{"id":12654,"date":"2026-07-30T08:15:00","date_gmt":"2026-07-30T08:15:00","guid":{"rendered":"https:\/\/veilles-affaires-sociales.fr\/?p=12654"},"modified":"2026-07-30T08:15:00","modified_gmt":"2026-07-30T08:15:00","slug":"a-structured-approach-to-identifying-and-characterizing-ai-vulnerabilities","status":"publish","type":"post","link":"https:\/\/veilles-affaires-sociales.fr\/?p=12654","title":{"rendered":"A Structured Approach to Identifying and Characterizing AI Vulnerabilities"},"content":{"rendered":"
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In this report, RAND researchers present a structured vulnerability-centric framework for identifying and characterizing security weaknesses in generative artificial intelligence (AI) systems. Moving beyond attack taxonomies, the analysis systematically decomposes AI architectures\u2014from training data and tokenization through transformer layers and deployment interfaces\u2014to map where and how vulnerabilities arise.<\/p>\n

The authors identify 31 distinct classes of AI vulnerabilities, most of which differ fundamentally from traditional software flaws, often emerging from probabilistic learning dynamics, data composition, and optimization trade-offs rather than deterministic code errors.<\/p>\n

The findings demonstrate that many AI vulnerabilities are only partially patchable, requiring architectural safeguards, provenance validation, and continuous monitoring rather than conventional software updates. The authors offer practical mitigation strategies and recommend integrating AI-specific vulnerabilities into global standards to support systematic risk management. By reframing AI security around structural weaknesses rather than adversarial techniques, this work provides a foundation for future policy and engineering efforts aimed at trustworthy AI deployment.<\/p>\n<\/div>\n<\/div>\n

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