9 papers
When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity
Samuel Jacob Chacko, James Hugglestone, Chashi Mahiul Islam +1
Agent Skills, structured packages of procedural knowledge loaded into an LLM agent at inference time, are widely reported to improve task pass rates by an average of 16.2~percentag…
STRIATUM-CTF: A Protocol-Driven Agentic Framework for General-Purpose CTF Solving
James Hugglestone, Samuel Jacob Chacko, Dawson Stoller +2
Large Language Models (LLMs) have demonstrated potential in code generation, yet they struggle with the multi-step, stateful reasoning required for offensive cybersecurity operatio…
CS-Guide: Leveraging LLMs and Student Reflections to Provide Frequent, Scalable Academic Monitoring Feedback to Computer Science Students
Samuel Jacob Chacko, An-I Andy Wang, Lara Perez-Felkner +3
Computer Science (CS) departments often serve large student populations, making timely academic monitoring and personalized feedback difficult. While the recommended counselor-to-s…
Spatial-ViLT: Enhancing Visual Spatial Reasoning through Multi-Task Learning
Chashi Mahiul Islam, Oteo Mamo, Samuel Jacob Chacko +2
Vision-language models (VLMs) have advanced multimodal reasoning but still face challenges in spatial reasoning for 3D scenes and complex object configurations. To address this, we…
Universal and Transferable Adversarial Attack on Large Language Models Using Exponentiated Gradient Descent
Sajib Biswas, Mao Nishino, Samuel Jacob Chacko +1
As large language models (LLMs) are increasingly deployed in critical applications, ensuring their robustness and safety alignment remains a major challenge. Despite the overall su…
Adversarial Attack on Large Language Models using Exponentiated Gradient Descent
Sajib Biswas, Mao Nishino, Samuel Jacob Chacko +1
As Large Language Models (LLMs) are widely used, understanding them systematically is key to improving their safety and realizing their full potential. Although many models are ali…