aiqbench analysis
This article focuses on a specific threat vector where ransomware attacks are increasingly targeting and destroying AI/ML infrastructure rather than just exfiltrating data, marking a shift in attacker strategy toward high-value model assets. The analysis highlights the operational details of the JADEPUFFER campaign, noting its use of an autonomous AI agent to execute the attack and the resulting impossibility of recovering encrypted model configurations.
When Ransomware Targets AI Models: Defending the AI/ML Recovery Chain

By Crystal Morin Ransomware crews have always followed the money. For years, that meant targets like banks and hospitals organizations with high-value assets and sensitive data who couldn’t afford downtime. Now, it means AI models too. A threat actor the Sysdig Threat Research Team (TRT) dubbed JADEPUFFER has already shown us where the future of ransomware is heading. In the span of a […] The post When Ransomware Targets AI Models: Defending the AI/ML Recovery Chain appeared first on Cyber Security News.
Ransomware crews have always followed the money. For years, that meant targets like banks and hospitals organizations with high-value assets and sensitive data who couldn’t afford downtime. Now, it means AI models too. A threat actor the Sysdig Threat Research Team (TRT) dubbed JADEPUFFER has already shown us where the future of ransomware is heading. In the span of a few weeks, JADEPUFFER went from poorly executed (albeit successful) database extortion with throwaway scripts to deploying a compiled ransomware binary built for one purpose: destroying AI and machine learning (ML) assets. That progression tells us what attackers now consider valuable. JADEPUFFER’s first campaign was scrappy, but it worked. The operator broke into a Langflow instance through CVE-2025-3248, an unauthenticated remote code execution (RCE) flaw that CISA added to its Known Exploited Vulnerabilities (KEV) catalog in May 2025. Langflow is a rich target because it holds LLM provider…
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