Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

LLMjacking: Stolen Cloud Credentials Used in New AI Attack

The Sysdig Threat Research Team (TRT) recently observed a new attack that leveraged stolen cloud credentials in order to target ten cloud-hosted large language model (LLM) services, known as LLMjacking. The credentials were obtained from a popular target, a system running a vulnerable version of Laravel (CVE-2021-3129). Attacks against LLM-based Artificial Intelligence (AI) systems have been discussed often, but mostly around prompt abuse and altering training data.

Elastic and AWS deliver on AI-driven security analytics

Amazon Bedrock and Elastic’s Attack Discovery automate security analyst workflows As cyber threats grow increasingly sophisticated, the need for highly effective security measures becomes imperative. Traditional SIEMs aren’t equipped to address threats fast enough because they rely on too many manual and labor-intensive tasks. AI-driven security analytics from Elastic’s Search AI platform solves these challenges.

How AI Voice Assistants Transform Restaurant Profitability

The restaurant industry is undergoing a digital transformation, driven by the integration of artificial intelligence (AI) technologies. At the forefront of this revolution are AI voice assistants, poised to revolutionize customer service, streamline operations, and unlock unprecedented profitability.

Unlocking the Future: Brivo's AI-Driven Security Solutions

Dive into the world of AI-driven security with Brivo! In this video, we explore how Brivo is at the forefront of the AI evolution, tailoring cutting-edge security solutions to meet customer needs. 🛡️🔐 Discover how staying updated with AI trends like generative AI, machine learning, and cyber security enables Brivo to innovate and respond to feedback effectively. From IBM's cloud technology to the future of AI in security, learn how artificial intelligence is transforming the way we protect our spaces. 🤖🌐

Retrieval Augmented Generation (RAG): Unlocking the Power of Hybrid AI Models

Language models have revolutionized natural language processing, enabling machines to generate human-like text with remarkable fluency and coherence. However, despite their impressive capabilities, traditional language models often need help with knowledge-intensive tasks that require factual accuracy, external knowledge integration, and contextual awareness.

Leveraging RAG for Domain-Specific Knowledge Retrieval and Generation

In the era of big data and information overload, efficiently retrieving and generating relevant knowledge has become increasingly crucial across various domains. While traditional language models have made significant strides in natural language processing tasks, they often need help with factual accuracy, context awareness, and integrating external knowledge sources.