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AI Workflow Security Risks Highlighted by Recent Attacks

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1 unique sources, 1 articles

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Recent incidents demonstrate that the primary risk in AI systems lies not in the models themselves but in the workflows that integrate them. Two Chrome extensions stole ChatGPT and DeepSeek chat data from over 900,000 users, while prompt injections tricked IBM's AI coding assistant into executing malware. These attacks exploit the context and integrations of AI systems, highlighting the need for comprehensive workflow security. AI models are increasingly embedded in business processes, automating tasks and connecting applications. This integration creates new attack surfaces, as AI systems rely on probabilistic decision-making and lack native trust boundaries. Traditional security controls are inadequate for these dynamic and context-dependent workflows. To mitigate these risks, organizations should treat the entire workflow as the security perimeter, implementing guardrails and monitoring for anomalies. Dynamic SaaS security platforms like Reco can help by providing real-time visibility and control over AI usage.

Timeline

  1. 15.01.2026 13:55 1 articles · 7h ago

    Recent Attacks Highlight AI Workflow Security Risks

    Two Chrome extensions stole ChatGPT and DeepSeek chat data from over 900,000 users, while prompt injections tricked IBM's AI coding assistant into executing malware. These incidents demonstrate that the primary risk in AI systems lies in the workflows that integrate them, not the models themselves. Traditional security controls are inadequate for these dynamic and context-dependent workflows.

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