Google Gemini “Diverse” Prompt Injection Know Your Meme, February 22, 2024 This scrutiny emphasizes the necessity for a steadfast commitment to Quality and Robustness testing before releasing AI in production. The crux of the controversy emerged on February 9th, 2024, when a Reddit user expressed dissatisfaction with Gemini’s seeming inability ...
DARPA and IBM are ensuring that anyone can protect their AI systems from hackers IBM, February 7, 2024 Collaborating with DARPA’s Guaranteeing AI Robustness Against Deception (GARD) project, IBM has been at the forefront of addressing these challenges, particularly through the development of the Adversarial Robustness Toolbox (ART). Beyond military ...
Menlo Ventures’ vision for the future of security for AI Venture Beat, February 6, 2024 Menlo Ventures draws a parallel between the rapid scalability of cloud platforms for enterprise computing and the burgeoning growth trajectory of the modern AI stack. The venture capital firm emphasizes the pivotal role of security ...
How to detect poisoned data in machine learning datasets VentureBeat, February 4, 2024 Data poisoning in machine learning datasets poses a significant threat, allowing attackers to manipulate model behavior intentionally. Proactive detection efforts are crucial to safeguarding against this threat. Data poisoning involves maliciously tampering with datasets to mislead machine ...
Welcome to the latest edition of our LLM Security Digest! We explore the dynamic landscape of LLM Security Platforms, innovative real-world incidents, and cutting-edge research that shape the field of LLM security. From adversarial AI attacks to the challenges of securing foundational models, we bring you insights, debates, and practical ...
Top 4 LLM threats to the enterprise CSO Online, January 22, 2024 The intersection of natural language prompts and training sources poses unique threats, including prompt injection, prompt extraction, phishing schemes, and the poisoning of models. Traditional security tools find it challenging to keep pace with these dynamic risks, necessitating ...
Here’s the top LLM security publications collected in one place for you. This digest provides insights into various aspects of Large Language Model (LLM) security. It covers a range of topics, from checklists for LLM Security and incidents involving vulnerabilities in chatbots to real-world attacks and initiatives by the Cloud ...
A CISO’s perspective on how to understand and address AI risk SCMedia, January 16, 2024 The adoption of AI in enterprises introduces significant risks that span technical, reputational, regulatory, and operational dimensions. From supply chain vulnerabilities to the potential theft of sensitive data, the stakes are high, demanding a proactive ...
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations NIST, January, 2024 In its comprehensive report on Trustworthy and Responsible Artificial Intelligence, the National Institute of Standards and Technology (NIST) presents a detailed classification and vocabulary for understanding adversarial machine learning (AML). This report, centered around the security ...
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