Ismail Hossain

Benign actions can be misclassified as attacks due to causal signal shifts, leading to significant utility loss in tool-using LLMs.

Quantization can enhance Bangla language model deployment, but the architecture and quantization method can dramatically influence performance, with some models losing over half their accuracy.

An LLM-as-Judge can achieve zero false negatives in identifying malicious skills, revealing critical vulnerabilities that traditional scanners miss.