Black Hat · July 8, 2026

Breaking AI Inference Systems: Lessons From Pwn2Own Berlin

video AI Red Teaming Adversarial ML AI Engineering

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Why it matters

Fuzzinglabs researchers explain how threat modeling, file-format fuzzing, and plugin analysis exposed an authentication bypass and memory-corruption issues in Ollama plus command injection in NVIDIA Triton Inference Server's model-configuration pipeline. The Pwn2Own case study also examines RedisAI, ChromaDB, and container-runtime attack surfaces.

My takeaway: Audit inference infrastructure as a software supply chain, not only as a model endpoint. Fuzz model and configuration parsers, review plugin boundaries and authentication defaults, isolate conversion and loading jobs, remove ambient credentials, and verify that malformed artifacts cannot reach shells or privileged runtimes.

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