arXiv subjects

Parth A. Gandhi

Publications and source records attributed to Parth A. Gandhi.

2 recordsLinked to original sources

arXiv2026

From Tool Orchestration to Code Execution: A Study of MCP Design Choices

Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution environments. As MCP-based systems scale to incorporate larger tool catalogs and multiple concurrently connected MCP servers, traditional tool-by-tool invocation increases coordination overhead, fragments state management, and limits support for wide-context operations. To address these scalability challenges, recent MCP designs have incorporated code execution as a first-class capability, an approach called Code Execution MCP (CE-MCP). This enables agents to consolidate complex workflows, such as SQL querying, file analysis, and multi-step data transformations, into a single program that executes within an isolated runtime environment. In this work, we formalize the architectural distinction between context-coupled (traditional) and context-decoupled (CE-MCP) models, analyzing their fundamental scalability trade-offs. Using the MCP-Bench framework across 10 representative servers, we empirically evaluate task behavior, tool utilization patterns, execution latency, and protocol efficiency as the scale of connected MCP servers and available tools increases, demonstrating that while CE-MCP significantly reduces token usage and execution latency, it introduces a vastly expanded attack surface. We address this security gap by applying the MAESTRO framework, identifying sixteen attack classes across five execution phases-including specific code execution threats such as exception-mediated code injection and unsafe capability synthesis. We validate these vulnerabilities through adversarial scenarios across multiple LLMs and propose a layered defense architecture comprising containerized sandboxing and semantic gating. Our findings provide a rigorous roadmap for balancing scalability and security in production-ready executable agent workflows.

cs.CR↗

arXiv2025

Rule-ATT&CK Mapper (RAM): Mapping SIEM Rules to TTPs Using LLMs

The growing frequency of cyberattacks has heightened the demand for accurate and efficient threat detection systems. SIEM platforms are important for analyzing log data and detecting adversarial activities through rule-based queries, also known as SIEM rules. The efficiency of the threat analysis process relies heavily on mapping these SIEM rules to the relevant attack techniques in the MITRE ATT&CK framework. Inaccurate annotation of SIEM rules can result in the misinterpretation of attacks, increasing the likelihood that threats will be overlooked. Existing solutions for annotating SIEM rules with MITRE ATT&CK technique labels have notable limitations: manual annotation of SIEM rules is both time-consuming and prone to errors, and ML-based approaches mainly focus on annotating unstructured free text sources rather than structured data like SIEM rules. Structured data often contains limited information, further complicating the annotation process and making it a challenging task. To address these challenges, we propose Rule-ATT&CK Mapper (RAM), a novel framework that leverages LLMs to automate the mapping of structured SIEM rules to MITRE ATT&CK techniques. RAM's multi-stage pipeline, which was inspired by the prompt chaining technique, enhances mapping accuracy without requiring LLM pre-training or fine-tuning. Using the Splunk Security Content dataset, we evaluate RAM's performance using several LLMs, including GPT-4-Turbo, Qwen, IBM Granite, and Mistral. Our evaluation highlights GPT-4-Turbo's superior performance, which derives from its enriched knowledge base, and an ablation study emphasizes the importance of external contextual knowledge in overcoming the limitations of LLMs' implicit knowledge for domain-specific tasks. These findings demonstrate RAM's potential in automating cybersecurity workflows and provide valuable insights for future advancements in this field.

cs.CR↗