The Adversarial Testing Podcast

AI Agents Enable Adaptive Computer Worms

1 h 0 min · 6 de jun de 2026
Portada del episodio AI Agents Enable Adaptive Computer Worms

Descripción

A verbatim reading of the paper by Jonas Guan, Tom Blanchard, Hanna Foerster, Hengrui Jia, Gabriel Huang, and Nicolas Papernot (arXiv, June 2026). It demonstrates a proof-of-concept AI-driven computer worm powered by a single-GPU open-weight LLM that autonomously propagates across heterogeneous networks by generating tailored attack strategies for each target, exploiting 73.8% of a 33-host test network.

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Portada del episodio Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools (Abstract, Introduction & Conclusion)

Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools (Abstract, Introduction & Conclusion)

The abstract, introduction, and conclusion of NBER Working Paper No. 35275 by Mert Demirer, Leon Musolff, and Liyuan Yang (May 2026). Using data on more than 100,000 GitHub developers and their AI usage telemetry, the paper traces how the productivity effects of AI coding tools evolve across three generations - autocomplete, sync agents, and async agents - and asks how much of those task-level gains reach final output. Each generation sharply increases coding activity, but the gains attenuate steeply across the production hierarchy: large effects on lines of code shrink to small effects on releases, consistent with a weak-link model in which human review and integration remain the binding constraint.

6 de jun de 202617 min