Author

Nicholas Carlini

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Preprint Oct 2026

Agentic Backdoors via Pretraining Poisoning

Large language models are pretrained on vast, uncurated corpora scraped from the public web. This creates a security risk from attackers who plant poisoned content in the pretraining data. We show for the first time that such poisoning can plant a remote code execution backdoor in an agent, causing it to execute attack...

Xin Hu, Ping-Bang Hu, Chloe Loughridge et al. · 0 citations

Preprint Oct 2026

Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?

Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML engineering. We introduce Agent4Sci, a long-horizon testbed for measuring how far today's agents ca...

Ming Zhong, Stacy Li, Nicholas Carlini et al. · 0 citations

Jailbreaking Jailbreaks: A Proactive Defense for LLMs

P RO A CT represents an orthogonal defense strategy that serves as an additional guardrail to enhance LLM safety against the most effective attacks.

Wei-Liang Zhao, Daniel Ben-Levi, Jinjun Peng et al. · 1 citation

Jul 2026

CryptanalysisBench: Can LLMs do Cryptanalysis?

This paper introduces CryptanalysisBench, 191 tasks across six families of cryptographic primitives drawn primarily from four NIST standardization competitions, and releases CryptanalysisBench as a tool to help track if (or when) AI cryptanalysis becomes a serious factor and as a scaffold for stress-testing candidate s...

Lukas Fluri, Avital Shafran, Nicholas Carlini et al. · arXiv.org · 1 citation