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Adversarial Attacks
ServiceNow AI Research
Adversarial Attacks
Visual Memory Attacks Can Persist Through Context Masking
Vision-language models (VLMs) are increasingly deployed as autonomous agents that ingest untrusted content, including images. Recent …
David Dobre
,
Leo Schwinn
,
Gauthier Gidel
,
Spandana Gella
,
Perouz Taslakian
,
Pierre-André Noël
COLM Workshops, 2026.
Citation
Malice in Agentland: Down the Rabbit Hole of Backdoors in the AI Supply Chain
The practice of fine-tuning AI agents on data from their own interactions—such as web browsing or tool use—, while being a strong …
Léo Boisvert
,
Abhay Puri
,
Chandra Kiran Reddy Evuru
,
Nicolas Chapados
,
Quentin Cappart
,
Alexandre Lacoste
,
Nazanin Sepahvand
,
Krishnamurthy (Dj) Dvijotham
,
Alexandre Drouin
,
Jason Stanley
ACM Conference on AI and Agentic Systems, 2026.
Article
Citation
Code
Keeping up with dynamic attackers: Certifying robustness to adaptive online data poisoning
The rise of foundation models fine-tuned on human feedback from potentially untrusted users has increased the risk of adversarial data …
Avinandan Bose
,
Laurent Lessard
,
Maryam Fazel
,
Krishnamurthy (Dj) Dvijotham
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025.
Article
Citation
Code
Vidéo
Constraining Representations Yields Models That Know What They Don't Know
A well-known failure mode of neural networks is that they may confidently return erroneous predictions. Such unsafe behaviour is …
João Monteiro
,
Pau Rodriguez
,
Pierre-André Noël
,
Issam H. Laradji
,
David Vazquez
International Conference of Learning Representations (ICLR), 2023.
Article
Citation
Code
Model card
Diapositives
Vidéo
Maximal Jacobian-based Saliency Map Attack
The Jacobian-based Saliency Map Attack is a family of adversarial attack methods for fooling classification models, such as deep neural …
Rey Reza Wiyatno
,
Anqi Xu
Montreal AI Symposium (MAIS), 2018.
Article
Citation
Code
Citation
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