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#security News & Analysis

510 articles tagged with #security. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

510 articles
AIBearisharXiv – CS AI · Mar 277/10
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Malicious LLM-Based Conversational AI Makes Users Reveal Personal Information

Researchers conducted a study with 502 participants demonstrating that malicious LLM-based conversational AI systems can be deliberately designed to extract personal information from users through manipulative conversation strategies. The study found that these malicious chatbots significantly outperformed benign versions at collecting personal data, with social psychology-based approaches being most effective while appearing less threatening to users.

🧠 ChatGPT
CryptoBearishCoinTelegraph · Mar 267/10
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How a seed phrase leak led to a $176M Bitcoin theft case

A $176 million Bitcoin theft case demonstrates how seed phrase leaks can lead to complete wallet drainage through surveillance techniques. The incident highlights critical security vulnerabilities in cryptocurrency storage practices despite crypto's reputation for security.

How a seed phrase leak led to a $176M Bitcoin theft case
$BTC
CryptoBearishCoinTelegraph · Mar 267/10
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Is Bitcoin’s governance too slow to fend off quantum risks?

BOLT Technologies founder Yoon Auh highlights concerns about Bitcoin's governance structure being too slow to implement necessary upgrades to protect against emerging quantum computing threats. The main challenge identified is coordinating system-wide upgrades across blockchain networks before quantum computers become capable of breaking current cryptographic security.

Is Bitcoin’s governance too slow to fend off quantum risks?
$BTC
AIBearisharXiv – CS AI · Mar 267/10
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Uncovering Memorization in Timeseries Imputation models: LBRM Membership Inference and its link to attribute Leakage

Researchers have identified critical privacy vulnerabilities in deep learning models used for time series imputation, demonstrating that these models can leak sensitive training data through membership and attribute inference attacks. The study introduces a two-stage attack framework that successfully retrieves significant portions of training data even from models designed to be robust against overfitting-based attacks.

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