Defaince

🚀 Unveiling the Best-of-N Jailbreaking Technique in AI! 🔍 Exciting new research introduces Best-of-N (BoN) Jailbreaking, a groundbreaking black-box algorithm designed to exploit vulnerabilities in frontier AI systems across multiple modalities. ❓ What’s the research about? - BoN Jailbreaking effectively jailbreaks advanced AI models by sampling variations of prompts through simple augmentations like random shuffling and capitalization. - The technique has achieved impressive attack success rates (ASRs) of 89% on GPT-4o and 78% on Claude 3.5 Sonnet after sampling 10,000 augmented prompts. ➡️ Why does it matter? - This research highlights the inherent vulnerabilities in AI systems, revealing that even minor changes to inputs can lead to harmful outputs. - BoN Jailbreaking is not just limited to text; it also effectively targets vision and audio language models, demonstrating its versatility. 🛡️ Implications for AI Security: - The findings underscore the need for enhanced defenses against such attacks, as BoN can circumvent state-of-the-art open-source protections. - Understanding these vulnerabilities is crucial for developing more robust AI systems that align with human values. 📊 This research not only advances our knowledge of AI security but also calls for urgent discussions on how to safeguard these powerful technologies. 🔗 Read the full paper here: https://lnkd.in/dUV8gfM7 Let’s work together to strengthen AI security! 💡🔒#AI #Cybersecurity #MachineLearning #Research #Jailbreaking #AIsecurity #Innovation #BoNJailbreaking

🚀 AttentionBreaker: Unmasking Vulnerabilities in LLMs 🔍 Discover the new study that explores the vulnerabilities of Large Language Models (LLMs) to bit-flip attacks, a critical concern as these models become integral to mission-critical applications. ❓ What's the paper about? -  Large Language Models (LLMs) are transforming natural language processing. -  Bit-flip attacks (BFAs) can compromise these models by targeting memory parameters. -  AttentionBreaker is introduced to efficiently identify critical parameters for BFAs. ➡️ Why does it matter? - Understanding vulnerabilities is crucial for maintaining the integrity of AI systems. - Just three bit-flips can lead to catastrophic performance drops in LLMs. 🛡️ What It Means for AI Security? - Enhanced defenses against BFAs are essential. - AttentionBreaker allows for better identification of critical parameters. 📊 This research improves both security measures and explainability in AI models. 🔗 Paper link: https://lnkd.in/dHSXH_4Z Let’s advance AI security together! 💡🔒 #AI #Cybersecurity #MachineLearning #LLM #Research #BitFlipAttacks #AIsecurity #Innovation

🌟 Live from the Forbes Tech Summit in Budapest! 🌟 We are thrilled to announce that our co-founder Silur Endre Abraham has been invited to co-host this prestigious event, given his expertise in cybersecurity and his influential role within the Hungarian tech landscape. Stay tuned for insights and highlights from the summit as we explore the future of technology together! #ForbesTechSummit #Cybersecurity #Innovation #HungaryTech

🚀 Exciting News from Defaince! 🚀 We are thrilled to announce that our founders have been invited to join two prestigious AI Security centered Working Groups at the EU Commission's Futurium initiative! These groups - under the AI Office - include: 1️⃣ Technical Risk Mitigation for Systemic Risks - Technical Research Working Group; 2️⃣ Risk mitigation for providers of general-purpose AI models - Technical Research Working Group; 👀 Futurium is an open initiative launched by the European Commission aimed at encouraging collaboration among EU citizens, stakeholders in the tech ecosystem and EU policymakers. 📕 While some discussion groups are publicly open, the Working Groups under the AI Office are specifically tailored for experts to help draft and influence EU policies in the AI field. 💡 Through this initiative, the EU Commission is dedicated to creating comprehensive frameworks that ensure AI technologies are safe, ethical, and aligned with fundamental rights. 🌍 Why This Matters for Defaince: + Collaboration: Directly engaging with policymakers and other industry leaders allows us to shape the landscape of AI security, ensuring that our innovations align with regulatory frameworks and our users' needs. + Innovation: Being part of these working groups means that we can share our insights and learn from others, driving forward-thinking solutions that enhance security in an increasingly digital world. + Impact: We are committed to making a difference in AI security, and this enables us to advocate for best practices and ethical standards we believe in directly to policymakers. ✨ 🔐 We look forward to this new journey and humbly hope our insights and learnings will help build a safer digital future for our fellow European netizens! #AI #Security #Innovation #Startups #EUCommission #Futurium #Collaboration

🚀 New exciting advancements in AI security are on the horizon! Recent research has introduced a groundbreaking meta-unlearning methodology designed to tackle the challenges of unlearning harmful or copyrighted concepts from pretrained diffusion models (DMs). As the landscape of content generation evolves rapidly, ensuring that our models remain secure and compliant is more critical than ever. 🔍 What’s the Problem? Even when DMs are unlearned effectively, they can still fall victim to malicious finetuning, allowing unlearned concepts to creep back in. For instance, a benign concept like “skin” could be linked to a harmful concept like “nudity,” making it easier for models to inadvertently relearn what they shouldn’t. 💡 The Solution: Enter meta-unlearning! This innovative approach not only maintains the integrity of unlearned DMs but also introduces a self-destruct mechanism for visually related concepts if a model is subjected to malicious finetuning. Think of it as a security alarm for your AI—if it senses something amiss, it activates to protect the valuable information it holds. 🔗 Curious to learn more? https://lnkd.in/dQ3qiC4g #AI #MachineLearning #DataSecurity #Research #Innovation

🚨 New Research Alert! 🚨 👉 In a recent study, researchers have once again highlighted the vulnerabilities that arise from heavily relying on artificial intelligence in network-based intrusion detection systems (NIDS). While it’s challenging to produce adversarial examples in this domain due to data inter-dependencies, attackers have developed multiple methods to bypass state-of-the-art anomaly detectors. 👀 This research underscores the importance of understanding the security landscape surrounding AI systems, especially as they become more integrated into our cybersecurity frameworks. As AI continues to evolve, so do the tactics employed by malicious actors. 🔍 Key Takeaways: - Relying on AI in NIDS introduces new vulnerabilities. - Attackers are finding ways to bypass advanced detection systems. - Continuous research and adaptation are essential for effective cybersecurity. 🔗 Link to the research paper: https://lnkd.in/eCspAUXd At Defaince, we are committed to staying ahead of these threats by continuously innovating our AI model security platform. 🔐 What measures are you taking to secure your AI-driven systems? Share your thoughts below! #CyberSecurity #AI #IntrusionDetection #Vulnerabilities #Research #Innovation #NetworkSecurity #AIsecurity

🚀 Exciting advancements in AI security! Researchers have introduced a groundbreaking poisoning technique that enhances conventional backdoor attacks by leveraging knowledge distillation. 👀 In this innovative approach, a smaller model—easier to exploit—acts as a teacher model, transferring its backdoor vulnerabilities to a larger student model (the victim). This method contrasts with traditional knowledge distillation, where a larger model is simplified using a smaller one. This research could significantly impact how we think about AI security and the protection of our systems! 🔐✨ 🔒 Is your AI system truly secure?  🔗 Read more here: https://lnkd.in/dv5QFH3N #AI #Cybersecurity #MachineLearning #Innovation #Research #TechForGood

🚨 New attack vector found in RAG systems! Retrieval-Augmented Generation (RAG) is a powerful technique that allows LLMs to access external knowledge for more accurate and up-to-date responses. But with great power comes great responsibility - and vulnerability. ▶ Researchers have identified a new attack called "PoisonedRAG" that targets the knowledge database in RAG systems. By injecting a few carefully crafted malicious texts, an attacker can manipulate the LLM to generate a specific answer for a chosen question. In experiments, PoisonedRAG achieved an impressive 90% success rate with just five malicious texts injected into a database of millions. ⚠ Current defenses are inadequate against PoisonedRAG, highlighting the urgent need for stronger security measures in RAG systems. It's essential to proactively identify and address these vulnerabilities before they can be exploited by malicious actors. 📕 Read more here: https://lnkd.in/dM-dwN_p #RAG #AISecurity #SecurityforAI #AIforgood #PoisonedRAG

Trendsetters of the Watermarking World 🎨 👀 What? TikTok has announced that it will automatically label AI-generated content created on other platforms, such as OpenAI's DALL·E 3, using a technology called Content Credentials from the Coalition for Content Provenance and Authenticity (C2PA). This technology attaches specific metadata to content, allowing the platform to instantly recognize and label AI-generated content. ➡ The company views this move as an additional measure to ensure AI-generated content is properly labeled and to eventually alleviate pressure on creators and end-users. 🤔 Did you know that the EU AI Act extends watermarking regulations to Europe, requiring either platform-level or end-user level enforcement? 📕 Read more here: https://lnkd.in/dgk8hXYM #AIWatermarking #Watermarking #AI #EUAIAct

⬛ AI Black Boxes just got a Little Less Mysterious ⬛ 👀 What? A new research paper published by Anthropic, a leading AI company, aims to demystify the "black box" phenomenon of AI's algorithmic behavior. The paper focuses on understanding why Anthropic's AI chatbot, Claude, prioritizes certain topics over others. The researchers used a process called "dictionary learning" to identify which parts of Claude's neural network corresponded to specific concepts, thereby gaining insight into the model's decision-making process. One notable discovery was a feature associated with the Golden Gate Bridge, which, when activated, indicated that Claude was contemplating the landmark. ➡ This research represents a significant step in the field of AI interpretation, which seeks to trace the decision-making path of AI systems to better understand their outputs. 📖 Read more here: https://lnkd.in/dZXPC5jf #AI #AISecurity #Antropic #BlackBox #Defaince #Security #LLM #AIBlackBox