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Matteo Dora
Giskard announces Phare, a new open & multi-lingual LLM Benchmark
During the Paris AI Summit, Giskard launches Phare, a new open & independent LLM benchmark to evaluate key AI security dimensions including hallucination, factual accuracy, bias, and potential for harm across several languages, with Google DeepMind as research partner. This initiative is meant to provide open measurements to assess trustworthiness of Generative AI models in real applications.
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DeepSeek R1: Complete analysis of capabilities and limitations
In this article, we provide a detailed analysis of DeepSeek R1, comparing its performance against leading AI models like GPT-4o and O1. Our testing reveals both impressive knowledge capabilities and significant concerns, particularly regarding the model's tendency to generate hallucinations. Through concrete examples, we examine how R1 handles politically sensitive topics.
OWASP Top 10 for LLM 2023: Understanding the Risks of Large Language Models
In this post, we introduce OWASP's first version of the Top 10 for LLM, which identifies critical security risks in modern LLM systems. It covers vulnerabilities like Prompt Injection, Insecure Output Handling, Model Denial of Service, and more. Each vulnerability is explained with examples, prevention tips, attack scenarios, and references. The document serves as a valuable guide for developers and security practitioners to protect LLM-based applications and data from potential attacks.
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