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Luca Beurer-Kellner

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#artificial intelligence Preprint May 2026

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents

The Trojan Hippo Bench is introduced, a dynamic evaluation framework for persistent memory attacks and memory-layer defenses, comprising an OpenEvolve-based adaptive red-teaming benchmark that stress-tests defenses and memory backends against continuously refined attacks, and a capability-aware security-utility analysi...

Debeshee Das, Julien Piet, D. Kaviani et al. · 12 citations · ⚡1
#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
#artificial intelligence Preprint Sep 2026

Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

Several open research questions are identified, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize, and privacy requirements are summarized and research directions for the secure and trustworthy deployment of AI agents are outlined.

Anastasia Pustozerova, E. Bagdasarian, Luca Beurer-Kellner 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

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