90 citations · 206 across the 39 of their papers we have counts for
8 papers · 1 filter
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…
The 2026 Singapore Consensus on Global AI Safety Research Priorities
Stephen Casper, Oskar Galeev, Yoshua Bengio +117
Frontier AI capabilities and autonomy are advancing rapidly. A growing number of real-world incidents make a trusted AI ecosystem essential to embracing AI with confidence. The 202…
Scaling Trends for Lie Detector Oversight in Preference Learning
Oskar J. Hollinsworth, Ann-Kathrin Dombrowski, Sam Adam-Day +2
Deceptive behavior in LLMs is costly to monitor and prevent, motivating approaches such as Scalable Oversight via Lie Detectors (SOLiD) (Cundy & Gleave, 2025), which uses lie detec…
Evaluating using Mock Tool Calls to Quarantine Untrusted Prompt Inputs
David Gros, Adam Gleave
Large language models must frequently process untrusted inputs, such as judging an answer from another model or running tasks like spam and harm classifiers while under adversarial…
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
Matthew Kowal, Goncalo Paulo, Louis Jaburi +6
As large language models are increasingly trained and fine-tuned, practitioners need methods to identify which training data drive specific behaviors, particularly unintended ones.…
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…
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
Mohammad Taufeeque, Stefan Heimersheim, Adam Gleave +1
Training against white-box deception detectors has been proposed as a way to make AI systems honest. However, such training risks models learning to obfuscate their deception to ev…
Large language models can effectively convince people to believe conspiracies
Thomas H. Costello, Kellin Pelrine, Matthew Kowal +6
Large language models (LLMs) have been shown to be persuasive across a variety of contexts. But it remains unclear whether this persuasive power advantages accuracy, or if bad acto…