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20182026
most citedAdversarial Policies: Attacking Deep Reinforcement Learning

90 citations · 206 across the 39 of their papers we have counts for

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5 papers · 1 filter

cs.CR2026

AI Security Leaderboard: Methodology, Results and Minimal Standard

Jasper Timm, Lukas Struppek, Ziwei Xu +12

The AI Security Leaderboard is an independent benchmark that ranks the safeguards of frontier AI models from least to most secure. It tests models against the FARAI Minimal Stan…

cs.CR2026

Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks

Lukas Struppek, Adam Gleave, Kellin Pelrine

As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on external defenses, open-weight mo…

cs.CR2025

Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility

Brendan Murphy, Dillon Bowen, Shahrad Mohammadzadeh +4

AI systems are rapidly advancing in capability, and frontier model developers broadly acknowledge the need for safeguards against serious misuse. However, this paper demonstrates t…

cs.CR2024★ 1 cited

Scaling Trends for Data Poisoning in LLMs

Dillon Bowen, Brendan Murphy, Will Cai +3

LLMs produce harmful and undesirable behavior when trained on datasets containing even a small fraction of poisoned data. We demonstrate that GPT models remain vulnerable to fine-t…

cs.CR2023★ 3 cited

Exploiting Novel GPT-4 APIs

Kellin Pelrine, Mohammad Taufeeque, Michał Zając +2

Language model attacks typically assume one of two extreme threat models: full white-box access to model weights, or black-box access limited to a text generation API. However, rea…