Author

Matthew Jagielski

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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

#artificial intelligence Preprint Sep 2026

Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks

This work formalizes poison selection as oracle-budgeted set optimization and introduces SAILS (Set-level Audit-Informed Iterative Learned Selection), which learns a set scorer from a few hundred finetune-and-evaluate runs, ranks millions of candidate sets, and audits only a small shortlist.

Aashiq Muhamed, Mona T. Diab, Virginia Smith 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

Jul 2026

Untrusted Content Masking for Web Agents with Security Guarantees

This paper presents Untrusted Content Masking (UCM), a simple and effective approach that restores the trust boundary in web environments by leveraging a key structural insight: a webpage's Document Object Model encodes sufficient information to distinguish trusted from untrusted regions without reading their content.

Kristina Nikoli'c, Egor Zverev, Javier Rando et al. · arXiv.org · 2 citations

#cybersecurity Preprint Aug 2026

The Next Challenge for Agentic Cybersecurity: A Realistic, Contamination-Free Reverse Engineering Benchmark

SRE-Bench is introduced, the first realistic, contamination-free RE benchmark, and results indicate that strong source-code security capabilities do not yet transfer to binary analysis, highlighting RE as an important frontier for agentic cybersecurity and SRE-Bench as a rigorous testbed to measure progress.

J. Spence, Nicholas Assaderaghi, Feng Xiao et al. · 1 citation