Deep learning, as an important branch of machine learning, has been widely applied in
computer vision, natural language processing, speech recognition, and more. However …

The safety of decentralized reinforcement learning (RL) is a challenging problem since
malicious agents can share their poisoned policies with benign agents. The paper …

Reinforcement learning (RL) is an actively growing field that is seeing increased usage in
real-world, safety-critical applications--making it paramount to ensure the robustness of RL …

Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-
MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will …

With the rapid development of generative artificial intelligence, particularly large language
models, a number of sub-fields of deep learning have made significant progress and are …

This paper proposes an online environment poisoning algorithm tailored for reinforcement
learning agents operating in a black-box setting, where an adversary deliberately …

In recent years, large language models (LLMs) have made significant progress in the field of
code generation. However, as more and more users rely on these models for software …

Modern machine learning underpins a large variety of commercial software products,
including many cybersecurity solutions. Widely different models, from large transformers …