A threat actor started using the Shai-Hulud worm in attacks only days after the malware’s source code was released.
Abstract: The Deep Neural Network (DNN) based detection model’s dependency on third-party crowdsourced sources poses a new security threat from backdoor attacks against malware detectors. Attackers ...
Abstract: Software vulnerabilities pose critical risks to the security and reliability of modern systems, requiring effective detection, repair, and explanation techniques. Large Language Models (LLMs ...
Stolen browser sessions and authentication tokens are becoming more valuable than stolen passwords. Flare explains how the ...
OpenAI says malware tied to the Shai-Hulud supply chain attack accessed internal repositories after infecting two employee ...
Weekly ThreatsDay Bulletin: supply chain attacks, fake support lures, AI tampering, data leaks, ransomware, and exploited ...
Microsoft and Palo Alto Networks have separately reported significant results after turning AI on their own code to find ...
Morning Overview on MSN
The AI-generated zero-day discovered by Google used clean 'textbook' Python code — a hallmark of large language model output
The exploit code was almost too neat. When Google’s Threat Intelligence Group flagged a previously unknown software ...
Google's GTIG identified the first zero-day exploit developed with AI and stopped a mass exploitation event. The report documents state actors using AI for vulnerability research and autonomous ...
Hundreds of packages across npm and PyPI have been compromised in a new Shai-Hulud supply-chain campaign delivering ...
Google says attackers are using AI for zero-day research, malware development, reconnaissance, and access to premium AI tools ...
TeamPCP’s Mini Shai-Hulud campaign used hijacked GitHub OIDC tokens to spread a credential-stealing worm through TanStack npm ...
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