Call for participants: Anti-BAD challenge at IEEE SaTML 2026 (March 2026, Germany)

[qiongkai xu]

Dear Colleagues, We are pleased to announce the Anti-Backdoor Challenge (Anti-BAD) at IEEE SaTML 2026.

Anti-BAD addresses LLM backdoor defense in deployment-oriented post-training settings. It presents a practical and timely challenge that aims to promote the development of lightweight and effective defense methods capable of restoring model integrity while preserving clean-task utility in realistic model-sharing ecosystems.

The competition has been released on Codabench (https://www.codabench.org/competitions/11188/), and the development phase will start on November 7, 2025, inviting everyone to participate and test their defense methods.

Competition Website: https://anti-bad.github.io/

=== Tracks ===

Track 1: Generation (English)

Track 2: Classification (English)

Track 3: Multilingual Classification (35+ languages)

These tracks represent key application scenarios of large language models, covering both generation and classification tasks across English and multilingual settings. Each track provides several backdoored models, each poisoned by a distinct and undisclosed method. We challenge participants to design robust and generalizable model-wise defenses in a post-training setting.

=== Timeline ===

Registration opens: October 21, 2025

Development phase: November 7, 2025

Test phase: February 1-7, 2026

Final results announcement: February 8, 2026

=== More Information ===

1. IEEE SaTML Competitions: https://satml.org/competitions/

2. Discord Channel for Discussion: https://discord.gg/x8GqKDF2Rb

Best regards,

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Dr. Qiongkai Xu (Lecturer) on behalf of Anti-BAD Challenge Organizing Team
School of Computing, FSE, Macquarie University
Sydney, NSW, Australia
Webpage: https://xuqiongkai.github.io/