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Ransomware operators are using generative AI to sharpen phishing campaigns, speed up reconnaissance, and identify sensitive corporate information to steal. Zscaler ThreatLabz describes a shift toward faster, more selective extortion, where attackers pr…
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A Chinese-speaking hacker used autonomous AI agents to compromise the websites of more than 100 companies, relying primarily on software for most of the intrusion work. Reuters reporting and research published by NorForge on October 19 provide this inf…
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RedAgents, a cloud platform that deploys multiple AI penetration testing agents against authorized targets to discover vulnerabilities and test attack paths. Announced on October 16, 2026, the service brings Artex Pro, Cyber(YanJi), Yanxun, and CyberSt…
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Hackers used AI-assisted scripts to compromise government Linux servers, harvest administrator credentials, and establish persistent backdoors during a campaign observed between October 2025 and April 2026. The cluster remains unattributed. Under Unit …
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A likely Russian-speaking threat actor used hundreds of AI agents to exploit PaperCut NG/MF servers, steal Windows credentials and establish reusable access to internal networks. GreyNoise identified at least 440 compromised instances belonging to 395 …
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AI agents can expose credentials during routine development tasks, moving secrets from protected stores into command arguments, logs, transcripts, and inputs to external models. The concern is not that developer tools are inherently malicious. Rather, …
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Artificial intelligence appeared in ransomware incidents affecting 84% of surveyed victims, while 87% reported attacks targeting or exploiting identity infrastructure. The findings connect accelerated criminal operations with weaknesses in the systems …
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Ransomware operators are extending their extortion tactics beyond corporate networks by threatening employees’ families and using artificial intelligence to enhance their attacks, according to the Semperis 2026 Ransomware Risk Report. Among the s…
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Built on Qwen3.5-2B, the System One model executes sub-31ms local inference, allowing enterprises to run automated classifications quickly, at low cost and completely offline without transmitting sensitive data to third-party cloud servers.
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Check Point Research found two LiteLLM vulnerabilities that could let low-privilege users execute code, access sensitive files, hijack admin accounts, and steal AI provider API keys.
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