Avishai Ziv’s Post

⚠️#ChatGPT / OpenAI security #vulnerability discovered⚠️ Being an AI expert and security researcher for more than 15 years, my journey into the world of AI systems and their vulnerabilities has led to uncover a #critical issue that affects the #datasecurity of OpenAI's ChatGPT. 📣The vulnerability enables malicious actors to extract sensitive information, including secrets, source code, personally identifiable information (PII), protected health information (PHI), and more, from ChatGPT with just a few lines of code. This revelation should serve as a wake-up call to the AI community, urging us to prioritize data security in the development and deployment of AI models and especially OpenAI, considering their relationship with recurring data leaks. 👎The actual #attack is surprisingly simple yet remarkably effective. It revolves around exploiting ChatGPT's ability to generate responses based on the vast amount of training data it has been exposed to. ➡️ By prompting the model with specific commands and monitoring its responses, attackers can effectively "trick" ChatGPT into revealing information that should remain confidential. This vulnerability is particularly concerning because it can potentially work with various types of training data. This means that not only personal data but also proprietary corporate information and other sensitive details could be extracted by an attacker with malicious intent. The consequences of this vulnerability are far-reaching and potentially devastating. Let's break down some of the potential impacts: -🔓 Privacy Breaches: Extracting PII and PHI can lead to severe privacy breaches, resulting in identity theft, fraud, and other malicious activities. -🏢 Corporate Espionage: Companies that utilize AI models like ChatGPT for customer support or internal operations may unwittingly expose their proprietary information, trade secrets, and confidential data to attackers. -⚠ Legal and Ethical Implications: Violations of data protection laws and ethical standards can result in severe penalties and damage to an organization's reputation. -🚨 National Security: If AI systems with access to sensitive information are compromised, it can pose a significant threat to national security. As AI models become increasingly integrated into our daily lives, safeguarding sensitive information becomes paramount. #OpenAI and the broader AI community must work together to address these vulnerabilities and ensure that AI technology remains a force for good rather than a potential threat to privacy and security. By taking proactive measures, we can protect individuals, organizations, and society at large from the risks associated with AI vulnerabilities. Attached are screenshots displaying the data exposed by the vulnerability. #ai #artificialintelligence #openai #chatgpt #privacy #breach #security

📚 Exciting Research Alert! 🧠 I came across this fascinating research paper titled "Mobile Keystroke Biometrics Using Transformers." 📱🤖 🔗 You can read the full paper here: Mobile Keystroke Biometrics Using Transformers (PDF) 🤯 Why is this paper worth your attention? 🤯 Mobile keystroke biometrics is a cutting-edge field that explores the unique typing patterns of individuals as a means of user identification and authentication on mobile devices. This research leverages Transformers, a powerful deep learning architecture, to take mobile keystroke biometrics to the next level. 🚀 Key Highlights: 📱 Mobile-Centric: This research focuses on mobile devices, where user authentication is crucial for privacy and security. 🤖 Transformer Magic: Transformers, known for their success in natural language processing, are applied here to analyze mobile keystroke data, potentially leading to more accurate and efficient user recognition. 🔄 Continuous Authentication: The paper may shed light on how mobile keystroke biometrics can be used for continuous user authentication, enhancing cybersecurity on mobile platforms. 🌐 Implications: The findings of this research could have significant implications for mobile app security, user experience, and the broader field of biometrics. 🧐 Why Should You Care? 🧐 In an increasingly digital world, ensuring the security and privacy of user data is paramount. Innovative research like this pushes the boundaries of what's possible in mobile security and authentication. It's a testament to the power of deep learning and the potential for transformative applications beyond natural language processing. 🤝 Collaboration Opportunity: If you're in the fields of mobile security, deep learning, or biometrics, this paper might spark new ideas or collaborations. It's always exciting to see research that bridges different domains. 👉 Take a Deep Dive: If you have the time and interest, I highly recommend giving this paper a read. It's a glimpse into the future of mobile security. 📢 What are your thoughts on this research? How do you see the future of mobile keystroke biometrics? Share your insights! 💬 #Research #MobileSecurity #DeepLearning #Biometrics #Transformers #TechInnovation #Cybersecurity

Excited to share that my first-ever IDPro® article is up, on Authentication and AI: A Race Against Time. Reach out if you'd a copy of my forthcoming white paper that expands on the article! #authentication #biometrics #AI

If you’re an identity wonk of any kind, go read Eve’s article RIGHT NOW. I talk all the time about how KYC isn’t ready for GenAI and how “secure” portals will soon play “host” to malicious code. Eve plays out the future of digital identity with concrete steps security practitioners can take today to harden their defenses.

Unveiling the Dark Side of Artificial Intelligence: Exploring Types of AI Driven Attacks Artificial Intelligence (AI) has revolutionized various industries, making our lives more convenient and efficient. However, as with any powerful technology, there is a darker side to AI. In recent years, the emergence of AI driven attacks has become a growing concern, threatening the security of both individuals and organizations. Let's delve into three common types of AI driven attacks: Adversarial Attacks, Data Extraction Attacks, and Model Inversion Attacks. Adversarial Attacks involve manipulating AI systems by introducing subtle modifications to input data. The objective is to mislead the AI model's decision-making process, leading to potentially catastrophic consequences. For example, by making slight changes to images, an adversary can trick an AI-powered self-driving car into misidentifying a stop sign or other traffic signals. Adversarial attacks exploit the vulnerability of AI algorithms, exposing the limitations of their decision-making processes. Data Extraction Attacks, on the other hand, aim to extract sensitive information from AI models. Attackers employ various techniques, such as model inversion or membership inference attacks, to gain unauthorized access to confidential data. In model inversion attacks, adversaries exploit the outputs of an AI model to reconstruct sensitive information about the input data. This method has been used to extract private information, such as medical records or credit card details, from AI systems. Data extraction attacks highlight the importance of safeguarding AI models and the data used to train them. Lastly, Model Inversion Attacks focus on reverse engineering AI models to gain insights into confidential information. By submitting carefully crafted queries to the model, attackers exploit the model's responses to reconstruct training data or infer sensitive information about individuals. This type of attack poses a significant threat to privacy and can be used for various malicious purposes, including identity theft, blackmail, or corporate espionage. To mitigate the risks associated with AI driven attacks, researchers and developers are working tirelessly to enhance the robustness and security of AI systems. Techniques like adversarial training and model hardening are being employed to make AI models more resilient against adversarial attacks. Additionally, improving data protection measures, implementing strict access controls, and anonymizing training data can help prevent data extraction and model inversion attacks. As AI continues to advance and permeate various aspects of our lives, it is crucial to remain vigilant and proactive in countering the threats posed by AI driven attacks. The development of robust defense mechanisms and the adoption of ethical AI practices are vital to ensure that this powerful technology continues to benefit society while minimizing the potential harm.

Artificial Intelligence (AI) is reshaping various aspects of our lives, yet it is crucial to acknowledge potential risks associated with AI and its impact on identity security. To provide in-depth insight into this matter, our blog post investigates the implications of AI, particularly ChatGPT technology, and reveals the concealed risks it poses to identity security. To learn more, please follow this link: https://lnkd.in/eDgShG3t. #ChatGPT #AI #identitysecurity

#ArtificialIntelligence Over the last year, the speed, scale, and sophistication of attacks has increased alongside the rapid development and adoption of AI. Defenders are only beginning to recognize and apply the power of generative AI to shift the cybersecurity balance in their favor and keep ahead of adversaries. At the same time, it is also important for us to understand how AI can be potentially misused in the hands of threat actors. In collaboration with OpenAI, today we are publishing research on emerging threats in the age of AI, focusing on identified activity associated with known threat actors, including prompt-injections, attempted misuse of large language models, and fraud. https://lnkd.in/dmEpiBmV

Interesting insight into the threat of deepfake videos and audio cloning. 💬 With the ability to access potent AI algorithms and the widespread availability of ChatGPT, cybercriminals now possess formidable tools at their disposal. These AI advancements have fundamentally reshaped the landscape of criminal operations, offering novel challenges and prospects for the cybersecurity realm. But Payton shows that it’s possible to turn the tables on cybercriminals, as was the aim of her keynote: “Cybercrime is Accelerating: AI, Deepfakes, Voice Cloning — Future-Proof Yourself Now!” “Train staff and clients on tools to detect deepfakes and fact check information,” Payton advised. “Consider implementing internal passphrase policies for additional authentication beyond passwords.” 💬 https://lnkd.in/eFYugFwQ

Safeguarding Data Privacy and Security in AI is a shared responsibility. The rapid proliferation of artificial intelligence brings transformative potential, but it also presents substantial risks that cannot be ignored. Government initiatives are crucial to establishing the foundation for safe and secure AI adoption. Model Poisoning, where malicious actors manipulate AI data, threatens the accuracy and reliability of AI systems. To combat this, the government should mandate advanced data security measures and security checks to protect users. User Privacy is equally paramount, with the rise in identity theft and online fraud. Legislation must enforce transparent data usage practices and make user agreements more accessible, ensuring that individuals understand the implications of sharing their data. Moreover, Insider Threats, often overlooked, can be more insidious than external threats. The government should encourage Zero Trust policies, employee training, and executive accountability to prevent internal data misuse. Read our blog to learn more: https://buff.ly/3QPGk2D #AI #DataPrivacy #Security #GovernmentInitiatives #qBotica #ai #artificialintelligence

British Officials Advise Care with AI Chatbots in Business. Are you ready for the AI era?" https://lnkd.in/gvPASfJG #cybersecurity #ai #generativeai