Author: Ng S.T. Chong
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How Sycophancy Shapes the Reliability of Large Language Models
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Large language models (LLMs) like ChatGPT, Claude, and Gemini are increasingly becoming trusted digital assistants in education, medicine, and professional settings. But what happens when these models prioritize pleasing the user over telling the truth? A new study from Stanford University, “SycEval: Evaluating LLM Sycophancy”, dives deep into this subtle but crucial problem: sycophancy-when AI models agree…
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HR Under Attack: Sophisticated Malware Campaign Targets Recruiters
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Recent investigations have revealed a coordinated cybersecurity threat exploiting the routine review of job applications to deploy advanced malware in corporate networks. Every day, HR professionals across the globe open dozens of resume attachments and click on application links—a routine practice that has become an ideal attack vector for cybercriminals. Security researchers at Artic Wolf have…
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LLMs: They Know More Than They Let On (And That’s a Problem)
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In a fascinating new study titled “Inside-Out: Hidden Factual Knowledge in LLMs,” researchers have uncovered compelling evidence of a significant gap between what LLMs know internally and what they can express in their outputs. This phenomenon, termed “hidden knowledge,” has important implications for evaluating and improving AI systems. The Knowledge Paradox Consider this scenario: You…
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The Invisible Threat in Your Code Editor: AI’s Package Hallucination Problem
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The intersection of artificial intelligence and software engineering is experiencing profound transformations, yet those advancements come with significant threats. A recent study conducted by researchers at the University of Texas at San Antonio (UTSA) sheds light on the critical safety issues posed by AI in software development, particularly focusing on ‘package hallucination’—a phenomenon where AI systems generate…