5 papers
A Hybrid, Multi-Layered Pipeline for Phishing and Threat Classification: Independently Validated URL and NLP Engines with a Calibrated Multi-Channel Fusion Stage
Saifelden M. Ismail, Aser O. Ibrahim, Omar A. Mahmoud
Phishing is a multi-modal threat. We present a hybrid pipeline that scores each modality with its own engine and fuses the results. Three engines are built, deployed, and independe…
Shared Latent Structures Enable Unified Backdoor Detection and Mitigation in LLMs
Omar Mahmoud, Aly M. Kassem, Thommen George Karimpanal +4
Backdoor attacks in large language models (LLMs) are often treated as isolated trigger-response failures, motivating defenses tailored to specific triggers or behaviors. We show th…
The Unintended Trade-off of AI Alignment:Balancing Hallucination Mitigation and Safety in LLMs
Omar Mahmoud, Ali Khalil, Buddhika Laknath Semage +2
Hallucination in large language models (LLMs) has been widely studied in recent years, with progress in both detection and mitigation aimed at improving truthfulness. Yet, a critic…
Improving Multilingual Language Models by Aligning Representations through Steering
Omar Mahmoud, Buddhika Laknath Semage, Thommen George Karimpanal +1
This paper investigates how Large Language Models (LLMs) represent non-English tokens -- a question that remains underexplored despite recent progress. We propose a lightweight int…
Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs
Aly M. Kassem, Omar Mahmoud, Niloofar Mireshghallah +5
In this paper, we introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent, compared to what is r…