activity
20172026
most citedTowards Deep Learning Models Resistant to Adversarial Attacks

1.5k citations · 1.5k across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026★ 18 cited

OpenAI GPT-5 System Card

Aaditya Singh, Adam Fry, Adam Perelman +483

This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…

cs.LG2025★ 2 cited

Persona Features Control Emergent Misalignment

Miles Wang, Tom Dupré la Tour, Olivia Watkins +8

Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that…

cs.LG2024★ 3 cited

Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control

Aleksandar Makelov, George Lange, Neel Nanda

Disentangling model activations into meaningful features is a central problem in interpretability. However, the absence of ground-truth for these features in realistic scenarios ma…

cs.LG2023★ 1 cited

Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching

Aleksandar Makelov, Georg Lange, Neel Nanda

Mechanistic interpretability aims to understand model behaviors in terms of specific, interpretable features, often hypothesized to manifest as low-dimensional subspaces of activat…

cs.CR2023★ 3 cited

Rethinking Backdoor Attacks

Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov +4

In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against s…

stat.ML2017★ 1.5k cited

Towards Deep Learning Models Resistant to Adversarial Attacks

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt +2

Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorr…