24 citations · 43 across the 17 of their papers we have counts for
17 papers
Trace: A Taxonomy-Guided Environment for Multidomain Visual Reasoning
Md Tanvirul Alam
Reinforcement learning with verifiable rewards (RLVR) has substantially improved language-model reasoning, yet its extension to vision-language models remains constrained by the la…
Beyond Perception Errors: Semantic Fixation in Large Vision-Language Models
Md Tanvirul Alam
Large vision-language models (VLMs) often rely on familiar semantic priors, but existing evaluations do not cleanly separate perception failures from rule-mapping failures. We stud…
Training for Trustworthy Saliency Maps: Adversarial Training Meets Feature-Map Smoothing
Dipkamal Bhusal, Md Tanvirul Alam, Nidhi Rastogi
Gradient-based saliency methods such as Vanilla Gradient (VG) and Integrated Gradients (IG) are widely used to explain image classifiers, yet the resulting maps are often noisy and…
Minerva: Reinforcement Learning with Verifiable Rewards for Cyber Threat Intelligence LLMs
Md Tanvirul Alam, Aritran Piplai, Ionut Cardei +2
Cyber threat intelligence (CTI) analysts routinely convert noisy, unstructured security artifacts into standardized, automation-ready representations. Although large language model…
Limits of Generalization in RLVR: Two Case Studies in Mathematical Reasoning
Md Tanvirul Alam, Nidhi Rastogi
Mathematical reasoning is a central challenge for large language models (LLMs), requiring not only correct answers but also faithful reasoning processes. Reinforcement Learning wit…
SPHINX: A Synthetic Environment for Visual Perception and Reasoning
Md Tanvirul Alam, Saksham Aggarwal, Justin Yang Chae +1
We present Sphinx, a synthetic environment for visual perception and reasoning that targets core cognitive primitives. Sphinx procedurally generates puzzles using motifs, tiles, ch…
AthenaBench: A Dynamic Benchmark for Evaluating LLMs in Cyber Threat Intelligence
Md Tanvirul Alam, Dipkamal Bhusal, Salman Ahmad +2
Large Language Models (LLMs) have demonstrated strong capabilities in natural language reasoning, yet their application to Cyber Threat Intelligence (CTI) remains limited. CTI anal…
Adapting Large Language Models to Emerging Cybersecurity using Retrieval Augmented Generation
Arnabh Borah, Md Tanvirul Alam, Nidhi Rastogi
Security applications are increasingly relying on large language models (LLMs) for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decis…
Towards Understanding Self-play for LLM Reasoning
Justin Yang Chae, Md Tanvirul Alam, Nidhi Rastogi
Recent advances in large language model (LLM) reasoning, led by reinforcement learning with verifiable rewards (RLVR), have inspired self-play post-training, where models improve b…
ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection
Md Tanvirul Alam, Aritran Piplai, Nidhi Rastogi
Machine learning models are commonly used for malware classification; however, they suffer from performance degradation over time due to concept drift. Adapting these models to cha…
Actionable Cyber Threat Intelligence using Knowledge Graphs and Large Language Models
Romy Fieblinger, Md Tanvirul Alam, Nidhi Rastogi
Cyber threats are constantly evolving. Extracting actionable insights from unstructured Cyber Threat Intelligence (CTI) data is essential to guide cybersecurity decisions. Increasi…
CTIBench: A Benchmark for Evaluating LLMs in Cyber Threat Intelligence
Md Tanvirul Alam, Dipkamal Bhusal, Le Nguyen +1
Cyber threat intelligence (CTI) is crucial in today's cybersecurity landscape, providing essential insights to understand and mitigate the ever-evolving cyber threats. The recent r…
SECURE: Benchmarking Large Language Models for Cybersecurity
Dipkamal Bhusal, Md Tanvirul Alam, Le Nguyen +7
Large Language Models (LLMs) have demonstrated potential in cybersecurity applications but have also caused lower confidence due to problems like hallucinations and a lack of truth…
PASA: Attack Agnostic Unsupervised Adversarial Detection using Prediction & Attribution Sensitivity Analysis
Dipkamal Bhusal, Md Tanvirul Alam, Monish K. Veerabhadran +3
Deep neural networks for classification are vulnerable to adversarial attacks, where small perturbations to input samples lead to incorrect predictions. This susceptibility, combin…
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection
Md Tanvirul Alam, Romy Fieblinger, Ashim Mahara +1
Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical. While prior re…
Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI
Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park +1
Public and commercial organizations extensively share cyberthreat intelligence (CTI) to prepare systems to defend against existing and emerging cyberattacks. However, traditional C…
CyNER: A Python Library for Cybersecurity Named Entity Recognition
Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park +1
Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet. We present CyNER, an open-source python libr…