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David Schmotz

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#artificial intelligence Preprint Sep 2026

LLM Agents Can Easily Tamper With Their Own Traces

This work identifies a concrete failure of trace integrity in agent infrastructure which can be used to conceal misaligned behaviors like scheming or sabotage, and advises practitioners to ensure trace logging happens through an independent interception mechanism outside of the agent's control, preserving trace integri...

Jeremy Qin, David Schmotz, Derck W. E. Prinzhorn et al. · 0 citations
Preprint Aug 2026

Stealing Reasoning Traces from Proprietary LLM APIs

An architectural vulnerability is identified that circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model's reasoning, as well as proposing concrete cryptographic and system-level mitigations to secure client-side reasoning.

Alexander Panfilov, David Schmotz, I. Shumailov et al. · 9 citations · ⚡3
Preprint Jul 2026

ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D

ResearchArena is released as a modular framework for evaluating sabotage and control in automated AI R&D with ResearchArena, a framework spanning four long-horizon tasks: safety post-training, capabilities post-training, CUDA-kernel optimization, and inference-server optimization.

Lena Libon, Ben Rank, Jehyeok Yeon et al. · 0 citations

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