Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers

Controllability-Aware Adversarial Examples Against LLM-Based Network Traffic Classifiers arXiv:2607.07739v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored as network intrusion detection classifiers, but their adversarial robustness under realistic attacker constraints remains unclear. We present a controllability-aware black-box transfer framework for LLM-based network traffic classifiers. The framework partitions flow features into direc…

https://arxiv.org/abs/2607.07739

sha256:51b2b4ce97dbb1f12ed54b97bbcd70c4a26f9476c1ae808155260ad189294b51

• cve0
• md50
• url0
• ipv40
• ipv60
• sha10
• domain0
• sha2560
• malware_token0
• attack_pattern0
• threat_actor_token0