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2 posts

• Sponge Examples: Energy and Latency Attacks on Neural Networks

  Adversarial inputs that don't fool a model's outputs — they exhaust its compute. Sponge examples maximize inference energy and latency, enabling DoS attacks that bypass rate limits, drain edge-device batteries, and degrade shared inference infrastructure. What they are, how they work, and how to defend against them.

  Jul 15, 2026 adversarial-examples denial-of-service inference-security agent-autonomy energy-attacks ml-security ai-security neural-networks

• Adversarial Examples: The Foundational ML Attack That Still Breaks AI Systems in Production

  Imperceptible perturbations that flip neural network classifications — from FGSM and PGD to physical-world stop-sign attacks and LLM adversarial suffixes. What adversarial examples are, why gradient-based attacks work, how defenses hold up, and what this means for production AI systems today.

  Jul 7, 2026 adversarial-examples evasion-attacks ml-security adversarial-training computer-vision llm-security ai-security