AI Applications Security Puzzle [Testμ 2026]

Maryia Tuleika’s Testμ Conf 2026 session argues that AI security must be assessed across four interconnected layers—application, model, infrastructure, and data—rather than only through the user-facing interface, because each layer introduces distinct risks that can create false confidence when overlooked. She highlights prompt injection as the principal application-layer threat, including direct attacks through chat and indirect instructions hidden in documents, and recommends input validation, separation of instructions from data, deterministic output controls, and least-privilege access. At the model layer, she describes model extraction through high-volume API querying and advises rate limiting and anomaly detection over techniques such as watermarking that may be difficult for smaller organizations to implement. For infrastructure, she emphasizes supply-chain risks in fast-changing AI tooling and calls for dependency management, software bills of materials, credential hygiene, and vendor assessment. Data poisoning, affecting training sets, RAG documents, and metadata, is presented as especially difficult to detect, requiring teams to treat all AI-fed content as potentially malicious and verify data sources. The session also notes that autonomous agents amplify risks through non-deterministic behavior and broad tool permissions, while urging collaboration between development, QA, and security teams and directing audiences to the OWASP AI Testing Guide and Google Secure AI Framework; several incident examples are presented as industry accounts but lack independently verifiable details in the source.

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