LLM-generated adversarial fake text can perform evasion, flooding, and poisoning attacks that mislead and degrade text-based CTI pipelines.
Adversarial deep ensemble: Evasion attacks and defenses for malware detection
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MalPurifier combines diversified adversarial perturbations, protective noise injection, and a denoising autoencoder with dual loss to defend Android malware detectors, reporting over 90.91% robust accuracy against 37 evasion attacks on two datasets.
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False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems
LLM-generated adversarial fake text can perform evasion, flooding, and poisoning attacks that mislead and degrade text-based CTI pipelines.
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MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
MalPurifier combines diversified adversarial perturbations, protective noise injection, and a denoising autoencoder with dual loss to defend Android malware detectors, reporting over 90.91% robust accuracy against 37 evasion attacks on two datasets.