demonstrate significant promise in code authorship attribution with zero-shot and few-shot learning setups, achieving up to 92% accuracy in specific contexts, notably outperforming traditional methods.
• Despite high accuracy potential, these models face challenges in generalized applicability across different programming languages and large-scale datasets, with performance notably declining in complex prompting tasks and non-native languages like Java.
• Robustness against adversarial modifications shows variance, as LLMs can withstand certain adversarial attacks but require strategic adversarial-aware prompting to enhance reliability and performance, as seen with models like Gemini-1.5-p achieving 70% accuracy under adversarial conditions.

🗳 Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards (http://arxiv.org/pdf/2501.07493v1.pdf)

• Anonymous model responses can be de-anonymized with over 95% accuracy, posing significant security risks to voting-based leaderboards.
• Adversarial manipulation using a few thousand votes can significantly alter model rankings, demonstrating vulnerabilities in the current voting systems.
• Countermeasures such as CAPTCHA, rate limiting, and user authentication can effectively increase the cost of adversarial attacks, enhancing leaderboard security.

Other Interesting Research

• Computing Optimization-Based Prompt Injections Against Closed-Weights Models By Misusing a Fine-Tuning API (http://arxiv.org/pdf/2501.09798v1.pdf) - The study highlights the security risks of fine-tuning interfaces, with adversarial prompts leading to high success rates in manipulating LLM outputs.
• A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (http://arxiv.org/pdf/2501.09431v1.pdf) - Exploring advanced mitigation strategies can lead to more responsible LLMs, reducing privacy risks and improving alignment with human values.
• Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment (http://arxiv.org/pdf/2501.09620v1.pdf) - Causal reward modeling significantly enhances the alignment, trustworthiness, and fairness of LLMs by effectively mitigating spurious correlations and biases.
• Self-Instruct Few-Shot Jailbreaking: Decompose the Attack into Pattern and Behavior Learning (http://arxiv.org/pdf/2501.07959v1.pdf) - Exploring few-shot jailbreaking exposes critical vulnerabilities in large language models, highlighting the importance of robust defense mechanisms.
• Augmenting Smart Contract Decompiler Output through Fine-grained Dependency Analysis and LLM-facilitated Semantic Recovery (http://arxiv.org/pdf/2501.08670v1.pdf) - The research introduces SmartHalo, a framework that dramatically enhances smart contract decompilation and security analysis using advanced language models.
• CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation (http://arxiv.org/pdf/2501.08200v1.pdf) - The CWE VAL framework provides a novel approach for evaluating both functionality and security of LLM-generated code, revealing critical insights and advancements in code security assessments.
• Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack (http://arxiv.org/pdf/2501.08454v1.pdf) - Tag&Tab offers a novel, efficient approach to detecting pretraining data in large language models, leveraging high-entropy keywords for improved accuracy.
• Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities (http://arxiv.org/pdf/2501.07058v1.pdf) - Understanding and optimizing LLM utility in blockchain vulnerability detection could mitigate financial losses and improve smart contract security.
• ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving (http://arxiv.org/pdf/2501.08203v1.pdf) - ArithmAttack reveals varying noise tolerance levels among LLMs, with Llama3.1 emerging as the most robust in noisy contextual scenarios.
• Exploring Robustness of LLMs to Sociodemographically-Conditioned Paraphrasing (http://arxiv.org/pdf/2501.08276v1.pdf) - The study highlights how demographic-specific paraphrasing impacts LLM performance and robustness, revealing critical insights into linguistic diversity's influence on AI models.
• Text-Diffusion Red-Teaming of Large Language Models: Unveiling Harmful Behaviors with Proximity Constraints (http://arxiv.org/pdf/2501.08246v1.pdf) - DART's diffusion-based approach effectively identifies harmful language model behaviors with minimal prompt alterations, maintaining high success rates.
• Measuring the Robustness of Reference-Free Dialogue Evaluation Systems (http://arxiv.org/pdf/2501.06728v1.pdf) - The study underscores the critical need for robust dialogue evaluation systems that can effectively resist adversarial manipulations while remaining aligned with human judgment.

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