arXiv subjects

Sudip Mittal

Publications and source records attributed to Sudip Mittal.

At least 37 records · Page 2Linked to original sources

arXiv2026

AgenticCyOps: Securing Multi-Agentic AI Integration in Enterprise Cyber Operations

Multi-agent systems (MAS) powered by LLMs promise adaptive, reasoning-driven enterprise workflows, yet granting agents autonomous control over tools, memory, and communication introduces attack surfaces absent from deterministic pipelines. While current research largely addresses prompt-level exploits and narrow individual vectors, it lacks a holistic architectural model for enterprise-grade security. We introduce AgenticCyOps (Securing Multi-Agentic AI Integration in Enterprise Cyber Operations), a framework built on a systematic decomposition of attack surfaces across component, coordination, and protocol layers, revealing that documented vectors consistently trace back to two integration surfaces: tool orchestration and memory management. Building on this observation, we formalize these integration surfaces as primary trust boundaries and define five defensive principles: authorized interfaces, capability scoping, verified execution, memory integrity & synchronization, and access-controlled data isolation; each aligned with established compliance standards (NIST, ISO 27001, GDPR, EU AI Act). We apply the framework to a Security Operations Center (SOC) workflow, adopting the Model Context Protocol (MCP) as the structural basis, with phase-scoped agents, consensus validation loops, and per-organization memory boundaries. Coverage analysis, attack path tracing, and trust boundary assessment confirm that the design addresses the documented attack vectors with defense-in-depth, intercepts three of four representative attack chains within the first two steps, and reduces exploitable trust boundaries by a minimum of 72% compared to a flat MAS, positioning AgenticCyOps as a foundation for securing enterprise-grade integration.

cs.CR↗

arXiv2026

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges

The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem introduces vulnerabilities, making them susceptible to adversarial attacks where a single misconfiguration can lead to compromised credentials, severe financial losses, damaged public trust, and the poisoning of training data. Our paper presents a systematic application of the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework, supplemented by reviews of white and grey literature, to systematically assess attacks across different phases of the MLOps ecosystem. We begin by reviewing prior work in this domain, then present our taxonomy and introduce a threat model that captures attackers with different knowledge and capabilities. We then present a structured taxonomy of attack techniques explicitly mapped to corresponding phases of the MLOps ecosystem, supported by examples drawn from red-teaming exercises and real-world incidents. This is followed by a taxonomy of mitigation strategies aligned with these attack categories, offering actionable early-stage defenses to strengthen the security of MLOps ecosystem. Given the gradual evolution and adoption of MLOps, we further highlight key research gaps that require immediate attention. Our work emphasizes the importance of implementing robust security protocols from the outset, empowering practitioners to safeguard MLOps ecosystem against evolving cyber attacks.

cs.CR↗

arXiv2025

IRSDA: An Agent-Orchestrated Framework for Enterprise Intrusion Response

Modern enterprise systems face escalating cyber threats that are increasingly dynamic, distributed, and multi-stage in nature. Traditional intrusion detection and response systems often rely on static rules and manual workflows, which limit their ability to respond with the speed and precision required in high-stakes environments. To address these challenges, we present the Intrusion Response System Digital Assistant (IRSDA), an agent-based framework designed to deliver autonomous and policy-compliant cyber defense. IRSDA combines Self-Adaptive Autonomic Computing Systems (SA-ACS) with the Knowledge guided Monitor, Analyze, Plan, and Execute (MAPE-K) loop to support real-time, partition-aware decision-making across enterprise infrastructure. IRSDA incorporates a knowledge-driven architecture that integrates contextual information with AI-based reasoning to support system-guided intrusion response. The framework leverages retrieval mechanisms and structured representations to inform decision-making while maintaining alignment with operational policies. We assess the system using a representative real-world microservices application, demonstrating its ability to automate containment, enforce compliance, and provide traceable outputs for security analyst interpretation. This work outlines a modular and agent-driven approach to cyber defense that emphasizes explainability, system-state awareness, and operational control in intrusion response.

cs.CR↗

arXiv2025

LLMZ+: Contextual Prompt Whitelist Principles for Agentic LLMs

Compared to traditional models, agentic AI represents a highly valuable target for potential attackers as they possess privileged access to data sources and API tools, which are traditionally not incorporated into classical agents. Unlike a typical software application residing in a Demilitarized Zone (DMZ), agentic LLMs consciously rely on nondeterministic behavior of the AI (only defining a final goal, leaving the path selection to LLM). This characteristic introduces substantial security risk to both operational security and information security. Most common existing defense mechanism rely on detection of malicious intent and preventing it from reaching the LLM agent, thus protecting against jailbreak attacks such as prompt injection. In this paper, we present an alternative approach, LLMZ+, which moves beyond traditional detection-based approaches by implementing prompt whitelisting. Through this method, only contextually appropriate and safe messages are permitted to interact with the agentic LLM. By leveraging the specificity of context, LLMZ+ guarantees that all exchanges between external users and the LLM conform to predefined use cases and operational boundaries. Our approach streamlines the security framework, enhances its long-term resilience, and reduces the resources required for sustaining LLM information security. Our empirical evaluation demonstrates that LLMZ+ provides strong resilience against the most common jailbreak prompts. At the same time, legitimate business communications are not disrupted, and authorized traffic flows seamlessly between users and the agentic LLM. We measure the effectiveness of approach using false positive and false negative rates, both of which can be reduced to 0 in our experimental setting.

cs.AI↗

arXiv2025

Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy

This paper addresses the critical challenge of estimating the reliability of an Electric Vehicle (EV) charging systems when facing risks such as overheating, unpredictable, weather, and cyberattacks. Traditional methods for predicting failures often rely on past data or limiting assumptions, making them ineffective for new or less common threats that results in failure. To solve this issue, we utilize the Principle of Maximum Entropy (PME), a statistical tool that estimates risks even with limited information. PME works by balancing known constraints to create an unbiased predictions without guessing missing details. Using the EV charging ecosystem as a case study, we show how PME models stress factors responsible for failure. Our findings reveal a critical insight: even minor, localized stress events can trigger disproportionately large drops in overall system reliability, similar to a domino effect. The our PME model demonstrates how high-impact components, such as the power grid, are more likely to fail as stress accumulates, creating network-wide tipping points. Beyond EVs, this approach applies to any complex system with incomplete data, such as smart grids, healthcare devices, or logistics networks. By mathematically establishing an inverse relationship between uncertainty (entropy) and reliability, our work quantifies how greater system unpredictability directly degrades robustness. This offers a universal tool to improve decision-making under unpredictable conditions. This work bridges advanced mathematics with real-world engineering, providing actionable insights for policymakers and industries to build safer, more efficient systems in our increasingly connected world.

eess.SY↗

arXiv2025

ModelForge: Using GenAI to Improve the Development of Security Protocols

Formal methods can be used for verifying security protocols, but their adoption can be hindered by the complexity of translating natural language protocol specifications into formal representations. In this paper, we introduce ModelForge, a novel tool that automates the translation of protocol specifications for the Cryptographic Protocol Shapes Analyzer (CPSA). By leveraging advances in Natural Language Processing (NLP) and Generative AI (GenAI), ModelForge processes protocol specifications and generates a CPSA protocol definition. This approach reduces the manual effort required, making formal analysis more accessible. We evaluate ModelForge by fine-tuning a large language model (LLM) to generate protocol definitions for CPSA, comparing its performance with other popular LLMs. The results from our evaluation show that ModelForge consistently produces quality outputs, excelling in syntactic accuracy, though some refinement is needed to handle certain protocol details. The contributions of this work include the architecture and proof of concept for a translating tool designed to simplify the adoption of formal methods in the development of security protocols.

cs.CR↗

arXiv2025

Towards a HIPAA Compliant Agentic AI System in Healthcare

Agentic AI systems powered by Large Language Models (LLMs) as their foundational reasoning engine, are transforming clinical workflows such as medical report generation and clinical summarization by autonomously analyzing sensitive healthcare data and executing decisions with minimal human oversight. However, their adoption demands strict compliance with regulatory frameworks such as Health Insurance Portability and Accountability Act (HIPAA), particularly when handling Protected Health Information (PHI). This work-in-progress paper introduces a HIPAA-compliant Agentic AI framework that enforces regulatory compliance through dynamic, context-aware policy enforcement. Our framework integrates three core mechanisms: (1) Attribute-Based Access Control (ABAC) for granular PHI governance, (2) a hybrid PHI sanitization pipeline combining regex patterns and BERT-based model to minimize leakage, and (3) immutable audit trails for compliance verification.

cs.MA↗

arXiv2025

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification, their utility is limited by semantic embedding overlaps and misalignment with binary behavioral features. We propose a contrastive fine-tuning (CFT) method that refines LLM embeddings via targeted selection of hard negative samples based on cosine similarity, enabling LLMs to distinguish between closely related malware families. Our approach combines high-similarity negatives to enhance discriminative power and mid-tier negatives to increase embedding diversity, optimizing both precision and generalization. Evaluated on the CIC-AndMal-2020 and BODMAS datasets, our refined embeddings are integrated into a multimodal classifier within a Model-Agnostic Meta-Learning (MAML) framework on a few-shot setting. Experiments demonstrate significant improvements: our method achieves 63.15% classification accuracy with as few as 20 samples on CIC-AndMal-2020, outperforming baselines by 11--21 percentage points and surpassing prior negative sampling strategies. Ablation studies confirm the superiority of similarity-based selection over random sampling, with gains of 10-23%. Additionally, fine-tuned LLMs generate attribute-aware descriptions that generalize to unseen variants, bridging textual and binary feature gaps. This work advances malware classification by enabling nuanced semantic distinctions and provides a scalable framework for adapting LLMs to cybersecurity challenges.

cs.CR↗

arXiv2025

Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers

The adoption of Machine Learning Operations (MLOps) enables automation and reliable model deployments across industries. However, differing MLOps lifecycle frameworks and maturity models proposed by industry, academia, and organizations have led to confusion regarding standard adoption practices. This paper introduces a unified MLOps lifecycle framework, further incorporating Large Language Model Operations (LLMOps), to address this gap. Additionally, we outlines key roles, tools, and costs associated with MLOps adoption at various maturity levels. By providing a standardized framework, we aim to help organizations clearly define and allocate the resources needed to implement MLOps effectively.

cs.SE↗

arXiv2025

From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction

The transition towards patient-centric healthcare necessitates a comprehensive understanding of patient journeys, which encompass all healthcare experiences and interactions across the care spectrum. Existing healthcare data systems are often fragmented and lack a holistic representation of patient trajectories, creating challenges for coordinated care and personalized interventions. Patient Journey Knowledge Graphs (PJKGs) represent a novel approach to addressing the challenge of fragmented healthcare data by integrating diverse patient information into a unified, structured representation. This paper presents a methodology for constructing PJKGs using Large Language Models (LLMs) to process and structure both formal clinical documentation and unstructured patient-provider conversations. These graphs encapsulate temporal and causal relationships among clinical encounters, diagnoses, treatments, and outcomes, enabling advanced temporal reasoning and personalized care insights. The research evaluates four different LLMs, such as Claude 3.5, Mistral, Llama 3.1, and Chatgpt4o, in their ability to generate accurate and computationally efficient knowledge graphs. Results demonstrate that while all models achieved perfect structural compliance, they exhibited variations in medical entity processing and computational efficiency. The paper concludes by identifying key challenges and future research directions. This work contributes to advancing patient-centric healthcare through the development of comprehensive, actionable knowledge graphs that support improved care coordination and outcome prediction.

cs.CL↗

arXiv2025

RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration

Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies that are adaptive, transferable, and capable of integrating domain-specific knowledge. In this paper, we present RAAD-LLM, a novel framework for adaptive anomaly detection, leveraging large language models (LLMs) integrated with Retrieval-Augmented Generation (RAG). This approach addresses the aforementioned PdM challenges. By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time series data without requiring fine-tuning on specific datasets. The framework's adaptability mechanism enables it to adjust its understanding of normal operating conditions dynamically, thus increasing detection accuracy. We validate this methodology through a real-world application for a plastics manufacturing plant and the Skoltech Anomaly Benchmark (SKAB). Results show significant improvements over our previous model with an accuracy increase from 70.7% to 88.6% on the real-world dataset. By allowing for the enriching of input series data with semantics, RAAD-LLM incorporates multimodal capabilities that facilitate more collaborative decision-making between the model and plant operators. Overall, our findings support RAAD-LLM's ability to revolutionize anomaly detection methodologies in PdM, potentially leading to a paradigm shift in how anomaly detection is implemented across various industries.

cs.LG↗

arXiv2025

The AI Pentad, the CHARME$^{2}$D Model, and an Assessment of Current-State AI Regulation

Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespread consensus on the need to regulate AI, there remains a lack of a unified approach to framing, developing, and assessing AI regulations. Many of the existing methods take a value-based approach, for example, accountability, fairness, free from bias, transparency, and trust. However, these methods often face challenges at the outset due to disagreements in academia over the subjective nature of these definitions. This paper aims to establish a unifying model for AI regulation from the perspective of core AI components. We first introduce the AI Pentad, which comprises the five essential components of AI: humans and organizations, algorithms, data, computing, and energy. We then review AI regulatory enablers, including AI registration and disclosure, AI monitoring, and AI enforcement mechanisms. Subsequently, we present the CHARME$^{2}$D Model to explore further the relationship between the AI Pentad and AI regulatory enablers. Finally, we apply the CHARME$^{2}$D model to assess AI regulatory efforts in the European Union (EU), China, the United Arab Emirates (UAE), the United Kingdom (UK), and the United States (US), highlighting their strengths, weaknesses, and gaps. This comparative evaluation offers insights for future legislative work in the AI domain.

cs.CY↗

arXiv2025

Patient Journey Ontology: Representing Medical Encounters for Enhanced Patient-Centric Applications

The healthcare industry is moving towards a patient-centric paradigm that requires advanced methods for managing and representing patient data. This paper presents a Patient Journey Ontology (PJO), a framework that aims to capture the entirety of a patient's healthcare encounters. Utilizing ontologies, the PJO integrates different patient data sources like medical histories, diagnoses, treatment pathways, and outcomes; it enables semantic interoperability and enhances clinical reasoning. By capturing temporal, sequential, and causal relationships between medical encounters, the PJO supports predictive analytics, enabling earlier interventions and optimized treatment plans. The ontology's structure, including its main classes, subclasses, properties, and relationships, as detailed in the paper, demonstrates its ability to provide a holistic view of patient care. Quantitative and qualitative evaluations by Subject Matter Experts (SMEs) demonstrate strong capabilities in patient history retrieval, symptom tracking, and provider interaction representation, while identifying opportunities for enhanced diagnosis-symptom linking. These evaluations reveal the PJO's reliability and practical applicability, demonstrating its potential to enhance patient outcomes and healthcare efficiency. This work contributes to the ongoing efforts of knowledge representation in healthcare, offering a reliable tool for personalized medicine, patient journey analysis and advancing the capabilities of Generative AI in healthcare applications.

cs.DB↗

arXiv2025

LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge

Security Operations Center (SoC) analysts gather threat reports from openly accessible global threat repositories and tailor the information to their organization's needs, such as developing threat intelligence and security policies. They also depend on organizational internal repositories, which act as private local knowledge database. These local knowledge databases store credible cyber intelligence, critical operational and infrastructure details. SoCs undertake a manual labor-intensive task of utilizing these global threat repositories and local knowledge databases to create both organization-specific threat intelligence and mitigation policies. Recently, Large Language Models (LLMs) have shown the capability to process diverse knowledge sources efficiently. We leverage this ability to automate this organization-specific threat intelligence generation. We present LocalIntel, a novel automated threat intelligence contextualization framework that retrieves zero-day vulnerability reports from the global threat repositories and uses its local knowledge database to determine implications and mitigation strategies to alert and assist the SoC analyst. LocalIntel comprises two key phases: knowledge retrieval and contextualization. Quantitative and qualitative assessment has shown effectiveness in generating up to 93% accurate organizational threat intelligence with 64% inter-rater agreement.

cs.CR↗

arXiv2024

CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations

This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, Objective, Assessment, and Plan (SOAP) information from conversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs), which leverage the extracted SOAP data to generate abstracted clinical summaries. To fine-tune the PLM, we created a training dataset of consisting 1,473 conversations-summaries pair by consolidating two publicly available datasets, FigShare and MTS-Dialog, with ground truth summaries validated by Subject Matter Experts (SMEs). ClinicSum's effectiveness is evaluated through both automatic metrics (e.g., ROUGE, BERTScore) and expert human assessments. Results show that ClinicSum outperforms state-of-the-art PLMs, demonstrating superior precision, recall, and F-1 scores in automatic evaluations and receiving high preference from SMEs in human assessment, making it a robust solution for automated clinical summarization.

cs.CL↗

arXiv2024

IRSKG: Unified Intrusion Response System Knowledge Graph Ontology for Cyber Defense

Cyberattacks are becoming increasingly difficult to detect and prevent due to their sophistication. In response, Autonomous Intelligent Cyber-defense Agents (AICAs) are emerging as crucial solutions. One prominent AICA agent is the Intrusion Response System (IRS), which is critical for mitigating threats after detection. IRS uses several Tactics, Techniques, and Procedures (TTPs) to mitigate attacks and restore the infrastructure to normal operations. Continuous monitoring of the enterprise infrastructure is an essential TTP the IRS uses. However, each system serves different purposes to meet operational needs. Integrating these disparate sources for continuous monitoring increases pre-processing complexity and limits automation, eventually prolonging critical response time for attackers to exploit. We propose a unified IRS Knowledge Graph ontology (IRSKG) that streamlines the onboarding of new enterprise systems as a source for the AICAs. Our ontology can capture system monitoring logs and supplemental data, such as a rules repository containing the administrator-defined policies to dictate the IRS responses. Besides, our ontology permits us to incorporate dynamic changes to adapt to the evolving cyber-threat landscape. This robust yet concise design allows machine learning models to train effectively and recover a compromised system to its desired state autonomously with explainability.

cs.CR↗

arXiv2024

Multivariate Data Augmentation for Predictive Maintenance using Diffusion

Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to train these models, due to organizations working to keep fault occurrences and down time to a minimum. For newly installed systems, no fault data exists since they have yet to fail. By using diffusion models for synthetic data generation, the complex training datasets for these predictive models can be supplemented with high level synthetic fault data to improve their performance in anomaly detection. By learning the relationship between healthy and faulty data in similar systems, a diffusion model can attempt to apply that relationship to healthy data of a newly installed system that has no fault data. The diffusion model would then be able to generate useful fault data for the new system, and enable predictive models to be trained for predictive maintenance. The following paper demonstrates a system for generating useful, multivariate synthetic data for predictive maintenance, and how it can be applied to systems that have yet to fail.

cs.LG↗

arXiv2024

Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant

Recent research has shown that large language models (LLMs) are particularly vulnerable to adversarial attacks. Since the release of ChatGPT, various industries are adopting LLM-based chatbots and virtual assistants in their data workflows. The rapid development pace of AI-based systems is being driven by the potential of Generative AI (GenAI) to assist humans in decision making. The immense optimism behind GenAI often overshadows the adversarial risks associated with these technologies. A threat actor can use security gaps, poor safeguards, and limited data governance to carry out attacks that grant unauthorized access to the system and its data. As a proof-of-concept, we assess the performance of BarkPlug, the Mississippi State University chatbot, against data poison attacks from a red team perspective.

cs.CR↗